stevenbucaille/image-classification-models-dataset

model_id stringlengths 7 105	model_card stringlengths 1 130k	model_labels listlengths 2 80k
Taki3d/CrackDetectionLowRes	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CrackDetectionLowRes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9940 - Loss: 0.0183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:--------:\|:---------------:\| \| 0.0126 \| 1.0 \| 992 \| 0.9879 \| 0.0344 \| \| 0.0788 \| 2.0 \| 1904 \| 0.9933 \| 0.0220 \| \| 0.1336 \| 3.0 \| 2856 \| 0.9933 \| 0.0222 \| \| 0.0066 \| 4.0 \| 3808 \| 0.9933 \| 0.0190 \| \| 0.0528 \| 5.0 \| 4760 \| 0.9940 \| 0.0183 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3	[ "crack", "noncrack" ]
jordyvl/vit-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6948 - Accuracy: 0.85 - Brier Loss: 0.2427 - Nll: 1.2265 - F1 Micro: 0.85 - F1 Macro: 0.8401 - Ece: 0.1325 - Aurc: 0.0510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 6.1834 \| 0.09 \| 1.0218 \| 8.8168 \| 0.09 \| 0.0691 \| 0.3227 \| 0.8979 \| \| No log \| 2.0 \| 14 \| 5.0430 \| 0.14 \| 0.9049 \| 8.5503 \| 0.14 \| 0.1102 \| 0.2495 \| 0.7963 \| \| No log \| 3.0 \| 21 \| 4.6038 \| 0.26 \| 0.8385 \| 6.2625 \| 0.26 \| 0.1872 \| 0.2701 \| 0.5801 \| \| No log \| 4.0 \| 28 \| 4.3342 \| 0.475 \| 0.7283 \| 4.0652 \| 0.4750 \| 0.3906 \| 0.3309 \| 0.3230 \| \| No log \| 5.0 \| 35 \| 4.0844 \| 0.575 \| 0.6168 \| 2.7058 \| 0.575 \| 0.4691 \| 0.2963 \| 0.2299 \| \| No log \| 6.0 \| 42 \| 3.9551 \| 0.67 \| 0.5721 \| 2.2402 \| 0.67 \| 0.5531 \| 0.3412 \| 0.1586 \| \| No log \| 7.0 \| 49 \| 3.8114 \| 0.695 \| 0.4999 \| 1.8621 \| 0.695 \| 0.5789 \| 0.3028 \| 0.1342 \| \| No log \| 8.0 \| 56 \| 3.7627 \| 0.77 \| 0.4472 \| 1.6313 \| 0.7700 \| 0.7306 \| 0.3170 \| 0.0894 \| \| No log \| 9.0 \| 63 \| 3.7510 \| 0.75 \| 0.4223 \| 1.4350 \| 0.75 \| 0.6574 \| 0.2576 \| 0.1046 \| \| No log \| 10.0 \| 70 \| 3.6586 \| 0.8 \| 0.3576 \| 1.5473 \| 0.8000 \| 0.7615 \| 0.2754 \| 0.0701 \| \| No log \| 11.0 \| 77 \| 3.6643 \| 0.825 \| 0.3327 \| 1.4335 \| 0.825 \| 0.7921 \| 0.2555 \| 0.0670 \| \| No log \| 12.0 \| 84 \| 3.6561 \| 0.79 \| 0.3465 \| 1.3859 \| 0.79 \| 0.7541 \| 0.2165 \| 0.0833 \| \| No log \| 13.0 \| 91 \| 3.6518 \| 0.805 \| 0.3209 \| 1.0963 \| 0.805 \| 0.7716 \| 0.1894 \| 0.0706 \| \| No log \| 14.0 \| 98 \| 3.6401 \| 0.84 \| 0.3045 \| 1.1974 \| 0.8400 \| 0.8290 \| 0.2261 \| 0.0551 \| \| No log \| 15.0 \| 105 \| 3.6253 \| 0.79 \| 0.3200 \| 1.2187 \| 0.79 \| 0.7658 \| 0.1973 \| 0.0777 \| \| No log \| 16.0 \| 112 \| 3.6019 \| 0.83 \| 0.2920 \| 1.0547 \| 0.83 \| 0.8050 \| 0.1655 \| 0.0632 \| \| No log \| 17.0 \| 119 \| 3.5622 \| 0.84 \| 0.2741 \| 1.0769 \| 0.8400 \| 0.8228 \| 0.1641 \| 0.0636 \| \| No log \| 18.0 \| 126 \| 3.5921 \| 0.835 \| 0.2706 \| 1.0996 \| 0.835 \| 0.8205 \| 0.1810 \| 0.0581 \| \| No log \| 19.0 \| 133 \| 3.5886 \| 0.815 \| 0.2773 \| 1.2384 \| 0.815 \| 0.7975 \| 0.1772 \| 0.0643 \| \| No log \| 20.0 \| 140 \| 3.5798 \| 0.84 \| 0.2732 \| 1.4226 \| 0.8400 \| 0.8236 \| 0.1624 \| 0.0672 \| \| No log \| 21.0 \| 147 \| 3.5683 \| 0.82 \| 0.2868 \| 1.3978 \| 0.82 \| 0.7990 \| 0.1629 \| 0.0681 \| \| No log \| 22.0 \| 154 \| 3.5891 \| 0.825 \| 0.2893 \| 1.4084 \| 0.825 \| 0.8024 \| 0.1856 \| 0.0666 \| \| No log \| 23.0 \| 161 \| 3.5484 \| 0.82 \| 0.2595 \| 1.0782 \| 0.82 \| 0.7990 \| 0.1477 \| 0.0595 \| \| No log \| 24.0 \| 168 \| 3.5882 \| 0.8 \| 0.2686 \| 1.0513 \| 0.8000 \| 0.7745 \| 0.1334 \| 0.0596 \| \| No log \| 25.0 \| 175 \| 3.5636 \| 0.81 \| 0.2774 \| 1.1250 \| 0.81 \| 0.7838 \| 0.1563 \| 0.0662 \| \| No log \| 26.0 \| 182 \| 3.5478 \| 0.82 \| 0.2669 \| 1.0724 \| 0.82 \| 0.8021 \| 0.1318 \| 0.0576 \| \| No log \| 27.0 \| 189 \| 3.5092 \| 0.84 \| 0.2487 \| 1.0652 \| 0.8400 \| 0.8144 \| 0.1500 \| 0.0567 \| \| No log \| 28.0 \| 196 \| 3.5746 \| 0.815 \| 0.2810 \| 1.3942 \| 0.815 \| 0.7997 \| 0.1547 \| 0.0682 \| \| No log \| 29.0 \| 203 \| 3.5633 \| 0.835 \| 0.2587 \| 1.1580 \| 0.835 \| 0.8154 \| 0.1476 \| 0.0604 \| \| No log \| 30.0 \| 210 \| 3.5221 \| 0.835 \| 0.2591 \| 0.9385 \| 0.835 \| 0.8180 \| 0.1496 \| 0.0584 \| \| No log \| 31.0 \| 217 \| 3.6263 \| 0.83 \| 0.2676 \| 1.3260 \| 0.83 \| 0.8213 \| 0.1670 \| 0.0550 \| \| No log \| 32.0 \| 224 \| 3.5758 \| 0.825 \| 0.2855 \| 1.3100 \| 0.825 \| 0.8082 \| 0.1453 \| 0.0637 \| \| No log \| 33.0 \| 231 \| 3.5836 \| 0.84 \| 0.2550 \| 0.9703 \| 0.8400 \| 0.8268 \| 0.1344 \| 0.0521 \| \| No log \| 34.0 \| 238 \| 3.5466 \| 0.825 \| 0.2580 \| 1.3118 \| 0.825 \| 0.8071 \| 0.1553 \| 0.0604 \| \| No log \| 35.0 \| 245 \| 3.5566 \| 0.835 \| 0.2574 \| 1.1729 \| 0.835 \| 0.8134 \| 0.1357 \| 0.0592 \| \| No log \| 36.0 \| 252 \| 3.6022 \| 0.83 \| 0.2848 \| 1.3337 \| 0.83 \| 0.8158 \| 0.1528 \| 0.0631 \| \| No log \| 37.0 \| 259 \| 3.5422 \| 0.845 \| 0.2598 \| 1.2362 \| 0.845 \| 0.8311 \| 0.1488 \| 0.0588 \| \| No log \| 38.0 \| 266 \| 3.5677 \| 0.825 \| 0.2662 \| 1.2509 \| 0.825 \| 0.8026 \| 0.1417 \| 0.0566 \| \| No log \| 39.0 \| 273 \| 3.5600 \| 0.83 \| 0.2673 \| 1.2032 \| 0.83 \| 0.8128 \| 0.1344 \| 0.0561 \| \| No log \| 40.0 \| 280 \| 3.5818 \| 0.82 \| 0.2634 \| 1.1062 \| 0.82 \| 0.8070 \| 0.1389 \| 0.0558 \| \| No log \| 41.0 \| 287 \| 3.5326 \| 0.85 \| 0.2520 \| 1.3207 \| 0.85 \| 0.8404 \| 0.1485 \| 0.0529 \| \| No log \| 42.0 \| 294 \| 3.5954 \| 0.83 \| 0.2708 \| 1.1103 \| 0.83 \| 0.8092 \| 0.1401 \| 0.0595 \| \| No log \| 43.0 \| 301 \| 3.5330 \| 0.84 \| 0.2528 \| 1.2339 \| 0.8400 \| 0.8241 \| 0.1405 \| 0.0540 \| \| No log \| 44.0 \| 308 \| 3.5696 \| 0.825 \| 0.2654 \| 1.1943 \| 0.825 \| 0.8079 \| 0.1358 \| 0.0544 \| \| No log \| 45.0 \| 315 \| 3.5438 \| 0.83 \| 0.2558 \| 1.1267 \| 0.83 \| 0.8138 \| 0.1314 \| 0.0530 \| \| No log \| 46.0 \| 322 \| 3.5537 \| 0.845 \| 0.2497 \| 1.2612 \| 0.845 \| 0.8298 \| 0.1338 \| 0.0525 \| \| No log \| 47.0 \| 329 \| 3.5609 \| 0.85 \| 0.2467 \| 1.4284 \| 0.85 \| 0.8315 \| 0.1333 \| 0.0563 \| \| No log \| 48.0 \| 336 \| 3.5723 \| 0.835 \| 0.2595 \| 1.1814 \| 0.835 \| 0.8187 \| 0.1402 \| 0.0545 \| \| No log \| 49.0 \| 343 \| 3.5591 \| 0.825 \| 0.2485 \| 1.1736 \| 0.825 \| 0.8072 \| 0.1429 \| 0.0536 \| \| No log \| 50.0 \| 350 \| 3.5715 \| 0.825 \| 0.2585 \| 1.3645 \| 0.825 \| 0.8098 \| 0.1445 \| 0.0564 \| \| No log \| 51.0 \| 357 \| 3.5813 \| 0.83 \| 0.2617 \| 1.2375 \| 0.83 \| 0.8210 \| 0.1371 \| 0.0551 \| \| No log \| 52.0 \| 364 \| 3.6084 \| 0.835 \| 0.2592 \| 1.2465 \| 0.835 \| 0.8168 \| 0.1557 \| 0.0550 \| \| No log \| 53.0 \| 371 \| 3.5574 \| 0.84 \| 0.2474 \| 1.1932 \| 0.8400 \| 0.8255 \| 0.1351 \| 0.0543 \| \| No log \| 54.0 \| 378 \| 3.5863 \| 0.85 \| 0.2428 \| 1.2885 \| 0.85 \| 0.8346 \| 0.1347 \| 0.0536 \| \| No log \| 55.0 \| 385 \| 3.5510 \| 0.83 \| 0.2520 \| 1.2654 \| 0.83 \| 0.8163 \| 0.1342 \| 0.0543 \| \| No log \| 56.0 \| 392 \| 3.5516 \| 0.835 \| 0.2476 \| 1.0430 \| 0.835 \| 0.8210 \| 0.1336 \| 0.0549 \| \| No log \| 57.0 \| 399 \| 3.5754 \| 0.835 \| 0.2475 \| 1.3656 \| 0.835 \| 0.8245 \| 0.1165 \| 0.0528 \| \| No log \| 58.0 \| 406 \| 3.6017 \| 0.83 \| 0.2584 \| 1.3561 \| 0.83 \| 0.8198 \| 0.1490 \| 0.0542 \| \| No log \| 59.0 \| 413 \| 3.5767 \| 0.845 \| 0.2488 \| 1.2699 \| 0.845 \| 0.8357 \| 0.1291 \| 0.0521 \| \| No log \| 60.0 \| 420 \| 3.5844 \| 0.835 \| 0.2513 \| 1.2919 \| 0.835 \| 0.8218 \| 0.1286 \| 0.0541 \| \| No log \| 61.0 \| 427 \| 3.5744 \| 0.84 \| 0.2443 \| 1.2315 \| 0.8400 \| 0.8334 \| 0.1441 \| 0.0515 \| \| No log \| 62.0 \| 434 \| 3.5948 \| 0.825 \| 0.2505 \| 1.2265 \| 0.825 \| 0.8052 \| 0.1266 \| 0.0539 \| \| No log \| 63.0 \| 441 \| 3.5833 \| 0.845 \| 0.2403 \| 1.2410 \| 0.845 \| 0.8382 \| 0.1268 \| 0.0506 \| \| No log \| 64.0 \| 448 \| 3.6000 \| 0.845 \| 0.2451 \| 1.2889 \| 0.845 \| 0.8282 \| 0.1408 \| 0.0526 \| \| No log \| 65.0 \| 455 \| 3.6050 \| 0.84 \| 0.2497 \| 1.2870 \| 0.8400 \| 0.8298 \| 0.1401 \| 0.0534 \| \| No log \| 66.0 \| 462 \| 3.5950 \| 0.86 \| 0.2381 \| 1.3004 \| 0.8600 \| 0.8491 \| 0.1231 \| 0.0511 \| \| No log \| 67.0 \| 469 \| 3.6030 \| 0.85 \| 0.2435 \| 1.2246 \| 0.85 \| 0.8374 \| 0.1252 \| 0.0517 \| \| No log \| 68.0 \| 476 \| 3.6028 \| 0.85 \| 0.2433 \| 1.2260 \| 0.85 \| 0.8404 \| 0.1370 \| 0.0513 \| \| No log \| 69.0 \| 483 \| 3.6112 \| 0.86 \| 0.2438 \| 1.2148 \| 0.8600 \| 0.8487 \| 0.1362 \| 0.0522 \| \| No log \| 70.0 \| 490 \| 3.6147 \| 0.85 \| 0.2409 \| 1.2230 \| 0.85 \| 0.8401 \| 0.1258 \| 0.0511 \| \| No log \| 71.0 \| 497 \| 3.6231 \| 0.845 \| 0.2391 \| 1.2277 \| 0.845 \| 0.8341 \| 0.1332 \| 0.0506 \| \| 3.5909 \| 72.0 \| 504 \| 3.6298 \| 0.845 \| 0.2415 \| 1.2897 \| 0.845 \| 0.8346 \| 0.1288 \| 0.0508 \| \| 3.5909 \| 73.0 \| 511 \| 3.6384 \| 0.85 \| 0.2427 \| 1.2893 \| 0.85 \| 0.8401 \| 0.1366 \| 0.0515 \| \| 3.5909 \| 74.0 \| 518 \| 3.6364 \| 0.845 \| 0.2420 \| 1.2224 \| 0.845 \| 0.8346 \| 0.1219 \| 0.0511 \| \| 3.5909 \| 75.0 \| 525 \| 3.6471 \| 0.845 \| 0.2441 \| 1.2252 \| 0.845 \| 0.8346 \| 0.1322 \| 0.0517 \| \| 3.5909 \| 76.0 \| 532 \| 3.6469 \| 0.85 \| 0.2423 \| 1.2259 \| 0.85 \| 0.8404 \| 0.1300 \| 0.0513 \| \| 3.5909 \| 77.0 \| 539 \| 3.6493 \| 0.85 \| 0.2423 \| 1.2253 \| 0.85 \| 0.8401 \| 0.1248 \| 0.0514 \| \| 3.5909 \| 78.0 \| 546 \| 3.6534 \| 0.85 \| 0.2434 \| 1.2273 \| 0.85 \| 0.8404 \| 0.1271 \| 0.0512 \| \| 3.5909 \| 79.0 \| 553 \| 3.6588 \| 0.845 \| 0.2430 \| 1.2254 \| 0.845 \| 0.8346 \| 0.1307 \| 0.0513 \| \| 3.5909 \| 80.0 \| 560 \| 3.6636 \| 0.845 \| 0.2434 \| 1.2249 \| 0.845 \| 0.8346 \| 0.1259 \| 0.0513 \| \| 3.5909 \| 81.0 \| 567 \| 3.6670 \| 0.845 \| 0.2433 \| 1.2253 \| 0.845 \| 0.8346 \| 0.1356 \| 0.0513 \| \| 3.5909 \| 82.0 \| 574 \| 3.6689 \| 0.845 \| 0.2427 \| 1.2256 \| 0.845 \| 0.8346 \| 0.1365 \| 0.0511 \| \| 3.5909 \| 83.0 \| 581 \| 3.6724 \| 0.845 \| 0.2433 \| 1.2278 \| 0.845 \| 0.8346 \| 0.1315 \| 0.0510 \| \| 3.5909 \| 84.0 \| 588 \| 3.6768 \| 0.85 \| 0.2431 \| 1.2260 \| 0.85 \| 0.8401 \| 0.1374 \| 0.0510 \| \| 3.5909 \| 85.0 \| 595 \| 3.6782 \| 0.85 \| 0.2424 \| 1.2265 \| 0.85 \| 0.8401 \| 0.1340 \| 0.0509 \| \| 3.5909 \| 86.0 \| 602 \| 3.6817 \| 0.85 \| 0.2428 \| 1.2261 \| 0.85 \| 0.8401 \| 0.1332 \| 0.0510 \| \| 3.5909 \| 87.0 \| 609 \| 3.6822 \| 0.85 \| 0.2427 \| 1.2266 \| 0.85 \| 0.8401 \| 0.1330 \| 0.0508 \| \| 3.5909 \| 88.0 \| 616 \| 3.6835 \| 0.85 \| 0.2425 \| 1.2259 \| 0.85 \| 0.8401 \| 0.1328 \| 0.0510 \| \| 3.5909 \| 89.0 \| 623 \| 3.6854 \| 0.85 \| 0.2425 \| 1.2260 \| 0.85 \| 0.8401 \| 0.1328 \| 0.0509 \| \| 3.5909 \| 90.0 \| 630 \| 3.6874 \| 0.85 \| 0.2426 \| 1.2259 \| 0.85 \| 0.8401 \| 0.1327 \| 0.0510 \| \| 3.5909 \| 91.0 \| 637 \| 3.6891 \| 0.85 \| 0.2428 \| 1.2264 \| 0.85 \| 0.8401 \| 0.1327 \| 0.0510 \| \| 3.5909 \| 92.0 \| 644 \| 3.6903 \| 0.85 \| 0.2426 \| 1.2265 \| 0.85 \| 0.8401 \| 0.1328 \| 0.0509 \| \| 3.5909 \| 93.0 \| 651 \| 3.6913 \| 0.85 \| 0.2427 \| 1.2264 \| 0.85 \| 0.8401 \| 0.1327 \| 0.0509 \| \| 3.5909 \| 94.0 \| 658 \| 3.6922 \| 0.85 \| 0.2427 \| 1.2265 \| 0.85 \| 0.8401 \| 0.1326 \| 0.0509 \| \| 3.5909 \| 95.0 \| 665 \| 3.6930 \| 0.85 \| 0.2426 \| 1.2262 \| 0.85 \| 0.8401 \| 0.1326 \| 0.0510 \| \| 3.5909 \| 96.0 \| 672 \| 3.6936 \| 0.85 \| 0.2427 \| 1.2266 \| 0.85 \| 0.8401 \| 0.1327 \| 0.0509 \| \| 3.5909 \| 97.0 \| 679 \| 3.6940 \| 0.85 \| 0.2426 \| 1.2264 \| 0.85 \| 0.8401 \| 0.1325 \| 0.0510 \| \| 3.5909 \| 98.0 \| 686 \| 3.6946 \| 0.85 \| 0.2427 \| 1.2265 \| 0.85 \| 0.8401 \| 0.1326 \| 0.0510 \| \| 3.5909 \| 99.0 \| 693 \| 3.6948 \| 0.85 \| 0.2427 \| 1.2266 \| 0.85 \| 0.8401 \| 0.1325 \| 0.0510 \| \| 3.5909 \| 100.0 \| 700 \| 3.6948 \| 0.85 \| 0.2427 \| 1.2265 \| 0.85 \| 0.8401 \| 0.1325 \| 0.0510 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH-40-30-30	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH-40-30-30 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0418 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3299 \| 1.0 \| 187 \| 0.2118 \| 0.9218 \| \| 0.5922 \| 2.0 \| 374 \| 0.3206 \| 0.8629 \| \| 0.1763 \| 3.0 \| 561 \| 0.2447 \| 0.9127 \| \| 0.1351 \| 4.0 \| 749 \| 0.1028 \| 0.9564 \| \| 0.142 \| 4.99 \| 935 \| 0.0418 \| 0.9869 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "lung-benign_tissue", "lung_adenocarcinoma", "lung_squamous_cell_carcinoma" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0733 - Accuracy: 0.4825 - Brier Loss: 0.7791 - Nll: 2.6387 - F1 Micro: 0.4825 - F1 Macro: 0.4847 - Ece: 0.3427 - Aurc: 0.2765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.0917 \| 0.0675 \| 0.9375 \| 7.4169 \| 0.0675 \| 0.0426 \| 0.1087 \| 0.9278 \| \| No log \| 2.0 \| 50 \| 0.0830 \| 0.07 \| 0.9373 \| 7.3255 \| 0.07 \| 0.0326 \| 0.1058 \| 0.9149 \| \| No log \| 3.0 \| 75 \| 0.0823 \| 0.08 \| 0.9370 \| 7.0476 \| 0.08 \| 0.0333 \| 0.1129 \| 0.9007 \| \| No log \| 4.0 \| 100 \| 0.0820 \| 0.0825 \| 0.9368 \| 6.9259 \| 0.0825 \| 0.0333 \| 0.1113 \| 0.8914 \| \| No log \| 5.0 \| 125 \| 0.0817 \| 0.095 \| 0.9366 \| 7.1920 \| 0.095 \| 0.0593 \| 0.1189 \| 0.8845 \| \| No log \| 6.0 \| 150 \| 0.0814 \| 0.105 \| 0.9363 \| 7.6541 \| 0.1050 \| 0.0654 \| 0.1364 \| 0.8354 \| \| No log \| 7.0 \| 175 \| 0.0810 \| 0.1075 \| 0.9361 \| 7.5199 \| 0.1075 \| 0.0628 \| 0.1235 \| 0.8559 \| \| No log \| 8.0 \| 200 \| 0.0806 \| 0.1025 \| 0.9357 \| 7.3552 \| 0.1025 \| 0.0532 \| 0.1230 \| 0.8697 \| \| No log \| 9.0 \| 225 \| 0.0801 \| 0.1125 \| 0.9353 \| 6.2436 \| 0.1125 \| 0.0580 \| 0.1291 \| 0.8258 \| \| No log \| 10.0 \| 250 \| 0.0797 \| 0.0975 \| 0.9342 \| 6.1811 \| 0.0975 \| 0.0486 \| 0.1217 \| 0.8531 \| \| No log \| 11.0 \| 275 \| 0.0792 \| 0.11 \| 0.9331 \| 5.1954 \| 0.11 \| 0.0558 \| 0.1330 \| 0.8172 \| \| No log \| 12.0 \| 300 \| 0.0789 \| 0.1225 \| 0.9310 \| 5.0567 \| 0.1225 \| 0.0536 \| 0.1428 \| 0.7847 \| \| No log \| 13.0 \| 325 \| 0.0785 \| 0.14 \| 0.9283 \| 4.2411 \| 0.14 \| 0.1085 \| 0.1561 \| 0.7098 \| \| No log \| 14.0 \| 350 \| 0.0780 \| 0.1925 \| 0.9234 \| 3.9402 \| 0.1925 \| 0.1627 \| 0.1956 \| 0.6553 \| \| No log \| 15.0 \| 375 \| 0.0780 \| 0.2275 \| 0.9186 \| 4.2282 \| 0.2275 \| 0.1806 \| 0.2151 \| 0.5919 \| \| No log \| 16.0 \| 400 \| 0.0770 \| 0.2925 \| 0.9082 \| 3.5789 \| 0.2925 \| 0.2357 \| 0.2602 \| 0.5043 \| \| No log \| 17.0 \| 425 \| 0.0766 \| 0.305 \| 0.8993 \| 3.6388 \| 0.305 \| 0.2465 \| 0.2603 \| 0.4771 \| \| No log \| 18.0 \| 450 \| 0.0762 \| 0.31 \| 0.8916 \| 3.2067 \| 0.31 \| 0.2602 \| 0.2755 \| 0.4341 \| \| No log \| 19.0 \| 475 \| 0.0758 \| 0.315 \| 0.8861 \| 3.1537 \| 0.315 \| 0.2659 \| 0.2820 \| 0.4282 \| \| 0.0818 \| 20.0 \| 500 \| 0.0755 \| 0.3475 \| 0.8713 \| 3.3614 \| 0.3475 \| 0.2869 \| 0.2830 \| 0.3966 \| \| 0.0818 \| 21.0 \| 525 \| 0.0755 \| 0.34 \| 0.8627 \| 3.3538 \| 0.34 \| 0.2728 \| 0.2781 \| 0.3934 \| \| 0.0818 \| 22.0 \| 550 \| 0.0752 \| 0.3575 \| 0.8578 \| 3.4181 \| 0.3575 \| 0.3052 \| 0.2867 \| 0.4037 \| \| 0.0818 \| 23.0 \| 575 \| 0.0745 \| 0.365 \| 0.8486 \| 2.7931 \| 0.3650 \| 0.3297 \| 0.2908 \| 0.3669 \| \| 0.0818 \| 24.0 \| 600 \| 0.0743 \| 0.395 \| 0.8392 \| 2.8800 \| 0.395 \| 0.3419 \| 0.3054 \| 0.3602 \| \| 0.0818 \| 25.0 \| 625 \| 0.0741 \| 0.3975 \| 0.8382 \| 2.8294 \| 0.3975 \| 0.3584 \| 0.3049 \| 0.3469 \| \| 0.0818 \| 26.0 \| 650 \| 0.0739 \| 0.4125 \| 0.8308 \| 2.9306 \| 0.4125 \| 0.3650 \| 0.3179 \| 0.3342 \| \| 0.0818 \| 27.0 \| 675 \| 0.0740 \| 0.425 \| 0.8237 \| 3.0954 \| 0.425 \| 0.3831 \| 0.3069 \| 0.3356 \| \| 0.0818 \| 28.0 \| 700 \| 0.0739 \| 0.425 \| 0.8325 \| 3.0230 \| 0.425 \| 0.3933 \| 0.3154 \| 0.3316 \| \| 0.0818 \| 29.0 \| 725 \| 0.0735 \| 0.445 \| 0.8150 \| 2.9001 \| 0.445 \| 0.4078 \| 0.3320 \| 0.3125 \| \| 0.0818 \| 30.0 \| 750 \| 0.0734 \| 0.44 \| 0.8127 \| 2.8272 \| 0.44 \| 0.4048 \| 0.3196 \| 0.3145 \| \| 0.0818 \| 31.0 \| 775 \| 0.0733 \| 0.45 \| 0.8105 \| 2.9716 \| 0.45 \| 0.4224 \| 0.3214 \| 0.3126 \| \| 0.0818 \| 32.0 \| 800 \| 0.0732 \| 0.4475 \| 0.8059 \| 2.7234 \| 0.4475 \| 0.4211 \| 0.3166 \| 0.3098 \| \| 0.0818 \| 33.0 \| 825 \| 0.0734 \| 0.45 \| 0.8091 \| 2.8963 \| 0.45 \| 0.4298 \| 0.3174 \| 0.3144 \| \| 0.0818 \| 34.0 \| 850 \| 0.0732 \| 0.45 \| 0.8021 \| 2.7268 \| 0.45 \| 0.4216 \| 0.3203 \| 0.3024 \| \| 0.0818 \| 35.0 \| 875 \| 0.0732 \| 0.465 \| 0.8013 \| 2.9374 \| 0.465 \| 0.4379 \| 0.3417 \| 0.2959 \| \| 0.0818 \| 36.0 \| 900 \| 0.0732 \| 0.4575 \| 0.8039 \| 2.9305 \| 0.4575 \| 0.4360 \| 0.3166 \| 0.3029 \| \| 0.0818 \| 37.0 \| 925 \| 0.0733 \| 0.4725 \| 0.8017 \| 2.7705 \| 0.4725 \| 0.4542 \| 0.3348 \| 0.2859 \| \| 0.0818 \| 38.0 \| 950 \| 0.0732 \| 0.4725 \| 0.7963 \| 2.8600 \| 0.4725 \| 0.4559 \| 0.3432 \| 0.2826 \| \| 0.0818 \| 39.0 \| 975 \| 0.0731 \| 0.4825 \| 0.7979 \| 2.7795 \| 0.4825 \| 0.4675 \| 0.3361 \| 0.2930 \| \| 0.0698 \| 40.0 \| 1000 \| 0.0732 \| 0.445 \| 0.7962 \| 2.8308 \| 0.445 \| 0.4366 \| 0.3056 \| 0.3058 \| \| 0.0698 \| 41.0 \| 1025 \| 0.0732 \| 0.4675 \| 0.7914 \| 2.7809 \| 0.4675 \| 0.4582 \| 0.3173 \| 0.2904 \| \| 0.0698 \| 42.0 \| 1050 \| 0.0731 \| 0.4625 \| 0.7952 \| 2.8907 \| 0.4625 \| 0.4644 \| 0.3175 \| 0.2910 \| \| 0.0698 \| 43.0 \| 1075 \| 0.0733 \| 0.4625 \| 0.7955 \| 2.7470 \| 0.4625 \| 0.4545 \| 0.3107 \| 0.2930 \| \| 0.0698 \| 44.0 \| 1100 \| 0.0731 \| 0.4725 \| 0.7894 \| 2.8684 \| 0.4725 \| 0.4640 \| 0.3281 \| 0.2883 \| \| 0.0698 \| 45.0 \| 1125 \| 0.0731 \| 0.475 \| 0.7912 \| 2.9091 \| 0.4750 \| 0.4594 \| 0.3302 \| 0.2830 \| \| 0.0698 \| 46.0 \| 1150 \| 0.0731 \| 0.47 \| 0.7911 \| 2.7282 \| 0.47 \| 0.4705 \| 0.3344 \| 0.2865 \| \| 0.0698 \| 47.0 \| 1175 \| 0.0732 \| 0.4775 \| 0.7886 \| 2.8402 \| 0.4775 \| 0.4737 \| 0.3151 \| 0.2846 \| \| 0.0698 \| 48.0 \| 1200 \| 0.0731 \| 0.4825 \| 0.7850 \| 2.7818 \| 0.4825 \| 0.4833 \| 0.3422 \| 0.2807 \| \| 0.0698 \| 49.0 \| 1225 \| 0.0731 \| 0.4625 \| 0.7863 \| 2.7929 \| 0.4625 \| 0.4621 \| 0.3205 \| 0.2828 \| \| 0.0698 \| 50.0 \| 1250 \| 0.0732 \| 0.4725 \| 0.7875 \| 2.8382 \| 0.4725 \| 0.4686 \| 0.3364 \| 0.2831 \| \| 0.0698 \| 51.0 \| 1275 \| 0.0731 \| 0.4725 \| 0.7861 \| 2.7543 \| 0.4725 \| 0.4661 \| 0.3229 \| 0.2838 \| \| 0.0698 \| 52.0 \| 1300 \| 0.0731 \| 0.475 \| 0.7863 \| 2.7936 \| 0.4750 \| 0.4771 \| 0.3285 \| 0.2801 \| \| 0.0698 \| 53.0 \| 1325 \| 0.0731 \| 0.4825 \| 0.7846 \| 2.8369 \| 0.4825 \| 0.4843 \| 0.3369 \| 0.2747 \| \| 0.0698 \| 54.0 \| 1350 \| 0.0731 \| 0.4725 \| 0.7852 \| 2.8102 \| 0.4725 \| 0.4747 \| 0.3175 \| 0.2869 \| \| 0.0698 \| 55.0 \| 1375 \| 0.0731 \| 0.475 \| 0.7855 \| 2.8205 \| 0.4750 \| 0.4801 \| 0.3409 \| 0.2802 \| \| 0.0698 \| 56.0 \| 1400 \| 0.0731 \| 0.48 \| 0.7855 \| 2.7926 \| 0.48 \| 0.4815 \| 0.3403 \| 0.2827 \| \| 0.0698 \| 57.0 \| 1425 \| 0.0731 \| 0.4825 \| 0.7826 \| 2.7536 \| 0.4825 \| 0.4815 \| 0.3381 \| 0.2788 \| \| 0.0698 \| 58.0 \| 1450 \| 0.0731 \| 0.4875 \| 0.7851 \| 2.8313 \| 0.4875 \| 0.4901 \| 0.3395 \| 0.2719 \| \| 0.0698 \| 59.0 \| 1475 \| 0.0731 \| 0.4875 \| 0.7838 \| 2.7423 \| 0.4875 \| 0.4905 \| 0.3410 \| 0.2735 \| \| 0.0654 \| 60.0 \| 1500 \| 0.0731 \| 0.48 \| 0.7849 \| 2.7730 \| 0.48 \| 0.4818 \| 0.3344 \| 0.2807 \| \| 0.0654 \| 61.0 \| 1525 \| 0.0732 \| 0.48 \| 0.7816 \| 2.7517 \| 0.48 \| 0.4813 \| 0.3370 \| 0.2762 \| \| 0.0654 \| 62.0 \| 1550 \| 0.0731 \| 0.4775 \| 0.7833 \| 2.8441 \| 0.4775 \| 0.4804 \| 0.3314 \| 0.2767 \| \| 0.0654 \| 63.0 \| 1575 \| 0.0731 \| 0.4775 \| 0.7835 \| 2.7252 \| 0.4775 \| 0.4811 \| 0.3354 \| 0.2769 \| \| 0.0654 \| 64.0 \| 1600 \| 0.0732 \| 0.4925 \| 0.7819 \| 2.7991 \| 0.4925 \| 0.4958 \| 0.3371 \| 0.2726 \| \| 0.0654 \| 65.0 \| 1625 \| 0.0731 \| 0.4825 \| 0.7806 \| 2.6719 \| 0.4825 \| 0.4850 \| 0.3190 \| 0.2752 \| \| 0.0654 \| 66.0 \| 1650 \| 0.0732 \| 0.48 \| 0.7817 \| 2.7669 \| 0.48 \| 0.4828 \| 0.3336 \| 0.2791 \| \| 0.0654 \| 67.0 \| 1675 \| 0.0731 \| 0.4775 \| 0.7813 \| 2.6678 \| 0.4775 \| 0.4822 \| 0.3304 \| 0.2750 \| \| 0.0654 \| 68.0 \| 1700 \| 0.0732 \| 0.4875 \| 0.7829 \| 2.7529 \| 0.4875 \| 0.4919 \| 0.3381 \| 0.2756 \| \| 0.0654 \| 69.0 \| 1725 \| 0.0731 \| 0.4825 \| 0.7795 \| 2.7291 \| 0.4825 \| 0.4839 \| 0.3418 \| 0.2737 \| \| 0.0654 \| 70.0 \| 1750 \| 0.0732 \| 0.4875 \| 0.7827 \| 2.7613 \| 0.4875 \| 0.4909 \| 0.3308 \| 0.2747 \| \| 0.0654 \| 71.0 \| 1775 \| 0.0732 \| 0.4825 \| 0.7816 \| 2.7348 \| 0.4825 \| 0.4863 \| 0.3306 \| 0.2733 \| \| 0.0654 \| 72.0 \| 1800 \| 0.0732 \| 0.4825 \| 0.7813 \| 2.6920 \| 0.4825 \| 0.4863 \| 0.3268 \| 0.2724 \| \| 0.0654 \| 73.0 \| 1825 \| 0.0731 \| 0.485 \| 0.7809 \| 2.6890 \| 0.485 \| 0.4872 \| 0.3307 \| 0.2741 \| \| 0.0654 \| 74.0 \| 1850 \| 0.0732 \| 0.4825 \| 0.7810 \| 2.6668 \| 0.4825 \| 0.4854 \| 0.3245 \| 0.2758 \| \| 0.0654 \| 75.0 \| 1875 \| 0.0732 \| 0.48 \| 0.7814 \| 2.7337 \| 0.48 \| 0.4836 \| 0.3232 \| 0.2767 \| \| 0.0654 \| 76.0 \| 1900 \| 0.0731 \| 0.49 \| 0.7802 \| 2.7219 \| 0.49 \| 0.4900 \| 0.3290 \| 0.2727 \| \| 0.0654 \| 77.0 \| 1925 \| 0.0732 \| 0.48 \| 0.7804 \| 2.7187 \| 0.48 \| 0.4821 \| 0.3223 \| 0.2759 \| \| 0.0654 \| 78.0 \| 1950 \| 0.0732 \| 0.485 \| 0.7811 \| 2.6797 \| 0.485 \| 0.4884 \| 0.3343 \| 0.2754 \| \| 0.0654 \| 79.0 \| 1975 \| 0.0731 \| 0.48 \| 0.7784 \| 2.6604 \| 0.48 \| 0.4816 \| 0.3345 \| 0.2751 \| \| 0.0641 \| 80.0 \| 2000 \| 0.0732 \| 0.485 \| 0.7797 \| 2.6380 \| 0.485 \| 0.4876 \| 0.3317 \| 0.2755 \| \| 0.0641 \| 81.0 \| 2025 \| 0.0732 \| 0.4775 \| 0.7805 \| 2.6934 \| 0.4775 \| 0.4808 \| 0.3225 \| 0.2758 \| \| 0.0641 \| 82.0 \| 2050 \| 0.0732 \| 0.4825 \| 0.7802 \| 2.7315 \| 0.4825 \| 0.4851 \| 0.3364 \| 0.2781 \| \| 0.0641 \| 83.0 \| 2075 \| 0.0732 \| 0.4875 \| 0.7800 \| 2.7011 \| 0.4875 \| 0.4899 \| 0.3222 \| 0.2736 \| \| 0.0641 \| 84.0 \| 2100 \| 0.0732 \| 0.4825 \| 0.7796 \| 2.6672 \| 0.4825 \| 0.4845 \| 0.3203 \| 0.2772 \| \| 0.0641 \| 85.0 \| 2125 \| 0.0732 \| 0.4825 \| 0.7798 \| 2.6956 \| 0.4825 \| 0.4833 \| 0.3373 \| 0.2757 \| \| 0.0641 \| 86.0 \| 2150 \| 0.0732 \| 0.48 \| 0.7797 \| 2.6349 \| 0.48 \| 0.4823 \| 0.3265 \| 0.2774 \| \| 0.0641 \| 87.0 \| 2175 \| 0.0732 \| 0.49 \| 0.7800 \| 2.7238 \| 0.49 \| 0.4921 \| 0.3407 \| 0.2755 \| \| 0.0641 \| 88.0 \| 2200 \| 0.0732 \| 0.4775 \| 0.7800 \| 2.6423 \| 0.4775 \| 0.4804 \| 0.3163 \| 0.2785 \| \| 0.0641 \| 89.0 \| 2225 \| 0.0732 \| 0.485 \| 0.7793 \| 2.6734 \| 0.485 \| 0.4881 \| 0.3310 \| 0.2760 \| \| 0.0641 \| 90.0 \| 2250 \| 0.0732 \| 0.4825 \| 0.7796 \| 2.6582 \| 0.4825 \| 0.4858 \| 0.3232 \| 0.2774 \| \| 0.0641 \| 91.0 \| 2275 \| 0.0732 \| 0.485 \| 0.7790 \| 2.6705 \| 0.485 \| 0.4882 \| 0.3277 \| 0.2760 \| \| 0.0641 \| 92.0 \| 2300 \| 0.0732 \| 0.49 \| 0.7795 \| 2.6465 \| 0.49 \| 0.4943 \| 0.3513 \| 0.2767 \| \| 0.0641 \| 93.0 \| 2325 \| 0.0732 \| 0.4825 \| 0.7791 \| 2.6495 \| 0.4825 \| 0.4852 \| 0.3414 \| 0.2763 \| \| 0.0641 \| 94.0 \| 2350 \| 0.0732 \| 0.49 \| 0.7793 \| 2.6402 \| 0.49 \| 0.4933 \| 0.3458 \| 0.2760 \| \| 0.0641 \| 95.0 \| 2375 \| 0.0732 \| 0.4875 \| 0.7792 \| 2.6448 \| 0.4875 \| 0.4898 \| 0.3420 \| 0.2763 \| \| 0.0641 \| 96.0 \| 2400 \| 0.0732 \| 0.4825 \| 0.7792 \| 2.6402 \| 0.4825 \| 0.4847 \| 0.3346 \| 0.2766 \| \| 0.0641 \| 97.0 \| 2425 \| 0.0733 \| 0.485 \| 0.7793 \| 2.6397 \| 0.485 \| 0.4873 \| 0.3407 \| 0.2768 \| \| 0.0641 \| 98.0 \| 2450 \| 0.0732 \| 0.4825 \| 0.7790 \| 2.6388 \| 0.4825 \| 0.4847 \| 0.3374 \| 0.2763 \| \| 0.0641 \| 99.0 \| 2475 \| 0.0733 \| 0.4825 \| 0.7792 \| 2.6390 \| 0.4825 \| 0.4847 \| 0.3393 \| 0.2767 \| \| 0.0637 \| 100.0 \| 2500 \| 0.0733 \| 0.4825 \| 0.7791 \| 2.6387 \| 0.4825 \| 0.4847 \| 0.3427 \| 0.2765 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-small_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2014 - Accuracy: 0.635 - Brier Loss: 0.5252 - Nll: 2.1069 - F1 Micro: 0.635 - F1 Macro: 0.6363 - Ece: 0.1836 - Aurc: 0.1520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 7.0792 \| 0.0825 \| 0.9657 \| 10.6330 \| 0.0825 \| 0.0736 \| 0.1618 \| 0.9054 \| \| No log \| 2.0 \| 14 \| 6.4691 \| 0.07 \| 0.9514 \| 8.1662 \| 0.07 \| 0.0643 \| 0.1779 \| 0.9461 \| \| No log \| 3.0 \| 21 \| 5.8986 \| 0.2975 \| 0.8596 \| 5.3260 \| 0.2975 \| 0.2944 \| 0.2211 \| 0.5304 \| \| No log \| 4.0 \| 28 \| 5.5468 \| 0.3925 \| 0.7531 \| 3.5791 \| 0.3925 \| 0.3860 \| 0.2145 \| 0.3645 \| \| No log \| 5.0 \| 35 \| 5.2678 \| 0.46 \| 0.6755 \| 3.2144 \| 0.46 \| 0.4517 \| 0.1901 \| 0.2901 \| \| No log \| 6.0 \| 42 \| 5.1237 \| 0.5075 \| 0.6334 \| 2.9369 \| 0.5075 \| 0.4888 \| 0.1985 \| 0.2550 \| \| No log \| 7.0 \| 49 \| 5.1530 \| 0.5125 \| 0.6131 \| 3.1196 \| 0.5125 \| 0.4843 \| 0.1634 \| 0.2417 \| \| No log \| 8.0 \| 56 \| 5.0462 \| 0.545 \| 0.5898 \| 2.7596 \| 0.545 \| 0.5376 \| 0.1792 \| 0.2232 \| \| No log \| 9.0 \| 63 \| 5.1437 \| 0.565 \| 0.5759 \| 2.9426 \| 0.565 \| 0.5660 \| 0.1715 \| 0.2208 \| \| No log \| 10.0 \| 70 \| 4.9658 \| 0.605 \| 0.5382 \| 2.4096 \| 0.605 \| 0.5945 \| 0.1828 \| 0.1734 \| \| No log \| 11.0 \| 77 \| 5.1189 \| 0.57 \| 0.5592 \| 2.5892 \| 0.57 \| 0.5677 \| 0.1381 \| 0.1952 \| \| No log \| 12.0 \| 84 \| 5.2082 \| 0.54 \| 0.5774 \| 2.7250 \| 0.54 \| 0.5323 \| 0.1578 \| 0.2144 \| \| No log \| 13.0 \| 91 \| 4.9674 \| 0.5775 \| 0.5365 \| 2.5469 \| 0.5775 \| 0.5654 \| 0.1603 \| 0.1824 \| \| No log \| 14.0 \| 98 \| 5.0007 \| 0.5875 \| 0.5299 \| 2.6635 \| 0.5875 \| 0.5778 \| 0.1567 \| 0.1701 \| \| No log \| 15.0 \| 105 \| 4.9925 \| 0.585 \| 0.5417 \| 2.6416 \| 0.585 \| 0.5760 \| 0.1731 \| 0.1896 \| \| No log \| 16.0 \| 112 \| 4.8314 \| 0.6425 \| 0.4939 \| 2.4601 \| 0.6425 \| 0.6444 \| 0.1492 \| 0.1506 \| \| No log \| 17.0 \| 119 \| 4.8729 \| 0.6075 \| 0.5197 \| 2.4297 \| 0.6075 \| 0.6054 \| 0.1511 \| 0.1702 \| \| No log \| 18.0 \| 126 \| 4.8960 \| 0.61 \| 0.5085 \| 2.2405 \| 0.61 \| 0.6197 \| 0.1664 \| 0.1657 \| \| No log \| 19.0 \| 133 \| 4.8227 \| 0.62 \| 0.5032 \| 2.4320 \| 0.62 \| 0.6177 \| 0.1399 \| 0.1615 \| \| No log \| 20.0 \| 140 \| 4.9420 \| 0.61 \| 0.5160 \| 2.3051 \| 0.61 \| 0.6119 \| 0.1460 \| 0.1722 \| \| No log \| 21.0 \| 147 \| 4.8779 \| 0.6125 \| 0.5132 \| 2.3564 \| 0.6125 \| 0.6080 \| 0.1549 \| 0.1639 \| \| No log \| 22.0 \| 154 \| 4.9454 \| 0.6125 \| 0.5261 \| 2.4064 \| 0.6125 \| 0.6155 \| 0.1792 \| 0.1733 \| \| No log \| 23.0 \| 161 \| 4.8659 \| 0.5925 \| 0.5018 \| 2.5961 \| 0.5925 \| 0.5897 \| 0.1537 \| 0.1607 \| \| No log \| 24.0 \| 168 \| 4.8150 \| 0.605 \| 0.4996 \| 2.2624 \| 0.605 \| 0.6050 \| 0.1525 \| 0.1588 \| \| No log \| 25.0 \| 175 \| 4.8303 \| 0.6175 \| 0.4970 \| 2.1999 \| 0.6175 \| 0.6204 \| 0.1284 \| 0.1515 \| \| No log \| 26.0 \| 182 \| 4.8442 \| 0.6225 \| 0.5060 \| 2.2842 \| 0.6225 \| 0.6251 \| 0.1639 \| 0.1614 \| \| No log \| 27.0 \| 189 \| 4.8260 \| 0.63 \| 0.4953 \| 2.2666 \| 0.63 \| 0.6345 \| 0.1638 \| 0.1531 \| \| No log \| 28.0 \| 196 \| 4.8421 \| 0.6375 \| 0.4979 \| 2.3173 \| 0.6375 \| 0.6344 \| 0.1430 \| 0.1525 \| \| No log \| 29.0 \| 203 \| 4.9011 \| 0.62 \| 0.5066 \| 2.2663 \| 0.62 \| 0.6221 \| 0.1596 \| 0.1602 \| \| No log \| 30.0 \| 210 \| 4.8689 \| 0.62 \| 0.4994 \| 2.1498 \| 0.62 \| 0.6260 \| 0.1581 \| 0.1567 \| \| No log \| 31.0 \| 217 \| 4.8681 \| 0.6075 \| 0.5143 \| 2.0979 \| 0.6075 \| 0.6080 \| 0.1673 \| 0.1641 \| \| No log \| 32.0 \| 224 \| 4.8489 \| 0.6 \| 0.5074 \| 2.1485 \| 0.6 \| 0.5913 \| 0.1579 \| 0.1613 \| \| No log \| 33.0 \| 231 \| 4.8669 \| 0.63 \| 0.5037 \| 2.3142 \| 0.63 \| 0.6272 \| 0.1512 \| 0.1519 \| \| No log \| 34.0 \| 238 \| 4.8382 \| 0.6075 \| 0.5005 \| 2.1817 \| 0.6075 \| 0.6038 \| 0.1683 \| 0.1552 \| \| No log \| 35.0 \| 245 \| 4.8406 \| 0.61 \| 0.5012 \| 2.2132 \| 0.61 \| 0.6019 \| 0.1443 \| 0.1518 \| \| No log \| 36.0 \| 252 \| 4.8241 \| 0.6275 \| 0.5040 \| 2.2466 \| 0.6275 \| 0.6182 \| 0.1511 \| 0.1563 \| \| No log \| 37.0 \| 259 \| 4.8359 \| 0.6225 \| 0.4993 \| 2.1727 \| 0.6225 \| 0.6201 \| 0.1665 \| 0.1570 \| \| No log \| 38.0 \| 266 \| 4.8812 \| 0.6025 \| 0.5155 \| 2.2712 \| 0.6025 \| 0.5990 \| 0.1634 \| 0.1649 \| \| No log \| 39.0 \| 273 \| 4.8672 \| 0.61 \| 0.5075 \| 2.1626 \| 0.61 \| 0.6073 \| 0.1603 \| 0.1592 \| \| No log \| 40.0 \| 280 \| 4.9083 \| 0.6175 \| 0.5098 \| 2.1507 \| 0.6175 \| 0.6204 \| 0.1524 \| 0.1594 \| \| No log \| 41.0 \| 287 \| 4.8942 \| 0.61 \| 0.5132 \| 2.2443 \| 0.61 \| 0.6070 \| 0.1574 \| 0.1618 \| \| No log \| 42.0 \| 294 \| 4.9435 \| 0.62 \| 0.5177 \| 2.1770 \| 0.62 \| 0.6186 \| 0.1567 \| 0.1664 \| \| No log \| 43.0 \| 301 \| 4.8836 \| 0.63 \| 0.5089 \| 2.1922 \| 0.63 \| 0.6300 \| 0.1612 \| 0.1553 \| \| No log \| 44.0 \| 308 \| 4.9806 \| 0.6225 \| 0.5205 \| 2.1855 \| 0.6225 \| 0.6213 \| 0.1715 \| 0.1631 \| \| No log \| 45.0 \| 315 \| 4.9314 \| 0.6225 \| 0.5185 \| 2.1783 \| 0.6225 \| 0.6182 \| 0.1743 \| 0.1631 \| \| No log \| 46.0 \| 322 \| 4.8615 \| 0.6275 \| 0.4984 \| 2.2407 \| 0.6275 \| 0.6259 \| 0.1529 \| 0.1497 \| \| No log \| 47.0 \| 329 \| 4.8550 \| 0.625 \| 0.4985 \| 2.1229 \| 0.625 \| 0.6261 \| 0.1517 \| 0.1531 \| \| No log \| 48.0 \| 336 \| 4.9218 \| 0.6125 \| 0.5113 \| 2.2200 \| 0.6125 \| 0.6114 \| 0.1627 \| 0.1588 \| \| No log \| 49.0 \| 343 \| 4.9067 \| 0.63 \| 0.5102 \| 2.2177 \| 0.63 \| 0.6299 \| 0.1534 \| 0.1567 \| \| No log \| 50.0 \| 350 \| 4.9040 \| 0.6125 \| 0.5110 \| 2.1105 \| 0.6125 \| 0.6136 \| 0.1731 \| 0.1559 \| \| No log \| 51.0 \| 357 \| 4.9557 \| 0.615 \| 0.5180 \| 2.2031 \| 0.615 \| 0.6157 \| 0.1726 \| 0.1602 \| \| No log \| 52.0 \| 364 \| 4.9409 \| 0.61 \| 0.5195 \| 2.2616 \| 0.61 \| 0.6079 \| 0.1627 \| 0.1618 \| \| No log \| 53.0 \| 371 \| 4.9290 \| 0.6225 \| 0.5125 \| 2.1352 \| 0.6225 \| 0.6227 \| 0.1873 \| 0.1549 \| \| No log \| 54.0 \| 378 \| 4.9297 \| 0.6225 \| 0.5075 \| 2.1558 \| 0.6225 \| 0.6216 \| 0.1724 \| 0.1530 \| \| No log \| 55.0 \| 385 \| 4.9192 \| 0.6225 \| 0.5131 \| 2.1572 \| 0.6225 \| 0.6220 \| 0.1655 \| 0.1578 \| \| No log \| 56.0 \| 392 \| 4.9760 \| 0.61 \| 0.5203 \| 2.1227 \| 0.61 \| 0.6092 \| 0.1852 \| 0.1594 \| \| No log \| 57.0 \| 399 \| 4.9860 \| 0.6125 \| 0.5208 \| 2.1996 \| 0.6125 \| 0.6154 \| 0.1812 \| 0.1608 \| \| No log \| 58.0 \| 406 \| 4.9418 \| 0.62 \| 0.5176 \| 2.1034 \| 0.62 \| 0.6220 \| 0.1635 \| 0.1549 \| \| No log \| 59.0 \| 413 \| 4.9462 \| 0.62 \| 0.5143 \| 2.1095 \| 0.62 \| 0.6221 \| 0.1855 \| 0.1553 \| \| No log \| 60.0 \| 420 \| 4.9447 \| 0.6175 \| 0.5142 \| 2.0731 \| 0.6175 \| 0.6180 \| 0.1571 \| 0.1533 \| \| No log \| 61.0 \| 427 \| 4.9677 \| 0.63 \| 0.5091 \| 2.1491 \| 0.63 \| 0.6346 \| 0.1693 \| 0.1498 \| \| No log \| 62.0 \| 434 \| 4.9567 \| 0.62 \| 0.5089 \| 2.1222 \| 0.62 \| 0.6242 \| 0.1609 \| 0.1546 \| \| No log \| 63.0 \| 441 \| 4.9378 \| 0.6325 \| 0.5030 \| 2.1787 \| 0.6325 \| 0.6310 \| 0.1558 \| 0.1471 \| \| No log \| 64.0 \| 448 \| 4.9764 \| 0.6175 \| 0.5154 \| 2.0751 \| 0.6175 \| 0.6192 \| 0.1835 \| 0.1549 \| \| No log \| 65.0 \| 455 \| 4.9520 \| 0.6325 \| 0.5069 \| 2.1067 \| 0.6325 \| 0.6352 \| 0.1670 \| 0.1499 \| \| No log \| 66.0 \| 462 \| 4.9649 \| 0.6375 \| 0.5109 \| 2.1016 \| 0.6375 \| 0.6361 \| 0.1665 \| 0.1506 \| \| No log \| 67.0 \| 469 \| 5.0023 \| 0.635 \| 0.5174 \| 2.2166 \| 0.635 \| 0.6350 \| 0.1653 \| 0.1543 \| \| No log \| 68.0 \| 476 \| 5.0084 \| 0.63 \| 0.5187 \| 2.1238 \| 0.63 \| 0.6302 \| 0.1674 \| 0.1535 \| \| No log \| 69.0 \| 483 \| 4.9875 \| 0.6325 \| 0.5096 \| 2.1744 \| 0.6325 \| 0.6345 \| 0.1822 \| 0.1510 \| \| No log \| 70.0 \| 490 \| 5.0129 \| 0.6325 \| 0.5151 \| 2.1042 \| 0.6325 \| 0.6335 \| 0.1691 \| 0.1535 \| \| No log \| 71.0 \| 497 \| 5.0389 \| 0.6275 \| 0.5201 \| 2.0941 \| 0.6275 \| 0.6283 \| 0.1765 \| 0.1550 \| \| 3.4121 \| 72.0 \| 504 \| 5.0288 \| 0.6325 \| 0.5168 \| 2.1299 \| 0.6325 \| 0.6314 \| 0.1802 \| 0.1529 \| \| 3.4121 \| 73.0 \| 511 \| 5.0181 \| 0.625 \| 0.5121 \| 2.1690 \| 0.625 \| 0.6236 \| 0.1683 \| 0.1511 \| \| 3.4121 \| 74.0 \| 518 \| 5.0422 \| 0.625 \| 0.5139 \| 2.1323 \| 0.625 \| 0.6264 \| 0.1873 \| 0.1517 \| \| 3.4121 \| 75.0 \| 525 \| 5.0557 \| 0.6325 \| 0.5177 \| 2.1695 \| 0.6325 \| 0.6342 \| 0.1677 \| 0.1503 \| \| 3.4121 \| 76.0 \| 532 \| 5.0440 \| 0.6375 \| 0.5113 \| 2.1384 \| 0.6375 \| 0.6384 \| 0.1714 \| 0.1489 \| \| 3.4121 \| 77.0 \| 539 \| 5.0710 \| 0.6375 \| 0.5163 \| 2.1017 \| 0.6375 \| 0.6397 \| 0.1785 \| 0.1508 \| \| 3.4121 \| 78.0 \| 546 \| 5.1024 \| 0.63 \| 0.5218 \| 2.0905 \| 0.63 \| 0.6280 \| 0.1724 \| 0.1538 \| \| 3.4121 \| 79.0 \| 553 \| 5.0906 \| 0.635 \| 0.5186 \| 2.1293 \| 0.635 \| 0.6358 \| 0.1908 \| 0.1509 \| \| 3.4121 \| 80.0 \| 560 \| 5.1027 \| 0.63 \| 0.5206 \| 2.1292 \| 0.63 \| 0.6299 \| 0.1850 \| 0.1525 \| \| 3.4121 \| 81.0 \| 567 \| 5.1063 \| 0.64 \| 0.5161 \| 2.1620 \| 0.64 \| 0.6404 \| 0.1754 \| 0.1489 \| \| 3.4121 \| 82.0 \| 574 \| 5.1267 \| 0.64 \| 0.5207 \| 2.1291 \| 0.64 \| 0.6400 \| 0.1849 \| 0.1516 \| \| 3.4121 \| 83.0 \| 581 \| 5.1332 \| 0.63 \| 0.5224 \| 2.1338 \| 0.63 \| 0.6322 \| 0.1750 \| 0.1522 \| \| 3.4121 \| 84.0 \| 588 \| 5.1408 \| 0.6325 \| 0.5233 \| 2.1333 \| 0.6325 \| 0.6334 \| 0.1797 \| 0.1522 \| \| 3.4121 \| 85.0 \| 595 \| 5.1510 \| 0.63 \| 0.5224 \| 2.1635 \| 0.63 \| 0.6301 \| 0.1755 \| 0.1522 \| \| 3.4121 \| 86.0 \| 602 \| 5.1536 \| 0.6375 \| 0.5215 \| 2.1628 \| 0.6375 \| 0.6382 \| 0.1683 \| 0.1511 \| \| 3.4121 \| 87.0 \| 609 \| 5.1580 \| 0.6325 \| 0.5228 \| 2.1348 \| 0.6325 \| 0.6328 \| 0.1779 \| 0.1523 \| \| 3.4121 \| 88.0 \| 616 \| 5.1701 \| 0.64 \| 0.5235 \| 2.1352 \| 0.64 \| 0.6417 \| 0.1818 \| 0.1515 \| \| 3.4121 \| 89.0 \| 623 \| 5.1734 \| 0.6375 \| 0.5235 \| 2.1354 \| 0.6375 \| 0.6385 \| 0.1775 \| 0.1515 \| \| 3.4121 \| 90.0 \| 630 \| 5.1779 \| 0.635 \| 0.5243 \| 2.1334 \| 0.635 \| 0.6360 \| 0.1842 \| 0.1519 \| \| 3.4121 \| 91.0 \| 637 \| 5.1834 \| 0.635 \| 0.5241 \| 2.1344 \| 0.635 \| 0.6363 \| 0.1813 \| 0.1521 \| \| 3.4121 \| 92.0 \| 644 \| 5.1877 \| 0.6375 \| 0.5247 \| 2.1356 \| 0.6375 \| 0.6385 \| 0.1871 \| 0.1517 \| \| 3.4121 \| 93.0 \| 651 \| 5.1906 \| 0.635 \| 0.5245 \| 2.1389 \| 0.635 \| 0.6360 \| 0.1888 \| 0.1520 \| \| 3.4121 \| 94.0 \| 658 \| 5.1935 \| 0.635 \| 0.5248 \| 2.1083 \| 0.635 \| 0.6363 \| 0.1831 \| 0.1521 \| \| 3.4121 \| 95.0 \| 665 \| 5.1955 \| 0.635 \| 0.5249 \| 2.1098 \| 0.635 \| 0.6363 \| 0.1795 \| 0.1521 \| \| 3.4121 \| 96.0 \| 672 \| 5.1978 \| 0.635 \| 0.5250 \| 2.1079 \| 0.635 \| 0.6363 \| 0.1820 \| 0.1521 \| \| 3.4121 \| 97.0 \| 679 \| 5.1995 \| 0.635 \| 0.5251 \| 2.1073 \| 0.635 \| 0.6363 \| 0.1834 \| 0.1521 \| \| 3.4121 \| 98.0 \| 686 \| 5.2004 \| 0.635 \| 0.5251 \| 2.1072 \| 0.635 \| 0.6360 \| 0.1834 \| 0.1520 \| \| 3.4121 \| 99.0 \| 693 \| 5.2012 \| 0.635 \| 0.5252 \| 2.1071 \| 0.635 \| 0.6360 \| 0.1836 \| 0.1520 \| \| 3.4121 \| 100.0 \| 700 \| 5.2014 \| 0.635 \| 0.5252 \| 2.1069 \| 0.635 \| 0.6363 \| 0.1836 \| 0.1520 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Trial006-007-008-YEL_STEM1 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0457 - Accuracy: 0.9912 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7163 \| 0.89 \| 4 \| 0.7036 \| 0.5175 \| \| 0.7183 \| 2.0 \| 9 \| 0.6982 \| 0.5439 \| \| 0.6561 \| 2.89 \| 13 \| 0.5914 \| 0.6667 \| \| 0.5799 \| 4.0 \| 18 \| 0.6411 \| 0.5526 \| \| 0.5731 \| 4.89 \| 22 \| 0.4833 \| 0.8158 \| \| 0.5162 \| 6.0 \| 27 \| 0.4483 \| 0.7807 \| \| 0.4738 \| 6.89 \| 31 \| 0.3961 \| 0.8596 \| \| 0.3283 \| 8.0 \| 36 \| 0.2897 \| 0.8772 \| \| 0.3365 \| 8.89 \| 40 \| 0.2774 \| 0.8860 \| \| 0.5987 \| 10.0 \| 45 \| 0.2617 \| 0.8947 \| \| 0.3633 \| 10.89 \| 49 \| 0.2661 \| 0.8772 \| \| 0.3342 \| 12.0 \| 54 \| 0.2269 \| 0.9211 \| \| 0.3406 \| 12.89 \| 58 \| 0.1903 \| 0.9211 \| \| 0.2331 \| 14.0 \| 63 \| 0.2075 \| 0.9123 \| \| 0.2494 \| 14.89 \| 67 \| 0.1345 \| 0.9474 \| \| 0.3143 \| 16.0 \| 72 \| 0.1508 \| 0.9298 \| \| 0.2622 \| 16.89 \| 76 \| 0.1267 \| 0.9298 \| \| 0.2894 \| 18.0 \| 81 \| 0.0921 \| 0.9737 \| \| 0.2105 \| 18.89 \| 85 \| 0.1058 \| 0.9298 \| \| 0.2011 \| 20.0 \| 90 \| 0.1021 \| 0.9561 \| \| 0.1977 \| 20.89 \| 94 \| 0.1122 \| 0.9561 \| \| 0.2685 \| 22.0 \| 99 \| 0.1656 \| 0.9298 \| \| 0.2394 \| 22.89 \| 103 \| 0.1197 \| 0.9561 \| \| 0.2394 \| 24.0 \| 108 \| 0.1461 \| 0.9211 \| \| 0.1523 \| 24.89 \| 112 \| 0.0969 \| 0.9649 \| \| 0.2231 \| 26.0 \| 117 \| 0.0827 \| 0.9649 \| \| 0.1887 \| 26.89 \| 121 \| 0.0979 \| 0.9561 \| \| 0.1656 \| 28.0 \| 126 \| 0.0907 \| 0.9386 \| \| 0.1824 \| 28.89 \| 130 \| 0.1009 \| 0.9561 \| \| 0.2443 \| 30.0 \| 135 \| 0.1249 \| 0.9474 \| \| 0.2082 \| 30.89 \| 139 \| 0.0755 \| 0.9649 \| \| 0.1276 \| 32.0 \| 144 \| 0.0531 \| 0.9825 \| \| 0.1857 \| 32.89 \| 148 \| 0.0707 \| 0.9561 \| \| 0.1676 \| 34.0 \| 153 \| 0.0570 \| 0.9825 \| \| 0.1992 \| 34.89 \| 157 \| 0.0591 \| 0.9737 \| \| 0.2283 \| 36.0 \| 162 \| 0.0575 \| 0.9737 \| \| 0.2125 \| 36.89 \| 166 \| 0.0519 \| 0.9825 \| \| 0.1915 \| 38.0 \| 171 \| 0.0457 \| 0.9912 \| \| 0.1969 \| 38.89 \| 175 \| 0.0644 \| 0.9561 \| \| 0.1522 \| 40.0 \| 180 \| 0.0771 \| 0.9561 \| \| 0.1037 \| 40.89 \| 184 \| 0.0557 \| 0.9561 \| \| 0.2348 \| 42.0 \| 189 \| 0.0470 \| 0.9737 \| \| 0.1674 \| 42.89 \| 193 \| 0.0469 \| 0.9737 \| \| 0.0874 \| 44.0 \| 198 \| 0.0483 \| 0.9737 \| \| 0.1605 \| 44.44 \| 200 \| 0.0485 \| 0.9649 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1	[ "plant2", "plant3" ]
sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Trial006-007-008-YEL_STEM2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0188 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7014 \| 0.94 \| 4 \| 0.6789 \| 0.5872 \| \| 0.6242 \| 1.88 \| 8 \| 0.6182 \| 0.6147 \| \| 0.5903 \| 2.82 \| 12 \| 0.5147 \| 0.8165 \| \| 0.516 \| 4.0 \| 17 \| 0.4137 \| 0.8440 \| \| 0.4612 \| 4.94 \| 21 \| 0.3127 \| 0.8899 \| \| 0.3615 \| 5.88 \| 25 \| 0.2448 \| 0.8899 \| \| 0.2899 \| 6.82 \| 29 \| 0.2502 \| 0.9083 \| \| 0.3271 \| 8.0 \| 34 \| 0.2268 \| 0.8899 \| \| 0.3765 \| 8.94 \| 38 \| 0.1419 \| 0.9266 \| \| 0.2954 \| 9.88 \| 42 \| 0.1233 \| 0.9450 \| \| 0.2187 \| 10.82 \| 46 \| 0.1541 \| 0.9358 \| \| 0.3391 \| 12.0 \| 51 \| 0.1575 \| 0.9266 \| \| 0.2027 \| 12.94 \| 55 \| 0.0838 \| 0.9908 \| \| 0.2894 \| 13.88 \| 59 \| 0.0852 \| 0.9633 \| \| 0.1972 \| 14.82 \| 63 \| 0.0648 \| 0.9908 \| \| 0.1533 \| 16.0 \| 68 \| 0.0884 \| 0.9908 \| \| 0.1588 \| 16.94 \| 72 \| 0.0951 \| 0.9633 \| \| 0.1746 \| 17.88 \| 76 \| 0.0793 \| 0.9725 \| \| 0.2386 \| 18.82 \| 80 \| 0.1204 \| 0.9633 \| \| 0.1226 \| 20.0 \| 85 \| 0.0502 \| 0.9633 \| \| 0.1901 \| 20.94 \| 89 \| 0.0188 \| 1.0 \| \| 0.1305 \| 21.88 \| 93 \| 0.0244 \| 0.9908 \| \| 0.1695 \| 22.82 \| 97 \| 0.0186 \| 1.0 \| \| 0.2682 \| 24.0 \| 102 \| 0.0460 \| 0.9817 \| \| 0.1956 \| 24.94 \| 106 \| 0.0328 \| 0.9817 \| \| 0.1651 \| 25.88 \| 110 \| 0.0493 \| 0.9817 \| \| 0.1841 \| 26.82 \| 114 \| 0.0168 \| 1.0 \| \| 0.1897 \| 28.0 \| 119 \| 0.0139 \| 1.0 \| \| 0.2088 \| 28.94 \| 123 \| 0.0195 \| 1.0 \| \| 0.1386 \| 29.88 \| 127 \| 0.0483 \| 0.9817 \| \| 0.1544 \| 30.82 \| 131 \| 0.0258 \| 0.9817 \| \| 0.1293 \| 32.0 \| 136 \| 0.0461 \| 0.9725 \| \| 0.1696 \| 32.94 \| 140 \| 0.0207 \| 0.9908 \| \| 0.1793 \| 33.88 \| 144 \| 0.0182 \| 1.0 \| \| 0.1545 \| 34.82 \| 148 \| 0.0361 \| 0.9817 \| \| 0.141 \| 36.0 \| 153 \| 0.0455 \| 0.9817 \| \| 0.138 \| 36.94 \| 157 \| 0.0175 \| 0.9908 \| \| 0.1382 \| 37.88 \| 161 \| 0.0359 \| 0.9817 \| \| 0.163 \| 38.82 \| 165 \| 0.0463 \| 0.9817 \| \| 0.1374 \| 40.0 \| 170 \| 0.0153 \| 0.9908 \| \| 0.1993 \| 40.94 \| 174 \| 0.0116 \| 1.0 \| \| 0.1471 \| 41.88 \| 178 \| 0.0199 \| 0.9817 \| \| 0.1414 \| 42.82 \| 182 \| 0.0330 \| 0.9817 \| \| 0.1494 \| 44.0 \| 187 \| 0.0228 \| 0.9817 \| \| 0.1367 \| 44.94 \| 191 \| 0.0174 \| 0.9817 \| \| 0.1529 \| 45.88 \| 195 \| 0.0162 \| 0.9817 \| \| 0.1365 \| 46.82 \| 199 \| 0.0153 \| 0.9908 \| \| 0.1301 \| 47.06 \| 200 \| 0.0152 \| 0.9908 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1	[ "plant2", "plant3" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4262 - Accuracy: 0.535 - Brier Loss: 0.6080 - Nll: 2.4569 - F1 Micro: 0.535 - F1 Macro: 0.5345 - Ece: 0.2120 - Aurc: 0.2105 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 8.7328 \| 0.0375 \| 1.0712 \| 7.1959 \| 0.0375 \| 0.0284 \| 0.2770 \| 0.9564 \| \| No log \| 2.0 \| 14 \| 6.6754 \| 0.0725 \| 0.9650 \| 6.2033 \| 0.0725 \| 0.0442 \| 0.1890 \| 0.9239 \| \| No log \| 3.0 \| 21 \| 6.2399 \| 0.1525 \| 0.9213 \| 5.6927 \| 0.1525 \| 0.1339 \| 0.1647 \| 0.8105 \| \| No log \| 4.0 \| 28 \| 5.9557 \| 0.2125 \| 0.8771 \| 4.2773 \| 0.2125 \| 0.1952 \| 0.1637 \| 0.6347 \| \| No log \| 5.0 \| 35 \| 5.5957 \| 0.3275 \| 0.7962 \| 3.1599 \| 0.3275 \| 0.3087 \| 0.1928 \| 0.4595 \| \| No log \| 6.0 \| 42 \| 5.4573 \| 0.4075 \| 0.7283 \| 3.0860 \| 0.4075 \| 0.3732 \| 0.1803 \| 0.3610 \| \| No log \| 7.0 \| 49 \| 5.3445 \| 0.43 \| 0.7150 \| 2.9840 \| 0.4300 \| 0.4112 \| 0.2071 \| 0.3498 \| \| No log \| 8.0 \| 56 \| 5.2986 \| 0.445 \| 0.6860 \| 2.9281 \| 0.445 \| 0.4327 \| 0.1891 \| 0.3159 \| \| No log \| 9.0 \| 63 \| 5.2567 \| 0.465 \| 0.6608 \| 3.0783 \| 0.465 \| 0.4494 \| 0.1835 \| 0.2885 \| \| No log \| 10.0 \| 70 \| 5.4317 \| 0.4425 \| 0.6752 \| 2.9067 \| 0.4425 \| 0.4477 \| 0.1836 \| 0.3209 \| \| No log \| 11.0 \| 77 \| 5.2073 \| 0.4825 \| 0.6342 \| 2.7960 \| 0.4825 \| 0.4664 \| 0.1731 \| 0.2708 \| \| No log \| 12.0 \| 84 \| 5.2094 \| 0.51 \| 0.6150 \| 2.8886 \| 0.51 \| 0.4767 \| 0.1463 \| 0.2459 \| \| No log \| 13.0 \| 91 \| 5.2379 \| 0.4975 \| 0.6135 \| 2.7446 \| 0.4975 \| 0.4819 \| 0.1702 \| 0.2436 \| \| No log \| 14.0 \| 98 \| 5.2716 \| 0.505 \| 0.6123 \| 2.7888 \| 0.505 \| 0.4962 \| 0.1595 \| 0.2496 \| \| No log \| 15.0 \| 105 \| 5.2410 \| 0.5125 \| 0.6090 \| 2.6474 \| 0.5125 \| 0.5144 \| 0.1659 \| 0.2451 \| \| No log \| 16.0 \| 112 \| 5.2764 \| 0.54 \| 0.6063 \| 2.7700 \| 0.54 \| 0.5316 \| 0.1634 \| 0.2383 \| \| No log \| 17.0 \| 119 \| 5.2652 \| 0.5275 \| 0.6027 \| 2.7934 \| 0.5275 \| 0.5171 \| 0.1800 \| 0.2326 \| \| No log \| 18.0 \| 126 \| 5.2145 \| 0.54 \| 0.5944 \| 2.6456 \| 0.54 \| 0.5350 \| 0.1548 \| 0.2323 \| \| No log \| 19.0 \| 133 \| 5.2611 \| 0.5175 \| 0.6096 \| 2.5302 \| 0.5175 \| 0.5273 \| 0.1709 \| 0.2435 \| \| No log \| 20.0 \| 140 \| 5.3536 \| 0.52 \| 0.6110 \| 2.6530 \| 0.52 \| 0.5229 \| 0.1619 \| 0.2359 \| \| No log \| 21.0 \| 147 \| 5.3020 \| 0.5125 \| 0.6060 \| 2.7184 \| 0.5125 \| 0.5070 \| 0.1724 \| 0.2398 \| \| No log \| 22.0 \| 154 \| 5.2107 \| 0.5275 \| 0.5926 \| 2.4436 \| 0.5275 \| 0.5242 \| 0.1618 \| 0.2255 \| \| No log \| 23.0 \| 161 \| 5.2723 \| 0.53 \| 0.5953 \| 2.7008 \| 0.53 \| 0.5209 \| 0.1698 \| 0.2253 \| \| No log \| 24.0 \| 168 \| 5.1615 \| 0.5325 \| 0.5875 \| 2.4753 \| 0.5325 \| 0.5254 \| 0.1699 \| 0.2247 \| \| No log \| 25.0 \| 175 \| 5.1795 \| 0.5375 \| 0.5825 \| 2.6856 \| 0.5375 \| 0.5316 \| 0.1781 \| 0.2144 \| \| No log \| 26.0 \| 182 \| 5.2340 \| 0.54 \| 0.5937 \| 2.6542 \| 0.54 \| 0.5271 \| 0.1778 \| 0.2215 \| \| No log \| 27.0 \| 189 \| 5.2197 \| 0.5375 \| 0.5831 \| 2.4800 \| 0.5375 \| 0.5366 \| 0.1666 \| 0.2119 \| \| No log \| 28.0 \| 196 \| 5.2345 \| 0.5275 \| 0.6105 \| 2.6475 \| 0.5275 \| 0.5247 \| 0.1919 \| 0.2338 \| \| No log \| 29.0 \| 203 \| 5.2050 \| 0.5475 \| 0.5917 \| 2.6350 \| 0.5475 \| 0.5531 \| 0.1753 \| 0.2251 \| \| No log \| 30.0 \| 210 \| 5.1753 \| 0.5425 \| 0.5891 \| 2.6472 \| 0.5425 \| 0.5282 \| 0.1831 \| 0.2215 \| \| No log \| 31.0 \| 217 \| 5.2349 \| 0.535 \| 0.5946 \| 2.5653 \| 0.535 \| 0.5257 \| 0.1617 \| 0.2186 \| \| No log \| 32.0 \| 224 \| 5.1497 \| 0.545 \| 0.5778 \| 2.6174 \| 0.545 \| 0.5425 \| 0.1716 \| 0.2138 \| \| No log \| 33.0 \| 231 \| 5.1688 \| 0.5175 \| 0.5899 \| 2.5079 \| 0.5175 \| 0.5149 \| 0.1624 \| 0.2159 \| \| No log \| 34.0 \| 238 \| 5.2269 \| 0.53 \| 0.5961 \| 2.5188 \| 0.53 \| 0.5326 \| 0.1746 \| 0.2206 \| \| No log \| 35.0 \| 245 \| 5.1477 \| 0.5325 \| 0.5867 \| 2.4762 \| 0.5325 \| 0.5369 \| 0.1728 \| 0.2176 \| \| No log \| 36.0 \| 252 \| 5.2229 \| 0.5375 \| 0.5838 \| 2.4397 \| 0.5375 \| 0.5386 \| 0.1693 \| 0.2167 \| \| No log \| 37.0 \| 259 \| 5.1578 \| 0.535 \| 0.5802 \| 2.5103 \| 0.535 \| 0.5286 \| 0.1755 \| 0.2124 \| \| No log \| 38.0 \| 266 \| 5.1405 \| 0.535 \| 0.5979 \| 2.5852 \| 0.535 \| 0.5346 \| 0.1913 \| 0.2268 \| \| No log \| 39.0 \| 273 \| 5.1236 \| 0.535 \| 0.5844 \| 2.4851 \| 0.535 \| 0.5378 \| 0.1729 \| 0.2168 \| \| No log \| 40.0 \| 280 \| 5.0813 \| 0.5475 \| 0.5757 \| 2.4305 \| 0.5475 \| 0.5434 \| 0.1781 \| 0.2091 \| \| No log \| 41.0 \| 287 \| 5.1844 \| 0.535 \| 0.5888 \| 2.4730 \| 0.535 \| 0.5306 \| 0.1707 \| 0.2159 \| \| No log \| 42.0 \| 294 \| 5.1468 \| 0.53 \| 0.5926 \| 2.4866 \| 0.53 \| 0.5316 \| 0.1776 \| 0.2200 \| \| No log \| 43.0 \| 301 \| 5.1469 \| 0.53 \| 0.5837 \| 2.5769 \| 0.53 \| 0.5252 \| 0.1805 \| 0.2168 \| \| No log \| 44.0 \| 308 \| 5.2168 \| 0.54 \| 0.5955 \| 2.5216 \| 0.54 \| 0.5419 \| 0.1689 \| 0.2226 \| \| No log \| 45.0 \| 315 \| 5.1395 \| 0.525 \| 0.5861 \| 2.4328 \| 0.525 \| 0.5293 \| 0.2006 \| 0.2180 \| \| No log \| 46.0 \| 322 \| 5.1163 \| 0.5425 \| 0.5822 \| 2.4635 \| 0.5425 \| 0.5416 \| 0.1937 \| 0.2106 \| \| No log \| 47.0 \| 329 \| 5.1227 \| 0.5475 \| 0.5786 \| 2.5198 \| 0.5475 \| 0.5489 \| 0.1580 \| 0.2111 \| \| No log \| 48.0 \| 336 \| 5.1134 \| 0.5375 \| 0.5839 \| 2.5239 \| 0.5375 \| 0.5318 \| 0.1832 \| 0.2071 \| \| No log \| 49.0 \| 343 \| 5.1907 \| 0.5375 \| 0.5913 \| 2.5012 \| 0.5375 \| 0.5334 \| 0.1853 \| 0.2145 \| \| No log \| 50.0 \| 350 \| 5.1364 \| 0.5375 \| 0.5875 \| 2.4105 \| 0.5375 \| 0.5415 \| 0.1857 \| 0.2121 \| \| No log \| 51.0 \| 357 \| 5.1739 \| 0.5425 \| 0.5905 \| 2.5208 \| 0.5425 \| 0.5399 \| 0.1894 \| 0.2112 \| \| No log \| 52.0 \| 364 \| 5.1635 \| 0.5325 \| 0.5841 \| 2.4658 \| 0.5325 \| 0.5300 \| 0.1924 \| 0.2124 \| \| No log \| 53.0 \| 371 \| 5.2055 \| 0.5425 \| 0.5866 \| 2.4800 \| 0.5425 \| 0.5390 \| 0.1983 \| 0.2135 \| \| No log \| 54.0 \| 378 \| 5.1547 \| 0.5375 \| 0.5869 \| 2.4575 \| 0.5375 \| 0.5340 \| 0.1839 \| 0.2117 \| \| No log \| 55.0 \| 385 \| 5.1437 \| 0.535 \| 0.5838 \| 2.4117 \| 0.535 \| 0.5366 \| 0.1914 \| 0.2110 \| \| No log \| 56.0 \| 392 \| 5.2042 \| 0.5425 \| 0.5915 \| 2.4286 \| 0.5425 \| 0.5445 \| 0.1905 \| 0.2124 \| \| No log \| 57.0 \| 399 \| 5.2084 \| 0.5625 \| 0.5909 \| 2.4774 \| 0.5625 \| 0.5646 \| 0.2006 \| 0.2116 \| \| No log \| 58.0 \| 406 \| 5.1844 \| 0.545 \| 0.5895 \| 2.3826 \| 0.545 \| 0.5466 \| 0.1948 \| 0.2102 \| \| No log \| 59.0 \| 413 \| 5.1759 \| 0.545 \| 0.5892 \| 2.4790 \| 0.545 \| 0.5498 \| 0.1730 \| 0.2143 \| \| No log \| 60.0 \| 420 \| 5.1783 \| 0.5475 \| 0.5894 \| 2.4294 \| 0.5475 \| 0.5452 \| 0.2043 \| 0.2087 \| \| No log \| 61.0 \| 427 \| 5.1874 \| 0.545 \| 0.5879 \| 2.4295 \| 0.545 \| 0.5412 \| 0.1959 \| 0.2080 \| \| No log \| 62.0 \| 434 \| 5.1861 \| 0.5475 \| 0.5840 \| 2.4513 \| 0.5475 \| 0.5430 \| 0.2097 \| 0.2107 \| \| No log \| 63.0 \| 441 \| 5.1608 \| 0.545 \| 0.5818 \| 2.4581 \| 0.545 \| 0.5450 \| 0.1666 \| 0.2055 \| \| No log \| 64.0 \| 448 \| 5.2018 \| 0.5475 \| 0.5911 \| 2.4537 \| 0.5475 \| 0.5448 \| 0.1938 \| 0.2113 \| \| No log \| 65.0 \| 455 \| 5.2113 \| 0.5375 \| 0.5953 \| 2.4444 \| 0.5375 \| 0.5360 \| 0.1757 \| 0.2106 \| \| No log \| 66.0 \| 462 \| 5.1985 \| 0.5425 \| 0.5897 \| 2.4287 \| 0.5425 \| 0.5377 \| 0.1870 \| 0.2095 \| \| No log \| 67.0 \| 469 \| 5.2218 \| 0.5325 \| 0.5856 \| 2.4340 \| 0.5325 \| 0.5320 \| 0.1882 \| 0.2059 \| \| No log \| 68.0 \| 476 \| 5.2243 \| 0.545 \| 0.5931 \| 2.3923 \| 0.545 \| 0.5447 \| 0.1799 \| 0.2120 \| \| No log \| 69.0 \| 483 \| 5.2103 \| 0.55 \| 0.5881 \| 2.4619 \| 0.55 \| 0.5486 \| 0.2084 \| 0.2060 \| \| No log \| 70.0 \| 490 \| 5.2370 \| 0.55 \| 0.5933 \| 2.4236 \| 0.55 \| 0.5521 \| 0.1920 \| 0.2108 \| \| No log \| 71.0 \| 497 \| 5.2185 \| 0.5475 \| 0.5890 \| 2.4137 \| 0.5475 \| 0.5435 \| 0.2121 \| 0.2076 \| \| 3.6002 \| 72.0 \| 504 \| 5.2460 \| 0.545 \| 0.5944 \| 2.4704 \| 0.545 \| 0.5430 \| 0.1922 \| 0.2117 \| \| 3.6002 \| 73.0 \| 511 \| 5.2454 \| 0.5425 \| 0.5928 \| 2.4750 \| 0.5425 \| 0.5406 \| 0.1940 \| 0.2080 \| \| 3.6002 \| 74.0 \| 518 \| 5.2307 \| 0.5575 \| 0.5935 \| 2.4623 \| 0.5575 \| 0.5599 \| 0.1959 \| 0.2071 \| \| 3.6002 \| 75.0 \| 525 \| 5.2674 \| 0.56 \| 0.5877 \| 2.4453 \| 0.56 \| 0.5587 \| 0.1956 \| 0.2033 \| \| 3.6002 \| 76.0 \| 532 \| 5.2263 \| 0.5525 \| 0.5907 \| 2.5044 \| 0.5525 \| 0.5526 \| 0.1862 \| 0.2067 \| \| 3.6002 \| 77.0 \| 539 \| 5.2498 \| 0.55 \| 0.5938 \| 2.4668 \| 0.55 \| 0.5467 \| 0.2072 \| 0.2059 \| \| 3.6002 \| 78.0 \| 546 \| 5.2671 \| 0.545 \| 0.5961 \| 2.4394 \| 0.545 \| 0.5421 \| 0.2056 \| 0.2093 \| \| 3.6002 \| 79.0 \| 553 \| 5.2923 \| 0.545 \| 0.5950 \| 2.4662 \| 0.545 \| 0.5455 \| 0.1833 \| 0.2058 \| \| 3.6002 \| 80.0 \| 560 \| 5.2854 \| 0.555 \| 0.5918 \| 2.5010 \| 0.555 \| 0.5526 \| 0.2040 \| 0.2059 \| \| 3.6002 \| 81.0 \| 567 \| 5.3009 \| 0.535 \| 0.5955 \| 2.4253 \| 0.535 \| 0.5319 \| 0.1939 \| 0.2101 \| \| 3.6002 \| 82.0 \| 574 \| 5.3016 \| 0.535 \| 0.5979 \| 2.4528 \| 0.535 \| 0.5315 \| 0.2020 \| 0.2101 \| \| 3.6002 \| 83.0 \| 581 \| 5.3262 \| 0.545 \| 0.5990 \| 2.4245 \| 0.545 \| 0.5422 \| 0.1816 \| 0.2081 \| \| 3.6002 \| 84.0 \| 588 \| 5.3206 \| 0.535 \| 0.5990 \| 2.4519 \| 0.535 \| 0.5350 \| 0.1959 \| 0.2121 \| \| 3.6002 \| 85.0 \| 595 \| 5.3333 \| 0.5375 \| 0.5999 \| 2.4909 \| 0.5375 \| 0.5352 \| 0.1881 \| 0.2109 \| \| 3.6002 \| 86.0 \| 602 \| 5.3407 \| 0.535 \| 0.6008 \| 2.5019 \| 0.535 \| 0.5331 \| 0.2087 \| 0.2096 \| \| 3.6002 \| 87.0 \| 609 \| 5.3413 \| 0.5425 \| 0.6015 \| 2.4753 \| 0.5425 \| 0.5402 \| 0.2147 \| 0.2101 \| \| 3.6002 \| 88.0 \| 616 \| 5.3716 \| 0.5375 \| 0.6041 \| 2.4290 \| 0.5375 \| 0.5373 \| 0.2234 \| 0.2094 \| \| 3.6002 \| 89.0 \| 623 \| 5.3639 \| 0.535 \| 0.6010 \| 2.4159 \| 0.535 \| 0.5319 \| 0.2068 \| 0.2108 \| \| 3.6002 \| 90.0 \| 630 \| 5.3742 \| 0.5425 \| 0.6030 \| 2.4588 \| 0.5425 \| 0.5420 \| 0.2021 \| 0.2099 \| \| 3.6002 \| 91.0 \| 637 \| 5.3731 \| 0.53 \| 0.6046 \| 2.4580 \| 0.53 \| 0.5284 \| 0.2193 \| 0.2122 \| \| 3.6002 \| 92.0 \| 644 \| 5.3919 \| 0.54 \| 0.6051 \| 2.4317 \| 0.54 \| 0.5395 \| 0.2057 \| 0.2090 \| \| 3.6002 \| 93.0 \| 651 \| 5.3947 \| 0.54 \| 0.6049 \| 2.4372 \| 0.54 \| 0.5385 \| 0.2053 \| 0.2092 \| \| 3.6002 \| 94.0 \| 658 \| 5.4070 \| 0.535 \| 0.6067 \| 2.4600 \| 0.535 \| 0.5328 \| 0.2297 \| 0.2108 \| \| 3.6002 \| 95.0 \| 665 \| 5.4129 \| 0.535 \| 0.6071 \| 2.4249 \| 0.535 \| 0.5345 \| 0.2186 \| 0.2104 \| \| 3.6002 \| 96.0 \| 672 \| 5.4137 \| 0.535 \| 0.6071 \| 2.4580 \| 0.535 \| 0.5339 \| 0.2192 \| 0.2102 \| \| 3.6002 \| 97.0 \| 679 \| 5.4218 \| 0.5325 \| 0.6079 \| 2.4584 \| 0.5325 \| 0.5316 \| 0.2197 \| 0.2112 \| \| 3.6002 \| 98.0 \| 686 \| 5.4236 \| 0.5325 \| 0.6083 \| 2.4585 \| 0.5325 \| 0.5311 \| 0.2154 \| 0.2110 \| \| 3.6002 \| 99.0 \| 693 \| 5.4261 \| 0.5325 \| 0.6081 \| 2.4569 \| 0.5325 \| 0.5316 \| 0.2294 \| 0.2114 \| \| 3.6002 \| 100.0 \| 700 \| 5.4262 \| 0.535 \| 0.6080 \| 2.4569 \| 0.535 \| 0.5345 \| 0.2120 \| 0.2105 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Trial006-007-008-YEL_STEM3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1618 - Accuracy: 0.9241 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7035 \| 1.0 \| 6 \| 0.6557 \| 0.6203 \| \| 0.6168 \| 2.0 \| 12 \| 0.5788 \| 0.7215 \| \| 0.5412 \| 3.0 \| 18 \| 0.5005 \| 0.7785 \| \| 0.496 \| 4.0 \| 24 \| 0.4946 \| 0.7722 \| \| 0.4024 \| 5.0 \| 30 \| 0.4057 \| 0.8165 \| \| 0.4098 \| 6.0 \| 36 \| 0.3076 \| 0.8544 \| \| 0.3645 \| 7.0 \| 42 \| 0.3250 \| 0.8418 \| \| 0.276 \| 8.0 \| 48 \| 0.2206 \| 0.8924 \| \| 0.3358 \| 9.0 \| 54 \| 0.2100 \| 0.8987 \| \| 0.3386 \| 10.0 \| 60 \| 0.1618 \| 0.9241 \| \| 0.2778 \| 11.0 \| 66 \| 0.1609 \| 0.9177 \| \| 0.25 \| 12.0 \| 72 \| 0.1581 \| 0.9114 \| \| 0.2914 \| 13.0 \| 78 \| 0.1663 \| 0.9114 \| \| 0.2273 \| 14.0 \| 84 \| 0.1525 \| 0.9177 \| \| 0.2694 \| 15.0 \| 90 \| 0.1708 \| 0.9051 \| \| 0.2745 \| 16.0 \| 96 \| 0.2364 \| 0.8734 \| \| 0.2809 \| 17.0 \| 102 \| 0.1976 \| 0.8608 \| \| 0.2368 \| 18.0 \| 108 \| 0.1517 \| 0.9114 \| \| 0.328 \| 19.0 \| 114 \| 0.2454 \| 0.8671 \| \| 0.2571 \| 20.0 \| 120 \| 0.1482 \| 0.9114 \| \| 0.2996 \| 21.0 \| 126 \| 0.1629 \| 0.8987 \| \| 0.266 \| 22.0 \| 132 \| 0.1360 \| 0.9114 \| \| 0.2323 \| 23.0 \| 138 \| 0.1427 \| 0.9114 \| \| 0.2285 \| 24.0 \| 144 \| 0.1683 \| 0.9051 \| \| 0.2566 \| 25.0 \| 150 \| 0.1442 \| 0.9114 \| \| 0.2509 \| 26.0 \| 156 \| 0.1595 \| 0.9114 \| \| 0.2337 \| 27.0 \| 162 \| 0.1291 \| 0.9177 \| \| 0.2203 \| 28.0 \| 168 \| 0.1302 \| 0.8987 \| \| 0.2409 \| 29.0 \| 174 \| 0.1274 \| 0.9114 \| \| 0.2256 \| 30.0 \| 180 \| 0.1272 \| 0.8987 \| \| 0.2157 \| 31.0 \| 186 \| 0.1289 \| 0.9177 \| \| 0.2168 \| 32.0 \| 192 \| 0.1267 \| 0.9114 \| \| 0.2426 \| 33.0 \| 198 \| 0.1438 \| 0.8987 \| \| 0.2404 \| 34.0 \| 204 \| 0.1388 \| 0.8987 \| \| 0.2218 \| 35.0 \| 210 \| 0.1243 \| 0.9241 \| \| 0.3068 \| 36.0 \| 216 \| 0.1268 \| 0.9241 \| \| 0.1721 \| 37.0 \| 222 \| 0.1477 \| 0.8987 \| \| 0.2201 \| 38.0 \| 228 \| 0.1545 \| 0.8987 \| \| 0.2581 \| 39.0 \| 234 \| 0.1700 \| 0.8987 \| \| 0.213 \| 40.0 \| 240 \| 0.1254 \| 0.9114 \| \| 0.2953 \| 41.0 \| 246 \| 0.1237 \| 0.9114 \| \| 0.2564 \| 42.0 \| 252 \| 0.1472 \| 0.9051 \| \| 0.249 \| 43.0 \| 258 \| 0.1409 \| 0.9051 \| \| 0.2372 \| 44.0 \| 264 \| 0.1495 \| 0.9114 \| \| 0.2541 \| 45.0 \| 270 \| 0.1412 \| 0.9051 \| \| 0.1997 \| 46.0 \| 276 \| 0.1308 \| 0.9114 \| \| 0.2381 \| 47.0 \| 282 \| 0.1253 \| 0.9177 \| \| 0.2623 \| 48.0 \| 288 \| 0.1267 \| 0.9051 \| \| 0.1855 \| 49.0 \| 294 \| 0.1285 \| 0.9051 \| \| 0.1877 \| 50.0 \| 300 \| 0.1289 \| 0.9051 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1	[ "plant2", "plant3" ]
sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Trial006-007-008-YEL_STEM4 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1729 - Accuracy: 0.9355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6572 \| 1.0 \| 6 \| 0.6719 \| 0.5677 \| \| 0.6279 \| 2.0 \| 12 \| 0.6436 \| 0.6516 \| \| 0.5282 \| 3.0 \| 18 \| 0.5677 \| 0.7871 \| \| 0.5405 \| 4.0 \| 24 \| 0.4718 \| 0.8258 \| \| 0.5276 \| 5.0 \| 30 \| 0.4959 \| 0.7419 \| \| 0.412 \| 6.0 \| 36 \| 0.3081 \| 0.8581 \| \| 0.3011 \| 7.0 \| 42 \| 0.2542 \| 0.8839 \| \| 0.3329 \| 8.0 \| 48 \| 0.2350 \| 0.8774 \| \| 0.4478 \| 9.0 \| 54 \| 0.1743 \| 0.9290 \| \| 0.268 \| 10.0 \| 60 \| 0.1708 \| 0.9161 \| \| 0.2177 \| 11.0 \| 66 \| 0.1729 \| 0.9355 \| \| 0.2675 \| 12.0 \| 72 \| 0.1913 \| 0.8968 \| \| 0.4784 \| 13.0 \| 78 \| 0.1826 \| 0.9032 \| \| 0.2456 \| 14.0 \| 84 \| 0.1774 \| 0.9032 \| \| 0.6229 \| 15.0 \| 90 \| 0.2196 \| 0.8968 \| \| 0.2561 \| 16.0 \| 96 \| 0.1823 \| 0.9226 \| \| 0.3785 \| 17.0 \| 102 \| 0.1770 \| 0.9032 \| \| 0.2334 \| 18.0 \| 108 \| 0.2056 \| 0.8903 \| \| 0.1904 \| 19.0 \| 114 \| 0.1564 \| 0.9097 \| \| 0.2256 \| 20.0 \| 120 \| 0.1407 \| 0.9226 \| \| 0.2547 \| 21.0 \| 126 \| 0.1552 \| 0.9032 \| \| 0.3468 \| 22.0 \| 132 \| 0.1819 \| 0.8968 \| \| 0.4116 \| 23.0 \| 138 \| 0.1537 \| 0.9290 \| \| 0.3689 \| 24.0 \| 144 \| 0.1645 \| 0.9097 \| \| 0.3541 \| 25.0 \| 150 \| 0.1527 \| 0.9290 \| \| 0.2498 \| 26.0 \| 156 \| 0.1670 \| 0.9161 \| \| 0.3625 \| 27.0 \| 162 \| 0.1522 \| 0.9161 \| \| 0.2463 \| 28.0 \| 168 \| 0.1552 \| 0.9226 \| \| 0.3447 \| 29.0 \| 174 \| 0.1510 \| 0.9097 \| \| 0.205 \| 30.0 \| 180 \| 0.1924 \| 0.9032 \| \| 0.2023 \| 31.0 \| 186 \| 0.1376 \| 0.9355 \| \| 0.3617 \| 32.0 \| 192 \| 0.1518 \| 0.9097 \| \| 0.3515 \| 33.0 \| 198 \| 0.1473 \| 0.9097 \| \| 0.1927 \| 34.0 \| 204 \| 0.1544 \| 0.9097 \| \| 0.4567 \| 35.0 \| 210 \| 0.1528 \| 0.9097 \| \| 0.3113 \| 36.0 \| 216 \| 0.1510 \| 0.9226 \| \| 0.3475 \| 37.0 \| 222 \| 0.1594 \| 0.9161 \| \| 0.1889 \| 38.0 \| 228 \| 0.1448 \| 0.9290 \| \| 0.1979 \| 39.0 \| 234 \| 0.1533 \| 0.9226 \| \| 0.3578 \| 40.0 \| 240 \| 0.1627 \| 0.9097 \| \| 0.2004 \| 41.0 \| 246 \| 0.1620 \| 0.9161 \| \| 0.3567 \| 42.0 \| 252 \| 0.1475 \| 0.9226 \| \| 0.192 \| 43.0 \| 258 \| 0.1504 \| 0.9032 \| \| 0.1872 \| 44.0 \| 264 \| 0.1535 \| 0.9097 \| \| 0.2079 \| 45.0 \| 270 \| 0.1490 \| 0.9161 \| \| 0.1503 \| 46.0 \| 276 \| 0.1459 \| 0.9161 \| \| 0.169 \| 47.0 \| 282 \| 0.1506 \| 0.8968 \| \| 0.1884 \| 48.0 \| 288 \| 0.1556 \| 0.8968 \| \| 0.1638 \| 49.0 \| 294 \| 0.1573 \| 0.8968 \| \| 0.1921 \| 50.0 \| 300 \| 0.1570 \| 0.8968 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1	[ "plant2", "plant3" ]
02shanky/test_model_graphics_classification_LION	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_model_graphics_classification_LION This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1569 - Accuracy: 0.9558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1765 \| 0.99 \| 42 \| 0.1569 \| 0.9558 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "cartoon", "icon", "images", "nongraphics" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6786 - Accuracy: 0.5475 - Brier Loss: 0.5719 - Nll: 2.4381 - F1 Micro: 0.5475 - F1 Macro: 0.5432 - Ece: 0.1563 - Aurc: 0.2080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.6969 \| 0.0425 \| 1.0710 \| 7.3408 \| 0.0425 \| 0.0336 \| 0.2818 \| 0.9543 \| \| No log \| 2.0 \| 14 \| 3.6834 \| 0.1 \| 0.9492 \| 5.8001 \| 0.1000 \| 0.0878 \| 0.1662 \| 0.8811 \| \| No log \| 3.0 \| 21 \| 3.2796 \| 0.2325 \| 0.8815 \| 5.3289 \| 0.2325 \| 0.1817 \| 0.1601 \| 0.6217 \| \| No log \| 4.0 \| 28 \| 2.7674 \| 0.2975 \| 0.7907 \| 3.6563 \| 0.2975 \| 0.2855 \| 0.1624 \| 0.4730 \| \| No log \| 5.0 \| 35 \| 2.4072 \| 0.3925 \| 0.7175 \| 3.0843 \| 0.3925 \| 0.3726 \| 0.1811 \| 0.3501 \| \| No log \| 6.0 \| 42 \| 2.2499 \| 0.44 \| 0.6923 \| 3.0183 \| 0.44 \| 0.4252 \| 0.2052 \| 0.3017 \| \| No log \| 7.0 \| 49 \| 2.2199 \| 0.455 \| 0.6973 \| 3.0254 \| 0.455 \| 0.4233 \| 0.2117 \| 0.2958 \| \| No log \| 8.0 \| 56 \| 2.1564 \| 0.4725 \| 0.6958 \| 2.8105 \| 0.4725 \| 0.4637 \| 0.2159 \| 0.2954 \| \| No log \| 9.0 \| 63 \| 2.2963 \| 0.4575 \| 0.7105 \| 2.7884 \| 0.4575 \| 0.4386 \| 0.2617 \| 0.2866 \| \| No log \| 10.0 \| 70 \| 2.3511 \| 0.4475 \| 0.7553 \| 2.8174 \| 0.4475 \| 0.4278 \| 0.2698 \| 0.3171 \| \| No log \| 11.0 \| 77 \| 2.4111 \| 0.4425 \| 0.7483 \| 2.9313 \| 0.4425 \| 0.4290 \| 0.2569 \| 0.3056 \| \| No log \| 12.0 \| 84 \| 2.2927 \| 0.4825 \| 0.7158 \| 2.7197 \| 0.4825 \| 0.4799 \| 0.2453 \| 0.2920 \| \| No log \| 13.0 \| 91 \| 2.2809 \| 0.4825 \| 0.7098 \| 2.6808 \| 0.4825 \| 0.4758 \| 0.2454 \| 0.2811 \| \| No log \| 14.0 \| 98 \| 2.1787 \| 0.4825 \| 0.7036 \| 2.5537 \| 0.4825 \| 0.4801 \| 0.2417 \| 0.2877 \| \| No log \| 15.0 \| 105 \| 2.1643 \| 0.495 \| 0.6934 \| 2.7769 \| 0.495 \| 0.4892 \| 0.2375 \| 0.2707 \| \| No log \| 16.0 \| 112 \| 2.1309 \| 0.48 \| 0.6866 \| 2.5936 \| 0.48 \| 0.4647 \| 0.2130 \| 0.2663 \| \| No log \| 17.0 \| 119 \| 2.1389 \| 0.5125 \| 0.6580 \| 2.6313 \| 0.5125 \| 0.4962 \| 0.1976 \| 0.2424 \| \| No log \| 18.0 \| 126 \| 2.0813 \| 0.5025 \| 0.6649 \| 2.6517 \| 0.5025 \| 0.5019 \| 0.2126 \| 0.2602 \| \| No log \| 19.0 \| 133 \| 2.0742 \| 0.4975 \| 0.6763 \| 2.6690 \| 0.4975 \| 0.4739 \| 0.2473 \| 0.2575 \| \| No log \| 20.0 \| 140 \| 2.0550 \| 0.48 \| 0.6702 \| 2.5468 \| 0.48 \| 0.4838 \| 0.2206 \| 0.2745 \| \| No log \| 21.0 \| 147 \| 1.9216 \| 0.52 \| 0.6332 \| 2.6838 \| 0.52 \| 0.5237 \| 0.1895 \| 0.2410 \| \| No log \| 22.0 \| 154 \| 1.9572 \| 0.5025 \| 0.6500 \| 2.4719 \| 0.5025 \| 0.4877 \| 0.2223 \| 0.2470 \| \| No log \| 23.0 \| 161 \| 1.8821 \| 0.5175 \| 0.6294 \| 2.5337 \| 0.5175 \| 0.5067 \| 0.2081 \| 0.2332 \| \| No log \| 24.0 \| 168 \| 1.9198 \| 0.5125 \| 0.6332 \| 2.5310 \| 0.5125 \| 0.5028 \| 0.2039 \| 0.2434 \| \| No log \| 25.0 \| 175 \| 1.9419 \| 0.515 \| 0.6358 \| 2.6324 \| 0.515 \| 0.5005 \| 0.2203 \| 0.2383 \| \| No log \| 26.0 \| 182 \| 1.8714 \| 0.525 \| 0.6145 \| 2.5820 \| 0.525 \| 0.5005 \| 0.1843 \| 0.2232 \| \| No log \| 27.0 \| 189 \| 1.9460 \| 0.4975 \| 0.6365 \| 2.6119 \| 0.4975 \| 0.4907 \| 0.2169 \| 0.2395 \| \| No log \| 28.0 \| 196 \| 1.9364 \| 0.525 \| 0.6333 \| 2.5789 \| 0.525 \| 0.5013 \| 0.1808 \| 0.2437 \| \| No log \| 29.0 \| 203 \| 1.9143 \| 0.5425 \| 0.6144 \| 2.7809 \| 0.5425 \| 0.5181 \| 0.1700 \| 0.2224 \| \| No log \| 30.0 \| 210 \| 1.8565 \| 0.5275 \| 0.6171 \| 2.5610 \| 0.5275 \| 0.5142 \| 0.1823 \| 0.2304 \| \| No log \| 31.0 \| 217 \| 1.8281 \| 0.5325 \| 0.6113 \| 2.6216 \| 0.5325 \| 0.5191 \| 0.1802 \| 0.2167 \| \| No log \| 32.0 \| 224 \| 1.8213 \| 0.525 \| 0.6192 \| 2.5521 \| 0.525 \| 0.5278 \| 0.1844 \| 0.2368 \| \| No log \| 33.0 \| 231 \| 1.7724 \| 0.545 \| 0.6030 \| 2.4900 \| 0.545 \| 0.5460 \| 0.1810 \| 0.2211 \| \| No log \| 34.0 \| 238 \| 1.7617 \| 0.5475 \| 0.5985 \| 2.5994 \| 0.5475 \| 0.5345 \| 0.1907 \| 0.2161 \| \| No log \| 35.0 \| 245 \| 1.8276 \| 0.54 \| 0.6090 \| 2.5254 \| 0.54 \| 0.5172 \| 0.1839 \| 0.2249 \| \| No log \| 36.0 \| 252 \| 1.7646 \| 0.5475 \| 0.5924 \| 2.5729 \| 0.5475 \| 0.5458 \| 0.1930 \| 0.2190 \| \| No log \| 37.0 \| 259 \| 1.7851 \| 0.5675 \| 0.5872 \| 2.5036 \| 0.5675 \| 0.5600 \| 0.1670 \| 0.2056 \| \| No log \| 38.0 \| 266 \| 1.7743 \| 0.5225 \| 0.6136 \| 2.4765 \| 0.5225 \| 0.5163 \| 0.1831 \| 0.2276 \| \| No log \| 39.0 \| 273 \| 1.7760 \| 0.5325 \| 0.5912 \| 2.5692 \| 0.5325 \| 0.5228 \| 0.1589 \| 0.2108 \| \| No log \| 40.0 \| 280 \| 1.7664 \| 0.53 \| 0.5917 \| 2.5363 \| 0.53 \| 0.5089 \| 0.1800 \| 0.2095 \| \| No log \| 41.0 \| 287 \| 1.8177 \| 0.535 \| 0.6070 \| 2.5195 \| 0.535 \| 0.5272 \| 0.1883 \| 0.2268 \| \| No log \| 42.0 \| 294 \| 1.7575 \| 0.56 \| 0.5868 \| 2.5956 \| 0.56 \| 0.5504 \| 0.1740 \| 0.2091 \| \| No log \| 43.0 \| 301 \| 1.7616 \| 0.54 \| 0.6001 \| 2.3469 \| 0.54 \| 0.5401 \| 0.1861 \| 0.2272 \| \| No log \| 44.0 \| 308 \| 1.7105 \| 0.5425 \| 0.5831 \| 2.4709 \| 0.5425 \| 0.5380 \| 0.1884 \| 0.2086 \| \| No log \| 45.0 \| 315 \| 1.7502 \| 0.565 \| 0.5880 \| 2.4600 \| 0.565 \| 0.5546 \| 0.1527 \| 0.2084 \| \| No log \| 46.0 \| 322 \| 1.7135 \| 0.565 \| 0.5834 \| 2.4571 \| 0.565 \| 0.5631 \| 0.1703 \| 0.2103 \| \| No log \| 47.0 \| 329 \| 1.7327 \| 0.5525 \| 0.5892 \| 2.4573 \| 0.5525 \| 0.5491 \| 0.1820 \| 0.2178 \| \| No log \| 48.0 \| 336 \| 1.7405 \| 0.535 \| 0.5947 \| 2.5414 \| 0.535 \| 0.5338 \| 0.1840 \| 0.2180 \| \| No log \| 49.0 \| 343 \| 1.7265 \| 0.555 \| 0.5758 \| 2.4824 \| 0.555 \| 0.5423 \| 0.1603 \| 0.2025 \| \| No log \| 50.0 \| 350 \| 1.7065 \| 0.5525 \| 0.5904 \| 2.3807 \| 0.5525 \| 0.5576 \| 0.1769 \| 0.2212 \| \| No log \| 51.0 \| 357 \| 1.7265 \| 0.545 \| 0.5833 \| 2.4535 \| 0.545 \| 0.5413 \| 0.1704 \| 0.2108 \| \| No log \| 52.0 \| 364 \| 1.7197 \| 0.55 \| 0.5740 \| 2.5386 \| 0.55 \| 0.5347 \| 0.1467 \| 0.2032 \| \| No log \| 53.0 \| 371 \| 1.7015 \| 0.5575 \| 0.5826 \| 2.4159 \| 0.5575 \| 0.5578 \| 0.1751 \| 0.2138 \| \| No log \| 54.0 \| 378 \| 1.7263 \| 0.55 \| 0.5873 \| 2.4471 \| 0.55 \| 0.5456 \| 0.1629 \| 0.2144 \| \| No log \| 55.0 \| 385 \| 1.6786 \| 0.555 \| 0.5780 \| 2.3908 \| 0.555 \| 0.5490 \| 0.1627 \| 0.2106 \| \| No log \| 56.0 \| 392 \| 1.7147 \| 0.55 \| 0.5811 \| 2.3876 \| 0.55 \| 0.5476 \| 0.1724 \| 0.2122 \| \| No log \| 57.0 \| 399 \| 1.6983 \| 0.5525 \| 0.5769 \| 2.5028 \| 0.5525 \| 0.5415 \| 0.1716 \| 0.2054 \| \| No log \| 58.0 \| 406 \| 1.7350 \| 0.5425 \| 0.5984 \| 2.3835 \| 0.5425 \| 0.5406 \| 0.1806 \| 0.2282 \| \| No log \| 59.0 \| 413 \| 1.7015 \| 0.54 \| 0.5779 \| 2.4865 \| 0.54 \| 0.5288 \| 0.1848 \| 0.2079 \| \| No log \| 60.0 \| 420 \| 1.6783 \| 0.5525 \| 0.5712 \| 2.4231 \| 0.5525 \| 0.5497 \| 0.1777 \| 0.2046 \| \| No log \| 61.0 \| 427 \| 1.7236 \| 0.545 \| 0.5856 \| 2.5104 \| 0.545 \| 0.5379 \| 0.1850 \| 0.2142 \| \| No log \| 62.0 \| 434 \| 1.6904 \| 0.5425 \| 0.5752 \| 2.4626 \| 0.5425 \| 0.5400 \| 0.1787 \| 0.2084 \| \| No log \| 63.0 \| 441 \| 1.7136 \| 0.5375 \| 0.5837 \| 2.4091 \| 0.5375 \| 0.5304 \| 0.1681 \| 0.2150 \| \| No log \| 64.0 \| 448 \| 1.6783 \| 0.56 \| 0.5762 \| 2.4017 \| 0.56 \| 0.5594 \| 0.1607 \| 0.2093 \| \| No log \| 65.0 \| 455 \| 1.7098 \| 0.5425 \| 0.5797 \| 2.5062 \| 0.5425 \| 0.5406 \| 0.1707 \| 0.2120 \| \| No log \| 66.0 \| 462 \| 1.6752 \| 0.5525 \| 0.5712 \| 2.4684 \| 0.5525 \| 0.5478 \| 0.1711 \| 0.2063 \| \| No log \| 67.0 \| 469 \| 1.7068 \| 0.545 \| 0.5761 \| 2.5107 \| 0.545 \| 0.5415 \| 0.1750 \| 0.2090 \| \| No log \| 68.0 \| 476 \| 1.6706 \| 0.5525 \| 0.5700 \| 2.4322 \| 0.5525 \| 0.5466 \| 0.1798 \| 0.2068 \| \| No log \| 69.0 \| 483 \| 1.6941 \| 0.5525 \| 0.5749 \| 2.4689 \| 0.5525 \| 0.5501 \| 0.1654 \| 0.2072 \| \| No log \| 70.0 \| 490 \| 1.6726 \| 0.545 \| 0.5711 \| 2.4348 \| 0.545 \| 0.5416 \| 0.1811 \| 0.2083 \| \| No log \| 71.0 \| 497 \| 1.6831 \| 0.5525 \| 0.5740 \| 2.4659 \| 0.5525 \| 0.5498 \| 0.1687 \| 0.2061 \| \| 0.4086 \| 72.0 \| 504 \| 1.6749 \| 0.5575 \| 0.5699 \| 2.4122 \| 0.5575 \| 0.5535 \| 0.1706 \| 0.2051 \| \| 0.4086 \| 73.0 \| 511 \| 1.6867 \| 0.555 \| 0.5740 \| 2.4402 \| 0.555 \| 0.5495 \| 0.1725 \| 0.2069 \| \| 0.4086 \| 74.0 \| 518 \| 1.6768 \| 0.5475 \| 0.5716 \| 2.4369 \| 0.5475 \| 0.5442 \| 0.1621 \| 0.2080 \| \| 0.4086 \| 75.0 \| 525 \| 1.6835 \| 0.55 \| 0.5736 \| 2.4404 \| 0.55 \| 0.5448 \| 0.1824 \| 0.2075 \| \| 0.4086 \| 76.0 \| 532 \| 1.6737 \| 0.5525 \| 0.5701 \| 2.4361 \| 0.5525 \| 0.5497 \| 0.1731 \| 0.2063 \| \| 0.4086 \| 77.0 \| 539 \| 1.6796 \| 0.55 \| 0.5721 \| 2.4399 \| 0.55 \| 0.5450 \| 0.1723 \| 0.2066 \| \| 0.4086 \| 78.0 \| 546 \| 1.6774 \| 0.55 \| 0.5718 \| 2.4362 \| 0.55 \| 0.5472 \| 0.1732 \| 0.2071 \| \| 0.4086 \| 79.0 \| 553 \| 1.6781 \| 0.555 \| 0.5711 \| 2.4390 \| 0.555 \| 0.5506 \| 0.1588 \| 0.2059 \| \| 0.4086 \| 80.0 \| 560 \| 1.6787 \| 0.555 \| 0.5714 \| 2.4380 \| 0.555 \| 0.5508 \| 0.1832 \| 0.2070 \| \| 0.4086 \| 81.0 \| 567 \| 1.6778 \| 0.555 \| 0.5717 \| 2.4366 \| 0.555 \| 0.5516 \| 0.1615 \| 0.2060 \| \| 0.4086 \| 82.0 \| 574 \| 1.6788 \| 0.55 \| 0.5717 \| 2.4380 \| 0.55 \| 0.5462 \| 0.1680 \| 0.2070 \| \| 0.4086 \| 83.0 \| 581 \| 1.6761 \| 0.5525 \| 0.5712 \| 2.4366 \| 0.5525 \| 0.5505 \| 0.1809 \| 0.2064 \| \| 0.4086 \| 84.0 \| 588 \| 1.6778 \| 0.55 \| 0.5714 \| 2.4388 \| 0.55 \| 0.5443 \| 0.1571 \| 0.2073 \| \| 0.4086 \| 85.0 \| 595 \| 1.6772 \| 0.5525 \| 0.5716 \| 2.4367 \| 0.5525 \| 0.5479 \| 0.1768 \| 0.2073 \| \| 0.4086 \| 86.0 \| 602 \| 1.6789 \| 0.5525 \| 0.5722 \| 2.4376 \| 0.5525 \| 0.5470 \| 0.1646 \| 0.2066 \| \| 0.4086 \| 87.0 \| 609 \| 1.6784 \| 0.5525 \| 0.5717 \| 2.4384 \| 0.5525 \| 0.5486 \| 0.1743 \| 0.2073 \| \| 0.4086 \| 88.0 \| 616 \| 1.6786 \| 0.55 \| 0.5720 \| 2.4382 \| 0.55 \| 0.5443 \| 0.1559 \| 0.2077 \| \| 0.4086 \| 89.0 \| 623 \| 1.6784 \| 0.5525 \| 0.5718 \| 2.4379 \| 0.5525 \| 0.5479 \| 0.1561 \| 0.2073 \| \| 0.4086 \| 90.0 \| 630 \| 1.6782 \| 0.5525 \| 0.5718 \| 2.4381 \| 0.5525 \| 0.5482 \| 0.1561 \| 0.2066 \| \| 0.4086 \| 91.0 \| 637 \| 1.6784 \| 0.5525 \| 0.5719 \| 2.4375 \| 0.5525 \| 0.5482 \| 0.1612 \| 0.2065 \| \| 0.4086 \| 92.0 \| 644 \| 1.6783 \| 0.55 \| 0.5718 \| 2.4377 \| 0.55 \| 0.5456 \| 0.1702 \| 0.2073 \| \| 0.4086 \| 93.0 \| 651 \| 1.6781 \| 0.55 \| 0.5718 \| 2.4378 \| 0.55 \| 0.5456 \| 0.1581 \| 0.2075 \| \| 0.4086 \| 94.0 \| 658 \| 1.6785 \| 0.55 \| 0.5719 \| 2.4379 \| 0.55 \| 0.5455 \| 0.1602 \| 0.2074 \| \| 0.4086 \| 95.0 \| 665 \| 1.6785 \| 0.55 \| 0.5719 \| 2.4379 \| 0.55 \| 0.5456 \| 0.1635 \| 0.2075 \| \| 0.4086 \| 96.0 \| 672 \| 1.6785 \| 0.5475 \| 0.5719 \| 2.4381 \| 0.5475 \| 0.5432 \| 0.1659 \| 0.2080 \| \| 0.4086 \| 97.0 \| 679 \| 1.6786 \| 0.55 \| 0.5719 \| 2.4381 \| 0.55 \| 0.5458 \| 0.1545 \| 0.2071 \| \| 0.4086 \| 98.0 \| 686 \| 1.6786 \| 0.5475 \| 0.5719 \| 2.4381 \| 0.5475 \| 0.5431 \| 0.1613 \| 0.2081 \| \| 0.4086 \| 99.0 \| 693 \| 1.6786 \| 0.5475 \| 0.5719 \| 2.4381 \| 0.5475 \| 0.5431 \| 0.1613 \| 0.2081 \| \| 0.4086 \| 100.0 \| 700 \| 1.6786 \| 0.5475 \| 0.5719 \| 2.4381 \| 0.5475 \| 0.5432 \| 0.1563 \| 0.2080 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-small_rvl_cdip	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-small_rvl_cdip This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0854 - Accuracy: 0.9110 - Brier Loss: 0.1305 - Nll: 1.2339 - F1 Micro: 0.9110 - F1 Macro: 0.9112 - Ece: 0.0097 - Aurc: 0.0122 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| 0.2638 \| 1.0 \| 2500 \| 0.2093 \| 0.8574 \| 0.2035 \| 1.6580 \| 0.8574 \| 0.8564 \| 0.0105 \| 0.0272 \| \| 0.1559 \| 2.0 \| 5000 \| 0.1413 \| 0.8854 \| 0.1645 \| 1.5062 \| 0.8854 \| 0.8863 \| 0.0101 \| 0.0185 \| \| 0.1036 \| 3.0 \| 7500 \| 0.1212 \| 0.8994 \| 0.1461 \| 1.4353 \| 0.8994 \| 0.8999 \| 0.0096 \| 0.0150 \| \| 0.0693 \| 4.0 \| 10000 \| 0.1196 \| 0.9013 \| 0.1440 \| 1.3827 \| 0.9013 \| 0.9017 \| 0.0152 \| 0.0148 \| \| 0.0451 \| 5.0 \| 12500 \| 0.1094 \| 0.9062 \| 0.1391 \| 1.3099 \| 0.9062 \| 0.9064 \| 0.0124 \| 0.0139 \| \| 0.0317 \| 6.0 \| 15000 \| 0.0997 \| 0.9073 \| 0.1357 \| 1.2889 \| 0.9073 \| 0.9078 \| 0.0091 \| 0.0132 \| \| 0.0224 \| 7.0 \| 17500 \| 0.0961 \| 0.9081 \| 0.1348 \| 1.2705 \| 0.9081 \| 0.9082 \| 0.0100 \| 0.0129 \| \| 0.0166 \| 8.0 \| 20000 \| 0.0890 \| 0.9099 \| 0.1328 \| 1.2484 \| 0.9099 \| 0.9102 \| 0.0085 \| 0.0126 \| \| 0.0117 \| 9.0 \| 22500 \| 0.0862 \| 0.9096 \| 0.1316 \| 1.2428 \| 0.9096 \| 0.9100 \| 0.0100 \| 0.0123 \| \| 0.0085 \| 10.0 \| 25000 \| 0.0854 \| 0.9110 \| 0.1305 \| 1.2339 \| 0.9110 \| 0.9112 \| 0.0097 \| 0.0122 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5923 - Accuracy: 0.57 - Brier Loss: 0.5750 - Nll: 2.3088 - F1 Micro: 0.57 - F1 Macro: 0.5661 - Ece: 0.1722 - Aurc: 0.2058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.3150 \| 0.0425 \| 1.0714 \| 7.3695 \| 0.0425 \| 0.0339 \| 0.2866 \| 0.9549 \| \| No log \| 2.0 \| 14 \| 3.3516 \| 0.1 \| 0.9507 \| 5.8558 \| 0.1000 \| 0.0866 \| 0.1647 \| 0.8831 \| \| No log \| 3.0 \| 21 \| 2.9890 \| 0.225 \| 0.8838 \| 5.3580 \| 0.225 \| 0.1805 \| 0.1576 \| 0.6316 \| \| No log \| 4.0 \| 28 \| 2.5376 \| 0.29 \| 0.7946 \| 3.5543 \| 0.29 \| 0.2749 \| 0.1832 \| 0.4807 \| \| No log \| 5.0 \| 35 \| 2.2193 \| 0.3875 \| 0.7186 \| 3.0794 \| 0.3875 \| 0.3677 \| 0.1577 \| 0.3531 \| \| No log \| 6.0 \| 42 \| 2.0818 \| 0.43 \| 0.6905 \| 2.9853 \| 0.4300 \| 0.4165 \| 0.1668 \| 0.3056 \| \| No log \| 7.0 \| 49 \| 2.1032 \| 0.45 \| 0.7019 \| 3.1044 \| 0.45 \| 0.4208 \| 0.2121 \| 0.2997 \| \| No log \| 8.0 \| 56 \| 2.0360 \| 0.455 \| 0.6994 \| 2.8491 \| 0.455 \| 0.4492 \| 0.2131 \| 0.3026 \| \| No log \| 9.0 \| 63 \| 2.1719 \| 0.475 \| 0.7092 \| 2.7831 \| 0.4750 \| 0.4549 \| 0.2332 \| 0.2870 \| \| No log \| 10.0 \| 70 \| 2.1820 \| 0.4525 \| 0.7393 \| 2.8185 \| 0.4525 \| 0.4318 \| 0.2813 \| 0.2994 \| \| No log \| 11.0 \| 77 \| 2.2659 \| 0.4475 \| 0.7485 \| 2.8020 \| 0.4475 \| 0.4227 \| 0.2677 \| 0.3046 \| \| No log \| 12.0 \| 84 \| 2.1798 \| 0.4575 \| 0.7325 \| 2.6772 \| 0.4575 \| 0.4555 \| 0.2738 \| 0.3081 \| \| No log \| 13.0 \| 91 \| 2.3031 \| 0.465 \| 0.7431 \| 2.8956 \| 0.465 \| 0.4390 \| 0.2771 \| 0.2945 \| \| No log \| 14.0 \| 98 \| 2.0867 \| 0.49 \| 0.7048 \| 2.5312 \| 0.49 \| 0.4823 \| 0.2528 \| 0.2921 \| \| No log \| 15.0 \| 105 \| 2.1671 \| 0.49 \| 0.7218 \| 2.7779 \| 0.49 \| 0.4749 \| 0.2396 \| 0.2877 \| \| No log \| 16.0 \| 112 \| 2.0091 \| 0.485 \| 0.6857 \| 2.7234 \| 0.485 \| 0.4608 \| 0.2493 \| 0.2577 \| \| No log \| 17.0 \| 119 \| 1.9494 \| 0.515 \| 0.6714 \| 2.4841 \| 0.515 \| 0.5072 \| 0.2380 \| 0.2614 \| \| No log \| 18.0 \| 126 \| 1.9132 \| 0.505 \| 0.6665 \| 2.4777 \| 0.505 \| 0.4945 \| 0.2206 \| 0.2622 \| \| No log \| 19.0 \| 133 \| 2.0539 \| 0.505 \| 0.6776 \| 2.7885 \| 0.505 \| 0.4986 \| 0.2209 \| 0.2724 \| \| No log \| 20.0 \| 140 \| 1.9533 \| 0.5125 \| 0.6666 \| 2.7287 \| 0.5125 \| 0.5044 \| 0.2385 \| 0.2645 \| \| No log \| 21.0 \| 147 \| 1.9038 \| 0.525 \| 0.6365 \| 2.8345 \| 0.525 \| 0.5099 \| 0.2021 \| 0.2290 \| \| No log \| 22.0 \| 154 \| 1.8525 \| 0.5075 \| 0.6448 \| 2.6337 \| 0.5075 \| 0.4958 \| 0.2083 \| 0.2494 \| \| No log \| 23.0 \| 161 \| 1.7880 \| 0.51 \| 0.6386 \| 2.4856 \| 0.51 \| 0.5078 \| 0.2186 \| 0.2478 \| \| No log \| 24.0 \| 168 \| 1.8363 \| 0.505 \| 0.6456 \| 2.5075 \| 0.505 \| 0.4966 \| 0.1962 \| 0.2399 \| \| No log \| 25.0 \| 175 \| 1.9655 \| 0.4725 \| 0.6864 \| 2.6331 \| 0.4725 \| 0.4608 \| 0.2291 \| 0.2669 \| \| No log \| 26.0 \| 182 \| 1.8660 \| 0.5175 \| 0.6547 \| 2.5404 \| 0.5175 \| 0.5076 \| 0.2252 \| 0.2489 \| \| No log \| 27.0 \| 189 \| 1.8693 \| 0.525 \| 0.6446 \| 2.6230 \| 0.525 \| 0.5145 \| 0.2047 \| 0.2540 \| \| No log \| 28.0 \| 196 \| 1.8113 \| 0.51 \| 0.6407 \| 2.4380 \| 0.51 \| 0.4978 \| 0.2030 \| 0.2454 \| \| No log \| 29.0 \| 203 \| 1.8313 \| 0.53 \| 0.6445 \| 2.4777 \| 0.53 \| 0.5284 \| 0.2071 \| 0.2575 \| \| No log \| 30.0 \| 210 \| 1.7310 \| 0.5425 \| 0.6197 \| 2.4559 \| 0.5425 \| 0.5384 \| 0.1869 \| 0.2367 \| \| No log \| 31.0 \| 217 \| 1.8023 \| 0.5325 \| 0.6351 \| 2.5026 \| 0.5325 \| 0.5216 \| 0.2081 \| 0.2496 \| \| No log \| 32.0 \| 224 \| 1.7652 \| 0.5325 \| 0.6186 \| 2.4794 \| 0.5325 \| 0.5145 \| 0.1715 \| 0.2338 \| \| No log \| 33.0 \| 231 \| 1.7583 \| 0.525 \| 0.6363 \| 2.4889 \| 0.525 \| 0.5275 \| 0.1984 \| 0.2463 \| \| No log \| 34.0 \| 238 \| 1.7552 \| 0.5475 \| 0.6164 \| 2.4291 \| 0.5475 \| 0.5305 \| 0.2026 \| 0.2377 \| \| No log \| 35.0 \| 245 \| 1.6839 \| 0.5375 \| 0.6085 \| 2.5915 \| 0.5375 \| 0.5253 \| 0.1828 \| 0.2214 \| \| No log \| 36.0 \| 252 \| 1.7480 \| 0.5425 \| 0.6104 \| 2.5809 \| 0.5425 \| 0.5366 \| 0.1716 \| 0.2232 \| \| No log \| 37.0 \| 259 \| 1.7534 \| 0.525 \| 0.6225 \| 2.3614 \| 0.525 \| 0.5183 \| 0.1930 \| 0.2249 \| \| No log \| 38.0 \| 266 \| 1.7484 \| 0.5425 \| 0.6125 \| 2.5714 \| 0.5425 \| 0.5282 \| 0.1792 \| 0.2272 \| \| No log \| 39.0 \| 273 \| 1.7073 \| 0.55 \| 0.6172 \| 2.4200 \| 0.55 \| 0.5370 \| 0.1902 \| 0.2314 \| \| No log \| 40.0 \| 280 \| 1.7303 \| 0.55 \| 0.6134 \| 2.4829 \| 0.55 \| 0.5394 \| 0.1916 \| 0.2324 \| \| No log \| 41.0 \| 287 \| 1.6684 \| 0.54 \| 0.6060 \| 2.4632 \| 0.54 \| 0.5350 \| 0.2028 \| 0.2251 \| \| No log \| 42.0 \| 294 \| 1.7171 \| 0.5375 \| 0.6055 \| 2.4705 \| 0.5375 \| 0.5213 \| 0.1776 \| 0.2262 \| \| No log \| 43.0 \| 301 \| 1.6493 \| 0.545 \| 0.5991 \| 2.5207 \| 0.545 \| 0.5412 \| 0.1779 \| 0.2214 \| \| No log \| 44.0 \| 308 \| 1.6548 \| 0.5625 \| 0.5920 \| 2.4810 \| 0.5625 \| 0.5568 \| 0.1892 \| 0.2182 \| \| No log \| 45.0 \| 315 \| 1.6392 \| 0.565 \| 0.5943 \| 2.3771 \| 0.565 \| 0.5586 \| 0.2165 \| 0.2162 \| \| No log \| 46.0 \| 322 \| 1.6923 \| 0.5225 \| 0.6159 \| 2.3661 \| 0.5225 \| 0.5158 \| 0.1775 \| 0.2400 \| \| No log \| 47.0 \| 329 \| 1.6266 \| 0.5525 \| 0.5827 \| 2.4385 \| 0.5525 \| 0.5468 \| 0.1845 \| 0.2100 \| \| No log \| 48.0 \| 336 \| 1.6804 \| 0.55 \| 0.6019 \| 2.3884 \| 0.55 \| 0.5481 \| 0.1895 \| 0.2291 \| \| No log \| 49.0 \| 343 \| 1.6202 \| 0.5725 \| 0.5847 \| 2.4882 \| 0.5725 \| 0.5596 \| 0.1642 \| 0.2125 \| \| No log \| 50.0 \| 350 \| 1.6222 \| 0.54 \| 0.5882 \| 2.4144 \| 0.54 \| 0.5311 \| 0.1830 \| 0.2226 \| \| No log \| 51.0 \| 357 \| 1.6119 \| 0.5775 \| 0.5794 \| 2.4063 \| 0.5775 \| 0.5731 \| 0.1647 \| 0.2019 \| \| No log \| 52.0 \| 364 \| 1.5958 \| 0.57 \| 0.5757 \| 2.3342 \| 0.57 \| 0.5642 \| 0.1778 \| 0.2094 \| \| No log \| 53.0 \| 371 \| 1.6206 \| 0.545 \| 0.5913 \| 2.3884 \| 0.545 \| 0.5365 \| 0.1799 \| 0.2187 \| \| No log \| 54.0 \| 378 \| 1.5982 \| 0.5675 \| 0.5745 \| 2.4276 \| 0.5675 \| 0.5640 \| 0.1746 \| 0.2050 \| \| No log \| 55.0 \| 385 \| 1.6258 \| 0.5525 \| 0.5856 \| 2.4005 \| 0.5525 \| 0.5373 \| 0.1890 \| 0.2124 \| \| No log \| 56.0 \| 392 \| 1.5763 \| 0.57 \| 0.5744 \| 2.4477 \| 0.57 \| 0.5729 \| 0.1651 \| 0.2081 \| \| No log \| 57.0 \| 399 \| 1.6249 \| 0.5525 \| 0.5861 \| 2.3791 \| 0.5525 \| 0.5432 \| 0.1531 \| 0.2114 \| \| No log \| 58.0 \| 406 \| 1.6240 \| 0.5775 \| 0.5791 \| 2.4540 \| 0.5775 \| 0.5730 \| 0.1582 \| 0.2054 \| \| No log \| 59.0 \| 413 \| 1.6149 \| 0.545 \| 0.5851 \| 2.3134 \| 0.545 \| 0.5395 \| 0.1870 \| 0.2137 \| \| No log \| 60.0 \| 420 \| 1.6163 \| 0.5775 \| 0.5792 \| 2.3778 \| 0.5775 \| 0.5708 \| 0.1762 \| 0.2076 \| \| No log \| 61.0 \| 427 \| 1.6132 \| 0.5575 \| 0.5868 \| 2.3759 \| 0.5575 \| 0.5530 \| 0.1842 \| 0.2159 \| \| No log \| 62.0 \| 434 \| 1.5940 \| 0.5725 \| 0.5756 \| 2.3394 \| 0.5725 \| 0.5731 \| 0.2102 \| 0.2054 \| \| No log \| 63.0 \| 441 \| 1.6167 \| 0.56 \| 0.5841 \| 2.4117 \| 0.56 \| 0.5541 \| 0.1806 \| 0.2160 \| \| No log \| 64.0 \| 448 \| 1.5988 \| 0.57 \| 0.5775 \| 2.3388 \| 0.57 \| 0.5667 \| 0.1680 \| 0.2064 \| \| No log \| 65.0 \| 455 \| 1.5893 \| 0.5725 \| 0.5752 \| 2.4281 \| 0.5725 \| 0.5695 \| 0.1624 \| 0.2050 \| \| No log \| 66.0 \| 462 \| 1.5975 \| 0.5725 \| 0.5737 \| 2.3760 \| 0.5725 \| 0.5662 \| 0.1733 \| 0.2026 \| \| No log \| 67.0 \| 469 \| 1.5903 \| 0.57 \| 0.5772 \| 2.2921 \| 0.57 \| 0.5675 \| 0.1888 \| 0.2112 \| \| No log \| 68.0 \| 476 \| 1.5878 \| 0.575 \| 0.5730 \| 2.3676 \| 0.575 \| 0.5706 \| 0.1683 \| 0.2039 \| \| No log \| 69.0 \| 483 \| 1.5950 \| 0.57 \| 0.5775 \| 2.3006 \| 0.57 \| 0.5641 \| 0.1639 \| 0.2076 \| \| No log \| 70.0 \| 490 \| 1.5916 \| 0.58 \| 0.5728 \| 2.3424 \| 0.58 \| 0.5769 \| 0.1714 \| 0.2026 \| \| No log \| 71.0 \| 497 \| 1.5960 \| 0.5675 \| 0.5784 \| 2.3057 \| 0.5675 \| 0.5624 \| 0.1600 \| 0.2073 \| \| 0.3705 \| 72.0 \| 504 \| 1.5907 \| 0.575 \| 0.5755 \| 2.3322 \| 0.575 \| 0.5723 \| 0.1578 \| 0.2066 \| \| 0.3705 \| 73.0 \| 511 \| 1.5918 \| 0.5675 \| 0.5762 \| 2.3182 \| 0.5675 \| 0.5605 \| 0.1942 \| 0.2071 \| \| 0.3705 \| 74.0 \| 518 \| 1.5894 \| 0.585 \| 0.5747 \| 2.3335 \| 0.585 \| 0.5818 \| 0.1739 \| 0.2035 \| \| 0.3705 \| 75.0 \| 525 \| 1.5878 \| 0.565 \| 0.5750 \| 2.3019 \| 0.565 \| 0.5607 \| 0.1649 \| 0.2060 \| \| 0.3705 \| 76.0 \| 532 \| 1.5923 \| 0.575 \| 0.5742 \| 2.3376 \| 0.575 \| 0.5699 \| 0.1779 \| 0.2048 \| \| 0.3705 \| 77.0 \| 539 \| 1.5891 \| 0.565 \| 0.5760 \| 2.2978 \| 0.565 \| 0.5616 \| 0.1691 \| 0.2066 \| \| 0.3705 \| 78.0 \| 546 \| 1.5896 \| 0.575 \| 0.5738 \| 2.3748 \| 0.575 \| 0.5703 \| 0.1733 \| 0.2048 \| \| 0.3705 \| 79.0 \| 553 \| 1.5901 \| 0.5675 \| 0.5757 \| 2.3039 \| 0.5675 \| 0.5634 \| 0.1710 \| 0.2064 \| \| 0.3705 \| 80.0 \| 560 \| 1.5906 \| 0.57 \| 0.5746 \| 2.3125 \| 0.57 \| 0.5657 \| 0.1692 \| 0.2054 \| \| 0.3705 \| 81.0 \| 567 \| 1.5907 \| 0.57 \| 0.5751 \| 2.3097 \| 0.57 \| 0.5659 \| 0.1600 \| 0.2047 \| \| 0.3705 \| 82.0 \| 574 \| 1.5902 \| 0.57 \| 0.5746 \| 2.3072 \| 0.57 \| 0.5657 \| 0.1797 \| 0.2055 \| \| 0.3705 \| 83.0 \| 581 \| 1.5906 \| 0.5725 \| 0.5746 \| 2.3145 \| 0.5725 \| 0.5681 \| 0.1547 \| 0.2050 \| \| 0.3705 \| 84.0 \| 588 \| 1.5909 \| 0.5725 \| 0.5750 \| 2.3057 \| 0.5725 \| 0.5684 \| 0.1746 \| 0.2055 \| \| 0.3705 \| 85.0 \| 595 \| 1.5906 \| 0.57 \| 0.5746 \| 2.3098 \| 0.57 \| 0.5661 \| 0.1721 \| 0.2054 \| \| 0.3705 \| 86.0 \| 602 \| 1.5916 \| 0.57 \| 0.5749 \| 2.3093 \| 0.57 \| 0.5661 \| 0.1659 \| 0.2058 \| \| 0.3705 \| 87.0 \| 609 \| 1.5913 \| 0.57 \| 0.5748 \| 2.3084 \| 0.57 \| 0.5661 \| 0.1631 \| 0.2058 \| \| 0.3705 \| 88.0 \| 616 \| 1.5918 \| 0.57 \| 0.5749 \| 2.3082 \| 0.57 \| 0.5661 \| 0.1652 \| 0.2058 \| \| 0.3705 \| 89.0 \| 623 \| 1.5919 \| 0.57 \| 0.5750 \| 2.3084 \| 0.57 \| 0.5661 \| 0.1658 \| 0.2059 \| \| 0.3705 \| 90.0 \| 630 \| 1.5918 \| 0.5725 \| 0.5749 \| 2.3087 \| 0.5725 \| 0.5685 \| 0.1650 \| 0.2056 \| \| 0.3705 \| 91.0 \| 637 \| 1.5921 \| 0.57 \| 0.5750 \| 2.3076 \| 0.57 \| 0.5661 \| 0.1549 \| 0.2059 \| \| 0.3705 \| 92.0 \| 644 \| 1.5920 \| 0.57 \| 0.5750 \| 2.3079 \| 0.57 \| 0.5661 \| 0.1581 \| 0.2058 \| \| 0.3705 \| 93.0 \| 651 \| 1.5917 \| 0.57 \| 0.5749 \| 2.3080 \| 0.57 \| 0.5661 \| 0.1680 \| 0.2057 \| \| 0.3705 \| 94.0 \| 658 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3083 \| 0.57 \| 0.5661 \| 0.1643 \| 0.2058 \| \| 0.3705 \| 95.0 \| 665 \| 1.5924 \| 0.57 \| 0.5751 \| 2.3085 \| 0.57 \| 0.5661 \| 0.1543 \| 0.2059 \| \| 0.3705 \| 96.0 \| 672 \| 1.5922 \| 0.57 \| 0.5750 \| 2.3085 \| 0.57 \| 0.5661 \| 0.1530 \| 0.2058 \| \| 0.3705 \| 97.0 \| 679 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1688 \| 0.2058 \| \| 0.3705 \| 98.0 \| 686 \| 1.5923 \| 0.57 \| 0.5749 \| 2.3089 \| 0.57 \| 0.5661 \| 0.1733 \| 0.2058 \| \| 0.3705 \| 99.0 \| 693 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1735 \| 0.2058 \| \| 0.3705 \| 100.0 \| 700 \| 1.5923 \| 0.57 \| 0.5750 \| 2.3088 \| 0.57 \| 0.5661 \| 0.1722 \| 0.2058 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5908 - Accuracy: 0.54 - Brier Loss: 0.6121 - Nll: 2.4999 - F1 Micro: 0.54 - F1 Macro: 0.5334 - Ece: 0.1895 - Aurc: 0.2228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 3.9334 \| 0.0425 \| 1.0719 \| 7.3494 \| 0.0425 \| 0.0341 \| 0.2781 \| 0.9557 \| \| No log \| 2.0 \| 14 \| 3.0206 \| 0.09 \| 0.9526 \| 5.9271 \| 0.09 \| 0.0682 \| 0.1659 \| 0.8896 \| \| No log \| 3.0 \| 21 \| 2.6955 \| 0.2275 \| 0.8867 \| 5.4041 \| 0.2275 \| 0.1804 \| 0.1637 \| 0.6351 \| \| No log \| 4.0 \| 28 \| 2.3029 \| 0.29 \| 0.8005 \| 3.5699 \| 0.29 \| 0.2727 \| 0.1512 \| 0.4877 \| \| No log \| 5.0 \| 35 \| 2.0293 \| 0.37 \| 0.7219 \| 2.9856 \| 0.37 \| 0.3519 \| 0.1495 \| 0.3632 \| \| No log \| 6.0 \| 42 \| 1.9194 \| 0.44 \| 0.6926 \| 2.9746 \| 0.44 \| 0.4240 \| 0.1778 \| 0.3091 \| \| No log \| 7.0 \| 49 \| 1.9640 \| 0.4675 \| 0.7000 \| 3.0857 \| 0.4675 \| 0.4390 \| 0.2009 \| 0.3004 \| \| No log \| 8.0 \| 56 \| 1.9049 \| 0.4625 \| 0.7003 \| 2.8472 \| 0.4625 \| 0.4602 \| 0.2053 \| 0.3051 \| \| No log \| 9.0 \| 63 \| 2.0561 \| 0.4675 \| 0.7168 \| 2.8229 \| 0.4675 \| 0.4501 \| 0.2288 \| 0.2994 \| \| No log \| 10.0 \| 70 \| 2.1002 \| 0.45 \| 0.7433 \| 2.7915 \| 0.45 \| 0.4234 \| 0.2691 \| 0.3022 \| \| No log \| 11.0 \| 77 \| 2.2528 \| 0.4525 \| 0.7686 \| 3.0103 \| 0.4525 \| 0.4320 \| 0.2921 \| 0.3183 \| \| No log \| 12.0 \| 84 \| 2.1190 \| 0.475 \| 0.7427 \| 2.6715 \| 0.4750 \| 0.4660 \| 0.2832 \| 0.3077 \| \| No log \| 13.0 \| 91 \| 2.3102 \| 0.445 \| 0.7825 \| 2.9698 \| 0.445 \| 0.4252 \| 0.3093 \| 0.3100 \| \| No log \| 14.0 \| 98 \| 2.3501 \| 0.42 \| 0.8145 \| 2.7585 \| 0.4200 \| 0.4248 \| 0.3206 \| 0.3662 \| \| No log \| 15.0 \| 105 \| 2.2402 \| 0.495 \| 0.7423 \| 3.0313 \| 0.495 \| 0.4702 \| 0.2692 \| 0.2818 \| \| No log \| 16.0 \| 112 \| 2.2266 \| 0.49 \| 0.7349 \| 2.8824 \| 0.49 \| 0.4714 \| 0.2763 \| 0.2895 \| \| No log \| 17.0 \| 119 \| 2.2989 \| 0.4725 \| 0.7509 \| 3.0951 \| 0.4725 \| 0.4499 \| 0.2863 \| 0.2855 \| \| No log \| 18.0 \| 126 \| 2.1355 \| 0.47 \| 0.7322 \| 2.9349 \| 0.47 \| 0.4616 \| 0.2725 \| 0.2845 \| \| No log \| 19.0 \| 133 \| 2.0965 \| 0.505 \| 0.7067 \| 2.8254 \| 0.505 \| 0.4956 \| 0.2523 \| 0.2757 \| \| No log \| 20.0 \| 140 \| 2.1961 \| 0.485 \| 0.7358 \| 3.1604 \| 0.485 \| 0.4567 \| 0.2825 \| 0.2841 \| \| No log \| 21.0 \| 147 \| 2.1287 \| 0.5025 \| 0.7247 \| 2.5998 \| 0.5025 \| 0.5074 \| 0.2703 \| 0.3064 \| \| No log \| 22.0 \| 154 \| 2.2280 \| 0.4675 \| 0.7760 \| 2.8571 \| 0.4675 \| 0.4636 \| 0.2911 \| 0.3232 \| \| No log \| 23.0 \| 161 \| 1.9649 \| 0.5025 \| 0.6828 \| 2.8224 \| 0.5025 \| 0.4970 \| 0.2410 \| 0.2633 \| \| No log \| 24.0 \| 168 \| 1.9361 \| 0.5125 \| 0.6780 \| 2.7309 \| 0.5125 \| 0.5035 \| 0.2326 \| 0.2553 \| \| No log \| 25.0 \| 175 \| 2.0161 \| 0.5 \| 0.6980 \| 2.9958 \| 0.5 \| 0.4912 \| 0.2580 \| 0.2556 \| \| No log \| 26.0 \| 182 \| 1.8763 \| 0.5025 \| 0.6624 \| 2.8291 \| 0.5025 \| 0.4952 \| 0.2305 \| 0.2431 \| \| No log \| 27.0 \| 189 \| 1.9057 \| 0.525 \| 0.6793 \| 2.5627 \| 0.525 \| 0.5174 \| 0.2161 \| 0.2634 \| \| No log \| 28.0 \| 196 \| 1.8529 \| 0.52 \| 0.6683 \| 2.7191 \| 0.52 \| 0.5132 \| 0.2375 \| 0.2535 \| \| No log \| 29.0 \| 203 \| 1.9603 \| 0.5125 \| 0.6831 \| 2.7822 \| 0.5125 \| 0.5076 \| 0.2395 \| 0.2657 \| \| No log \| 30.0 \| 210 \| 1.8247 \| 0.52 \| 0.6533 \| 2.8547 \| 0.52 \| 0.5058 \| 0.2080 \| 0.2426 \| \| No log \| 31.0 \| 217 \| 1.8275 \| 0.5125 \| 0.6547 \| 2.6194 \| 0.5125 \| 0.5032 \| 0.2208 \| 0.2488 \| \| No log \| 32.0 \| 224 \| 1.8003 \| 0.52 \| 0.6455 \| 2.6138 \| 0.52 \| 0.5124 \| 0.2302 \| 0.2370 \| \| No log \| 33.0 \| 231 \| 1.8714 \| 0.505 \| 0.6694 \| 2.6643 \| 0.505 \| 0.4970 \| 0.2195 \| 0.2553 \| \| No log \| 34.0 \| 238 \| 1.8018 \| 0.5075 \| 0.6659 \| 2.5423 \| 0.5075 \| 0.4978 \| 0.2241 \| 0.2515 \| \| No log \| 35.0 \| 245 \| 1.7844 \| 0.5225 \| 0.6503 \| 2.6100 \| 0.5225 \| 0.5181 \| 0.2181 \| 0.2435 \| \| No log \| 36.0 \| 252 \| 1.8321 \| 0.5225 \| 0.6674 \| 2.7821 \| 0.5225 \| 0.5020 \| 0.2285 \| 0.2462 \| \| No log \| 37.0 \| 259 \| 1.7859 \| 0.4975 \| 0.6725 \| 2.6066 \| 0.4975 \| 0.4974 \| 0.2351 \| 0.2627 \| \| No log \| 38.0 \| 266 \| 1.7790 \| 0.5125 \| 0.6595 \| 2.6983 \| 0.5125 \| 0.5023 \| 0.2172 \| 0.2497 \| \| No log \| 39.0 \| 273 \| 1.6989 \| 0.5225 \| 0.6401 \| 2.6743 \| 0.5225 \| 0.5151 \| 0.2100 \| 0.2407 \| \| No log \| 40.0 \| 280 \| 1.7568 \| 0.52 \| 0.6488 \| 2.5294 \| 0.52 \| 0.5132 \| 0.2208 \| 0.2442 \| \| No log \| 41.0 \| 287 \| 1.6896 \| 0.5275 \| 0.6362 \| 2.5489 \| 0.5275 \| 0.5141 \| 0.2045 \| 0.2323 \| \| No log \| 42.0 \| 294 \| 1.7193 \| 0.5275 \| 0.6517 \| 2.5525 \| 0.5275 \| 0.5232 \| 0.1986 \| 0.2467 \| \| No log \| 43.0 \| 301 \| 1.7199 \| 0.535 \| 0.6403 \| 2.5974 \| 0.535 \| 0.5279 \| 0.2104 \| 0.2432 \| \| No log \| 44.0 \| 308 \| 1.6594 \| 0.5375 \| 0.6330 \| 2.4854 \| 0.5375 \| 0.5316 \| 0.2015 \| 0.2321 \| \| No log \| 45.0 \| 315 \| 1.6543 \| 0.5275 \| 0.6239 \| 2.4955 \| 0.5275 \| 0.5223 \| 0.2144 \| 0.2308 \| \| No log \| 46.0 \| 322 \| 1.6490 \| 0.5425 \| 0.6262 \| 2.5215 \| 0.5425 \| 0.5358 \| 0.2104 \| 0.2273 \| \| No log \| 47.0 \| 329 \| 1.6570 \| 0.54 \| 0.6233 \| 2.5454 \| 0.54 \| 0.5380 \| 0.2047 \| 0.2301 \| \| No log \| 48.0 \| 336 \| 1.6359 \| 0.5375 \| 0.6218 \| 2.5546 \| 0.5375 \| 0.5320 \| 0.2171 \| 0.2257 \| \| No log \| 49.0 \| 343 \| 1.6320 \| 0.55 \| 0.6214 \| 2.4958 \| 0.55 \| 0.5452 \| 0.2014 \| 0.2267 \| \| No log \| 50.0 \| 350 \| 1.6230 \| 0.53 \| 0.6208 \| 2.4979 \| 0.53 \| 0.5243 \| 0.2017 \| 0.2315 \| \| No log \| 51.0 \| 357 \| 1.6374 \| 0.535 \| 0.6257 \| 2.4644 \| 0.535 \| 0.5293 \| 0.2038 \| 0.2286 \| \| No log \| 52.0 \| 364 \| 1.6190 \| 0.5375 \| 0.6199 \| 2.5279 \| 0.5375 \| 0.5310 \| 0.1855 \| 0.2290 \| \| No log \| 53.0 \| 371 \| 1.6155 \| 0.5475 \| 0.6158 \| 2.4738 \| 0.5475 \| 0.5435 \| 0.1913 \| 0.2239 \| \| No log \| 54.0 \| 378 \| 1.6131 \| 0.5425 \| 0.6184 \| 2.4982 \| 0.5425 \| 0.5377 \| 0.1969 \| 0.2248 \| \| No log \| 55.0 \| 385 \| 1.6035 \| 0.545 \| 0.6138 \| 2.4690 \| 0.545 \| 0.5406 \| 0.2164 \| 0.2223 \| \| No log \| 56.0 \| 392 \| 1.5990 \| 0.54 \| 0.6153 \| 2.4701 \| 0.54 \| 0.5356 \| 0.2019 \| 0.2249 \| \| No log \| 57.0 \| 399 \| 1.6024 \| 0.5425 \| 0.6153 \| 2.4626 \| 0.5425 \| 0.5375 \| 0.1826 \| 0.2237 \| \| No log \| 58.0 \| 406 \| 1.5935 \| 0.545 \| 0.6141 \| 2.4390 \| 0.545 \| 0.5415 \| 0.1933 \| 0.2238 \| \| No log \| 59.0 \| 413 \| 1.6016 \| 0.545 \| 0.6137 \| 2.4640 \| 0.545 \| 0.5401 \| 0.2021 \| 0.2230 \| \| No log \| 60.0 \| 420 \| 1.5976 \| 0.54 \| 0.6146 \| 2.4618 \| 0.54 \| 0.5355 \| 0.1912 \| 0.2245 \| \| No log \| 61.0 \| 427 \| 1.5984 \| 0.545 \| 0.6133 \| 2.4683 \| 0.545 \| 0.5408 \| 0.1971 \| 0.2228 \| \| No log \| 62.0 \| 434 \| 1.5941 \| 0.54 \| 0.6131 \| 2.4639 \| 0.54 \| 0.5358 \| 0.1898 \| 0.2236 \| \| No log \| 63.0 \| 441 \| 1.5936 \| 0.545 \| 0.6123 \| 2.4689 \| 0.545 \| 0.5404 \| 0.1953 \| 0.2222 \| \| No log \| 64.0 \| 448 \| 1.5970 \| 0.5425 \| 0.6138 \| 2.4647 \| 0.5425 \| 0.5384 \| 0.2015 \| 0.2238 \| \| No log \| 65.0 \| 455 \| 1.5943 \| 0.545 \| 0.6130 \| 2.4963 \| 0.545 \| 0.5400 \| 0.1979 \| 0.2229 \| \| No log \| 66.0 \| 462 \| 1.5936 \| 0.545 \| 0.6127 \| 2.4977 \| 0.545 \| 0.5400 \| 0.1933 \| 0.2229 \| \| No log \| 67.0 \| 469 \| 1.5928 \| 0.5425 \| 0.6127 \| 2.4965 \| 0.5425 \| 0.5381 \| 0.1976 \| 0.2233 \| \| No log \| 68.0 \| 476 \| 1.5946 \| 0.5425 \| 0.6128 \| 2.4768 \| 0.5425 \| 0.5383 \| 0.2149 \| 0.2233 \| \| No log \| 69.0 \| 483 \| 1.5924 \| 0.54 \| 0.6126 \| 2.4946 \| 0.54 \| 0.5356 \| 0.2094 \| 0.2233 \| \| No log \| 70.0 \| 490 \| 1.5921 \| 0.54 \| 0.6120 \| 2.4964 \| 0.54 \| 0.5356 \| 0.1801 \| 0.2230 \| \| No log \| 71.0 \| 497 \| 1.5926 \| 0.54 \| 0.6126 \| 2.4955 \| 0.54 \| 0.5356 \| 0.2039 \| 0.2235 \| \| 0.3138 \| 72.0 \| 504 \| 1.5916 \| 0.5425 \| 0.6121 \| 2.4964 \| 0.5425 \| 0.5366 \| 0.1898 \| 0.2229 \| \| 0.3138 \| 73.0 \| 511 \| 1.5917 \| 0.54 \| 0.6119 \| 2.4966 \| 0.54 \| 0.5356 \| 0.2039 \| 0.2231 \| \| 0.3138 \| 74.0 \| 518 \| 1.5918 \| 0.54 \| 0.6123 \| 2.4964 \| 0.54 \| 0.5351 \| 0.2035 \| 0.2229 \| \| 0.3138 \| 75.0 \| 525 \| 1.5912 \| 0.54 \| 0.6118 \| 2.4975 \| 0.54 \| 0.5351 \| 0.2059 \| 0.2228 \| \| 0.3138 \| 76.0 \| 532 \| 1.5918 \| 0.54 \| 0.6124 \| 2.4965 \| 0.54 \| 0.5351 \| 0.1971 \| 0.2231 \| \| 0.3138 \| 77.0 \| 539 \| 1.5919 \| 0.5425 \| 0.6120 \| 2.4974 \| 0.5425 \| 0.5358 \| 0.2087 \| 0.2227 \| \| 0.3138 \| 78.0 \| 546 \| 1.5903 \| 0.54 \| 0.6118 \| 2.4978 \| 0.54 \| 0.5341 \| 0.2169 \| 0.2228 \| \| 0.3138 \| 79.0 \| 553 \| 1.5922 \| 0.54 \| 0.6124 \| 2.4976 \| 0.54 \| 0.5351 \| 0.2109 \| 0.2234 \| \| 0.3138 \| 80.0 \| 560 \| 1.5914 \| 0.54 \| 0.6122 \| 2.4983 \| 0.54 \| 0.5345 \| 0.2041 \| 0.2228 \| \| 0.3138 \| 81.0 \| 567 \| 1.5907 \| 0.54 \| 0.6119 \| 2.4981 \| 0.54 \| 0.5345 \| 0.2128 \| 0.2226 \| \| 0.3138 \| 82.0 \| 574 \| 1.5921 \| 0.5425 \| 0.6124 \| 2.4986 \| 0.5425 \| 0.5362 \| 0.2084 \| 0.2227 \| \| 0.3138 \| 83.0 \| 581 \| 1.5918 \| 0.5425 \| 0.6125 \| 2.4987 \| 0.5425 \| 0.5362 \| 0.2038 \| 0.2230 \| \| 0.3138 \| 84.0 \| 588 \| 1.5902 \| 0.54 \| 0.6120 \| 2.4989 \| 0.54 \| 0.5345 \| 0.2043 \| 0.2226 \| \| 0.3138 \| 85.0 \| 595 \| 1.5919 \| 0.5425 \| 0.6124 \| 2.4988 \| 0.5425 \| 0.5360 \| 0.1998 \| 0.2228 \| \| 0.3138 \| 86.0 \| 602 \| 1.5916 \| 0.5425 \| 0.6124 \| 2.4990 \| 0.5425 \| 0.5362 \| 0.2079 \| 0.2227 \| \| 0.3138 \| 87.0 \| 609 \| 1.5906 \| 0.54 \| 0.6120 \| 2.4990 \| 0.54 \| 0.5345 \| 0.2037 \| 0.2227 \| \| 0.3138 \| 88.0 \| 616 \| 1.5908 \| 0.54 \| 0.6120 \| 2.4989 \| 0.54 \| 0.5345 \| 0.2091 \| 0.2230 \| \| 0.3138 \| 89.0 \| 623 \| 1.5909 \| 0.54 \| 0.6120 \| 2.4995 \| 0.54 \| 0.5344 \| 0.2113 \| 0.2228 \| \| 0.3138 \| 90.0 \| 630 \| 1.5906 \| 0.54 \| 0.6119 \| 2.4996 \| 0.54 \| 0.5345 \| 0.1969 \| 0.2228 \| \| 0.3138 \| 91.0 \| 637 \| 1.5911 \| 0.5425 \| 0.6121 \| 2.4999 \| 0.5425 \| 0.5360 \| 0.1954 \| 0.2226 \| \| 0.3138 \| 92.0 \| 644 \| 1.5909 \| 0.54 \| 0.6121 \| 2.4994 \| 0.54 \| 0.5344 \| 0.1928 \| 0.2228 \| \| 0.3138 \| 93.0 \| 651 \| 1.5907 \| 0.5425 \| 0.6121 \| 2.4999 \| 0.5425 \| 0.5360 \| 0.2034 \| 0.2225 \| \| 0.3138 \| 94.0 \| 658 \| 1.5910 \| 0.5425 \| 0.6122 \| 2.4996 \| 0.5425 \| 0.5360 \| 0.1974 \| 0.2227 \| \| 0.3138 \| 95.0 \| 665 \| 1.5909 \| 0.5375 \| 0.6121 \| 2.4995 \| 0.5375 \| 0.5319 \| 0.1990 \| 0.2230 \| \| 0.3138 \| 96.0 \| 672 \| 1.5907 \| 0.5375 \| 0.6120 \| 2.4997 \| 0.5375 \| 0.5318 \| 0.1980 \| 0.2229 \| \| 0.3138 \| 97.0 \| 679 \| 1.5907 \| 0.54 \| 0.6120 \| 2.4998 \| 0.54 \| 0.5344 \| 0.1900 \| 0.2228 \| \| 0.3138 \| 98.0 \| 686 \| 1.5907 \| 0.5425 \| 0.6120 \| 2.4999 \| 0.5425 \| 0.5362 \| 0.1899 \| 0.2226 \| \| 0.3138 \| 99.0 \| 693 \| 1.5908 \| 0.54 \| 0.6121 \| 2.4999 \| 0.54 \| 0.5334 \| 0.1936 \| 0.2228 \| \| 0.3138 \| 100.0 \| 700 \| 1.5908 \| 0.54 \| 0.6121 \| 2.4999 \| 0.54 \| 0.5334 \| 0.1895 \| 0.2228 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
ayanban011/vit-base_tobacco	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7324 - Accuracy: 0.8 - Brier Loss: 0.3049 - Nll: 1.3070 - F1 Micro: 0.8000 - F1 Macro: 0.7733 - Ece: 0.2124 - Aurc: 0.0840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 40 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 640 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.8 \| 1 \| 0.7434 \| 0.815 \| 0.3073 \| 1.1863 \| 0.815 \| 0.7942 \| 0.2217 \| 0.0720 \| \| No log \| 1.6 \| 2 \| 0.7569 \| 0.81 \| 0.3117 \| 1.2131 \| 0.81 \| 0.7893 \| 0.2153 \| 0.0800 \| \| No log \| 2.4 \| 3 \| 0.7491 \| 0.82 \| 0.3107 \| 1.2631 \| 0.82 \| 0.8063 \| 0.2311 \| 0.0777 \| \| No log \| 4.0 \| 5 \| 0.7489 \| 0.795 \| 0.3088 \| 1.1544 \| 0.795 \| 0.7766 \| 0.2427 \| 0.0730 \| \| No log \| 4.8 \| 6 \| 0.7658 \| 0.81 \| 0.3171 \| 1.3766 \| 0.81 \| 0.7983 \| 0.2434 \| 0.0822 \| \| No log \| 5.6 \| 7 \| 0.7496 \| 0.815 \| 0.3097 \| 1.1920 \| 0.815 \| 0.8014 \| 0.2434 \| 0.0848 \| \| No log \| 6.4 \| 8 \| 0.7468 \| 0.8 \| 0.3090 \| 1.0732 \| 0.8000 \| 0.7750 \| 0.2195 \| 0.0774 \| \| No log \| 8.0 \| 10 \| 0.7563 \| 0.815 \| 0.3131 \| 1.3472 \| 0.815 \| 0.8082 \| 0.2255 \| 0.0741 \| \| No log \| 8.8 \| 11 \| 0.7548 \| 0.81 \| 0.3116 \| 1.2016 \| 0.81 \| 0.7930 \| 0.2496 \| 0.0868 \| \| No log \| 9.6 \| 12 \| 0.7395 \| 0.805 \| 0.3071 \| 1.1664 \| 0.805 \| 0.7841 \| 0.2432 \| 0.0772 \| \| No log \| 10.4 \| 13 \| 0.7296 \| 0.82 \| 0.3018 \| 1.1776 \| 0.82 \| 0.8078 \| 0.2214 \| 0.0676 \| \| No log \| 12.0 \| 15 \| 0.7515 \| 0.815 \| 0.3104 \| 1.2034 \| 0.815 \| 0.7987 \| 0.2307 \| 0.0835 \| \| No log \| 12.8 \| 16 \| 0.7350 \| 0.81 \| 0.3053 \| 1.1762 \| 0.81 \| 0.7978 \| 0.2196 \| 0.0747 \| \| No log \| 13.6 \| 17 \| 0.7281 \| 0.805 \| 0.3023 \| 1.1664 \| 0.805 \| 0.7841 \| 0.2144 \| 0.0718 \| \| No log \| 14.4 \| 18 \| 0.7395 \| 0.81 \| 0.3064 \| 1.1750 \| 0.81 \| 0.7871 \| 0.2306 \| 0.0778 \| \| No log \| 16.0 \| 20 \| 0.7427 \| 0.81 \| 0.3076 \| 1.2637 \| 0.81 \| 0.7986 \| 0.2194 \| 0.0808 \| \| No log \| 16.8 \| 21 \| 0.7337 \| 0.81 \| 0.3044 \| 1.2447 \| 0.81 \| 0.7948 \| 0.2321 \| 0.0743 \| \| No log \| 17.6 \| 22 \| 0.7340 \| 0.805 \| 0.3050 \| 1.1681 \| 0.805 \| 0.7841 \| 0.2307 \| 0.0743 \| \| No log \| 18.4 \| 23 \| 0.7338 \| 0.805 \| 0.3047 \| 1.1708 \| 0.805 \| 0.7841 \| 0.2290 \| 0.0759 \| \| No log \| 20.0 \| 25 \| 0.7390 \| 0.815 \| 0.3058 \| 1.2551 \| 0.815 \| 0.7984 \| 0.2489 \| 0.0818 \| \| No log \| 20.8 \| 26 \| 0.7390 \| 0.815 \| 0.3063 \| 1.1894 \| 0.815 \| 0.7984 \| 0.2294 \| 0.0818 \| \| No log \| 21.6 \| 27 \| 0.7349 \| 0.805 \| 0.3054 \| 1.1714 \| 0.805 \| 0.7847 \| 0.2011 \| 0.0791 \| \| No log \| 22.4 \| 28 \| 0.7308 \| 0.81 \| 0.3037 \| 1.1694 \| 0.81 \| 0.7948 \| 0.2128 \| 0.0766 \| \| No log \| 24.0 \| 30 \| 0.7353 \| 0.81 \| 0.3051 \| 1.1852 \| 0.81 \| 0.7956 \| 0.2282 \| 0.0794 \| \| No log \| 24.8 \| 31 \| 0.7378 \| 0.81 \| 0.3062 \| 1.1870 \| 0.81 \| 0.7956 \| 0.2293 \| 0.0819 \| \| No log \| 25.6 \| 32 \| 0.7356 \| 0.81 \| 0.3054 \| 1.1863 \| 0.81 \| 0.7956 \| 0.2287 \| 0.0817 \| \| No log \| 26.4 \| 33 \| 0.7309 \| 0.81 \| 0.3037 \| 1.1801 \| 0.81 \| 0.7954 \| 0.2209 \| 0.0795 \| \| No log \| 28.0 \| 35 \| 0.7336 \| 0.805 \| 0.3050 \| 1.1733 \| 0.805 \| 0.7850 \| 0.2082 \| 0.0789 \| \| No log \| 28.8 \| 36 \| 0.7334 \| 0.81 \| 0.3045 \| 1.1799 \| 0.81 \| 0.7956 \| 0.2207 \| 0.0797 \| \| No log \| 29.6 \| 37 \| 0.7320 \| 0.81 \| 0.3040 \| 1.2447 \| 0.81 \| 0.7956 \| 0.2279 \| 0.0804 \| \| No log \| 30.4 \| 38 \| 0.7328 \| 0.81 \| 0.3045 \| 1.2473 \| 0.81 \| 0.7956 \| 0.2154 \| 0.0812 \| \| No log \| 32.0 \| 40 \| 0.7322 \| 0.805 \| 0.3044 \| 1.1796 \| 0.805 \| 0.7850 \| 0.2384 \| 0.0804 \| \| No log \| 32.8 \| 41 \| 0.7318 \| 0.81 \| 0.3045 \| 1.1792 \| 0.81 \| 0.7954 \| 0.2291 \| 0.0794 \| \| No log \| 33.6 \| 42 \| 0.7302 \| 0.81 \| 0.3034 \| 1.2401 \| 0.81 \| 0.7954 \| 0.2086 \| 0.0794 \| \| No log \| 34.4 \| 43 \| 0.7311 \| 0.805 \| 0.3036 \| 1.2424 \| 0.805 \| 0.7850 \| 0.2278 \| 0.0804 \| \| No log \| 36.0 \| 45 \| 0.7323 \| 0.805 \| 0.3043 \| 1.1902 \| 0.805 \| 0.7850 \| 0.2119 \| 0.0816 \| \| No log \| 36.8 \| 46 \| 0.7304 \| 0.805 \| 0.3034 \| 1.2428 \| 0.805 \| 0.7850 \| 0.2330 \| 0.0807 \| \| No log \| 37.6 \| 47 \| 0.7297 \| 0.805 \| 0.3032 \| 1.2413 \| 0.805 \| 0.7850 \| 0.2447 \| 0.0801 \| \| No log \| 38.4 \| 48 \| 0.7310 \| 0.805 \| 0.3039 \| 1.2424 \| 0.805 \| 0.7850 \| 0.2233 \| 0.0802 \| \| No log \| 40.0 \| 50 \| 0.7316 \| 0.805 \| 0.3040 \| 1.2451 \| 0.805 \| 0.7850 \| 0.2094 \| 0.0809 \| \| No log \| 40.8 \| 51 \| 0.7313 \| 0.805 \| 0.3041 \| 1.2450 \| 0.805 \| 0.7850 \| 0.2093 \| 0.0810 \| \| No log \| 41.6 \| 52 \| 0.7313 \| 0.805 \| 0.3041 \| 1.2445 \| 0.805 \| 0.7850 \| 0.2073 \| 0.0814 \| \| No log \| 42.4 \| 53 \| 0.7315 \| 0.805 \| 0.3040 \| 1.2447 \| 0.805 \| 0.7850 \| 0.2198 \| 0.0821 \| \| No log \| 44.0 \| 55 \| 0.7303 \| 0.805 \| 0.3034 \| 1.2441 \| 0.805 \| 0.7850 \| 0.2048 \| 0.0813 \| \| No log \| 44.8 \| 56 \| 0.7306 \| 0.805 \| 0.3038 \| 1.2444 \| 0.805 \| 0.7850 \| 0.1966 \| 0.0809 \| \| No log \| 45.6 \| 57 \| 0.7317 \| 0.805 \| 0.3043 \| 1.2449 \| 0.805 \| 0.7850 \| 0.1976 \| 0.0821 \| \| No log \| 46.4 \| 58 \| 0.7317 \| 0.805 \| 0.3041 \| 1.2466 \| 0.805 \| 0.7850 \| 0.2007 \| 0.0822 \| \| No log \| 48.0 \| 60 \| 0.7316 \| 0.805 \| 0.3041 \| 1.2499 \| 0.805 \| 0.7850 \| 0.2137 \| 0.0820 \| \| No log \| 48.8 \| 61 \| 0.7320 \| 0.8 \| 0.3043 \| 1.2536 \| 0.8000 \| 0.7733 \| 0.2081 \| 0.0822 \| \| No log \| 49.6 \| 62 \| 0.7319 \| 0.805 \| 0.3044 \| 1.2494 \| 0.805 \| 0.7850 \| 0.1998 \| 0.0825 \| \| No log \| 50.4 \| 63 \| 0.7326 \| 0.805 \| 0.3048 \| 1.2476 \| 0.805 \| 0.7850 \| 0.1936 \| 0.0828 \| \| No log \| 52.0 \| 65 \| 0.7313 \| 0.8 \| 0.3044 \| 1.2495 \| 0.8000 \| 0.7733 \| 0.2117 \| 0.0822 \| \| No log \| 52.8 \| 66 \| 0.7304 \| 0.8 \| 0.3039 \| 1.2524 \| 0.8000 \| 0.7733 \| 0.2009 \| 0.0818 \| \| No log \| 53.6 \| 67 \| 0.7306 \| 0.8 \| 0.3038 \| 1.2505 \| 0.8000 \| 0.7733 \| 0.2182 \| 0.0818 \| \| No log \| 54.4 \| 68 \| 0.7321 \| 0.8 \| 0.3044 \| 1.2513 \| 0.8000 \| 0.7733 \| 0.2185 \| 0.0833 \| \| No log \| 56.0 \| 70 \| 0.7326 \| 0.8 \| 0.3049 \| 1.2519 \| 0.8000 \| 0.7733 \| 0.2014 \| 0.0833 \| \| No log \| 56.8 \| 71 \| 0.7320 \| 0.8 \| 0.3047 \| 1.2580 \| 0.8000 \| 0.7733 \| 0.2175 \| 0.0829 \| \| No log \| 57.6 \| 72 \| 0.7313 \| 0.8 \| 0.3043 \| 1.2571 \| 0.8000 \| 0.7733 \| 0.2045 \| 0.0828 \| \| No log \| 58.4 \| 73 \| 0.7314 \| 0.8 \| 0.3043 \| 1.3065 \| 0.8000 \| 0.7733 \| 0.2038 \| 0.0827 \| \| No log \| 60.0 \| 75 \| 0.7322 \| 0.8 \| 0.3046 \| 1.3081 \| 0.8000 \| 0.7733 \| 0.2047 \| 0.0840 \| \| No log \| 60.8 \| 76 \| 0.7323 \| 0.8 \| 0.3047 \| 1.3078 \| 0.8000 \| 0.7733 \| 0.2053 \| 0.0839 \| \| No log \| 61.6 \| 77 \| 0.7322 \| 0.8 \| 0.3047 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2051 \| 0.0837 \| \| No log \| 62.4 \| 78 \| 0.7316 \| 0.8 \| 0.3045 \| 1.3062 \| 0.8000 \| 0.7733 \| 0.2145 \| 0.0835 \| \| No log \| 64.0 \| 80 \| 0.7315 \| 0.8 \| 0.3044 \| 1.3063 \| 0.8000 \| 0.7733 \| 0.2067 \| 0.0836 \| \| No log \| 64.8 \| 81 \| 0.7320 \| 0.8 \| 0.3047 \| 1.3064 \| 0.8000 \| 0.7733 \| 0.2041 \| 0.0839 \| \| No log \| 65.6 \| 82 \| 0.7323 \| 0.8 \| 0.3048 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2046 \| 0.0839 \| \| No log \| 66.4 \| 83 \| 0.7323 \| 0.8 \| 0.3048 \| 1.3068 \| 0.8000 \| 0.7733 \| 0.2045 \| 0.0838 \| \| No log \| 68.0 \| 85 \| 0.7320 \| 0.8 \| 0.3046 \| 1.3068 \| 0.8000 \| 0.7733 \| 0.2046 \| 0.0840 \| \| No log \| 68.8 \| 86 \| 0.7318 \| 0.8 \| 0.3045 \| 1.3069 \| 0.8000 \| 0.7733 \| 0.2114 \| 0.0838 \| \| No log \| 69.6 \| 87 \| 0.7316 \| 0.8 \| 0.3045 \| 1.3066 \| 0.8000 \| 0.7733 \| 0.2149 \| 0.0836 \| \| No log \| 70.4 \| 88 \| 0.7316 \| 0.8 \| 0.3045 \| 1.3066 \| 0.8000 \| 0.7733 \| 0.2244 \| 0.0834 \| \| No log \| 72.0 \| 90 \| 0.7321 \| 0.8 \| 0.3047 \| 1.3069 \| 0.8000 \| 0.7733 \| 0.2151 \| 0.0837 \| \| No log \| 72.8 \| 91 \| 0.7322 \| 0.8 \| 0.3048 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2151 \| 0.0839 \| \| No log \| 73.6 \| 92 \| 0.7322 \| 0.8 \| 0.3048 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2155 \| 0.0840 \| \| No log \| 74.4 \| 93 \| 0.7323 \| 0.8 \| 0.3048 \| 1.3071 \| 0.8000 \| 0.7733 \| 0.2129 \| 0.0842 \| \| No log \| 76.0 \| 95 \| 0.7324 \| 0.8 \| 0.3049 \| 1.3071 \| 0.8000 \| 0.7733 \| 0.2084 \| 0.0841 \| \| No log \| 76.8 \| 96 \| 0.7324 \| 0.8 \| 0.3049 \| 1.3071 \| 0.8000 \| 0.7733 \| 0.2141 \| 0.0842 \| \| No log \| 77.6 \| 97 \| 0.7324 \| 0.8 \| 0.3049 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2136 \| 0.0841 \| \| No log \| 78.4 \| 98 \| 0.7324 \| 0.8 \| 0.3049 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2136 \| 0.0841 \| \| No log \| 80.0 \| 100 \| 0.7324 \| 0.8 \| 0.3049 \| 1.3070 \| 0.8000 \| 0.7733 \| 0.2124 \| 0.0840 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9682 - Accuracy: 0.5575 - Brier Loss: 0.5680 - Nll: 2.3526 - F1 Micro: 0.5575 - F1 Macro: 0.5516 - Ece: 0.1676 - Aurc: 0.1973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 5.4578 \| 0.045 \| 1.0710 \| 7.3211 \| 0.045 \| 0.0342 \| 0.2911 \| 0.9529 \| \| No log \| 2.0 \| 14 \| 4.3718 \| 0.0975 \| 0.9466 \| 5.6031 \| 0.0975 \| 0.0893 \| 0.1665 \| 0.8820 \| \| No log \| 3.0 \| 21 \| 3.9389 \| 0.2325 \| 0.8820 \| 5.3266 \| 0.2325 \| 0.1837 \| 0.1656 \| 0.6302 \| \| No log \| 4.0 \| 28 \| 3.3251 \| 0.3075 \| 0.7848 \| 3.6819 \| 0.3075 \| 0.2963 \| 0.1644 \| 0.4646 \| \| No log \| 5.0 \| 35 \| 2.8980 \| 0.4025 \| 0.7180 \| 2.9699 \| 0.4025 \| 0.3740 \| 0.1867 \| 0.3388 \| \| No log \| 6.0 \| 42 \| 2.7055 \| 0.4375 \| 0.7010 \| 3.2343 \| 0.4375 \| 0.4029 \| 0.2096 \| 0.3042 \| \| No log \| 7.0 \| 49 \| 2.6466 \| 0.4575 \| 0.7040 \| 2.9977 \| 0.4575 \| 0.4272 \| 0.2082 \| 0.2999 \| \| No log \| 8.0 \| 56 \| 2.5399 \| 0.4775 \| 0.6809 \| 2.8098 \| 0.4775 \| 0.4693 \| 0.2106 \| 0.2837 \| \| No log \| 9.0 \| 63 \| 2.5949 \| 0.49 \| 0.6824 \| 2.6503 \| 0.49 \| 0.4827 \| 0.2289 \| 0.2718 \| \| No log \| 10.0 \| 70 \| 2.6997 \| 0.4725 \| 0.7318 \| 2.8289 \| 0.4725 \| 0.4523 \| 0.2536 \| 0.3056 \| \| No log \| 11.0 \| 77 \| 2.5694 \| 0.47 \| 0.7040 \| 2.7969 \| 0.47 \| 0.4534 \| 0.2348 \| 0.2766 \| \| No log \| 12.0 \| 84 \| 2.4539 \| 0.4975 \| 0.6762 \| 2.6929 \| 0.4975 \| 0.4831 \| 0.2300 \| 0.2664 \| \| No log \| 13.0 \| 91 \| 2.4841 \| 0.5025 \| 0.6664 \| 2.6140 \| 0.5025 \| 0.4944 \| 0.2098 \| 0.2584 \| \| No log \| 14.0 \| 98 \| 2.2755 \| 0.535 \| 0.6410 \| 2.4991 \| 0.535 \| 0.5249 \| 0.1905 \| 0.2405 \| \| No log \| 15.0 \| 105 \| 2.2998 \| 0.5125 \| 0.6282 \| 2.6561 \| 0.5125 \| 0.4982 \| 0.1916 \| 0.2371 \| \| No log \| 16.0 \| 112 \| 2.2156 \| 0.525 \| 0.6195 \| 2.4837 \| 0.525 \| 0.5191 \| 0.1870 \| 0.2232 \| \| No log \| 17.0 \| 119 \| 2.1862 \| 0.5225 \| 0.6096 \| 2.7252 \| 0.5225 \| 0.5202 \| 0.1747 \| 0.2292 \| \| No log \| 18.0 \| 126 \| 2.2058 \| 0.5375 \| 0.6160 \| 2.5446 \| 0.5375 \| 0.5154 \| 0.2011 \| 0.2231 \| \| No log \| 19.0 \| 133 \| 2.2147 \| 0.5375 \| 0.6143 \| 2.5720 \| 0.5375 \| 0.5232 \| 0.2028 \| 0.2221 \| \| No log \| 20.0 \| 140 \| 2.1791 \| 0.525 \| 0.6191 \| 2.4505 \| 0.525 \| 0.5107 \| 0.1918 \| 0.2233 \| \| No log \| 21.0 \| 147 \| 2.1165 \| 0.535 \| 0.5960 \| 2.5369 \| 0.535 \| 0.5280 \| 0.1867 \| 0.2133 \| \| No log \| 22.0 \| 154 \| 2.1193 \| 0.54 \| 0.6009 \| 2.5568 \| 0.54 \| 0.5313 \| 0.2049 \| 0.2184 \| \| No log \| 23.0 \| 161 \| 2.1082 \| 0.5425 \| 0.5929 \| 2.5238 \| 0.5425 \| 0.5360 \| 0.1691 \| 0.2099 \| \| No log \| 24.0 \| 168 \| 2.1221 \| 0.535 \| 0.6115 \| 2.4854 \| 0.535 \| 0.5252 \| 0.1779 \| 0.2234 \| \| No log \| 25.0 \| 175 \| 2.1912 \| 0.52 \| 0.6295 \| 2.4975 \| 0.52 \| 0.5109 \| 0.1970 \| 0.2339 \| \| No log \| 26.0 \| 182 \| 2.1056 \| 0.5225 \| 0.6150 \| 2.4697 \| 0.5225 \| 0.5250 \| 0.2020 \| 0.2346 \| \| No log \| 27.0 \| 189 \| 2.1017 \| 0.535 \| 0.6027 \| 2.4992 \| 0.535 \| 0.5399 \| 0.2003 \| 0.2173 \| \| No log \| 28.0 \| 196 \| 2.0999 \| 0.545 \| 0.5929 \| 2.6313 \| 0.545 \| 0.5306 \| 0.1844 \| 0.2126 \| \| No log \| 29.0 \| 203 \| 2.1188 \| 0.54 \| 0.6044 \| 2.5420 \| 0.54 \| 0.5211 \| 0.1745 \| 0.2159 \| \| No log \| 30.0 \| 210 \| 2.0670 \| 0.56 \| 0.5938 \| 2.4868 \| 0.56 \| 0.5500 \| 0.1849 \| 0.2132 \| \| No log \| 31.0 \| 217 \| 2.0709 \| 0.5525 \| 0.5937 \| 2.4206 \| 0.5525 \| 0.5489 \| 0.1759 \| 0.2140 \| \| No log \| 32.0 \| 224 \| 2.0390 \| 0.5675 \| 0.5794 \| 2.5007 \| 0.5675 \| 0.5547 \| 0.1745 \| 0.2032 \| \| No log \| 33.0 \| 231 \| 2.0725 \| 0.5375 \| 0.5912 \| 2.4328 \| 0.5375 \| 0.5237 \| 0.1817 \| 0.2157 \| \| No log \| 34.0 \| 238 \| 2.0644 \| 0.565 \| 0.5950 \| 2.5103 \| 0.565 \| 0.5560 \| 0.1712 \| 0.2085 \| \| No log \| 35.0 \| 245 \| 2.0665 \| 0.5575 \| 0.5918 \| 2.4401 \| 0.5575 \| 0.5519 \| 0.1604 \| 0.2133 \| \| No log \| 36.0 \| 252 \| 2.0485 \| 0.53 \| 0.5977 \| 2.4321 \| 0.53 \| 0.5259 \| 0.1927 \| 0.2163 \| \| No log \| 37.0 \| 259 \| 2.0352 \| 0.555 \| 0.5780 \| 2.4665 \| 0.555 \| 0.5450 \| 0.1701 \| 0.2000 \| \| No log \| 38.0 \| 266 \| 2.0423 \| 0.5425 \| 0.5777 \| 2.5061 \| 0.5425 \| 0.5327 \| 0.1689 \| 0.2021 \| \| No log \| 39.0 \| 273 \| 2.0138 \| 0.5625 \| 0.5799 \| 2.3714 \| 0.5625 \| 0.5581 \| 0.1705 \| 0.2060 \| \| No log \| 40.0 \| 280 \| 2.0161 \| 0.5525 \| 0.5918 \| 2.3704 \| 0.5525 \| 0.5439 \| 0.1720 \| 0.2111 \| \| No log \| 41.0 \| 287 \| 2.0315 \| 0.545 \| 0.5843 \| 2.4582 \| 0.545 \| 0.5355 \| 0.1782 \| 0.2083 \| \| No log \| 42.0 \| 294 \| 2.0157 \| 0.545 \| 0.5861 \| 2.5374 \| 0.545 \| 0.5386 \| 0.1995 \| 0.2129 \| \| No log \| 43.0 \| 301 \| 2.0495 \| 0.555 \| 0.5922 \| 2.4841 \| 0.555 \| 0.5393 \| 0.1538 \| 0.2138 \| \| No log \| 44.0 \| 308 \| 2.0293 \| 0.5525 \| 0.5824 \| 2.4853 \| 0.5525 \| 0.5352 \| 0.1745 \| 0.2042 \| \| No log \| 45.0 \| 315 \| 2.0253 \| 0.5575 \| 0.5776 \| 2.4516 \| 0.5575 \| 0.5421 \| 0.1978 \| 0.2045 \| \| No log \| 46.0 \| 322 \| 2.0246 \| 0.5525 \| 0.5953 \| 2.4196 \| 0.5525 \| 0.5362 \| 0.1715 \| 0.2122 \| \| No log \| 47.0 \| 329 \| 2.0114 \| 0.555 \| 0.5730 \| 2.4431 \| 0.555 \| 0.5462 \| 0.1759 \| 0.2008 \| \| No log \| 48.0 \| 336 \| 2.0046 \| 0.5575 \| 0.5801 \| 2.3784 \| 0.5575 \| 0.5445 \| 0.1703 \| 0.2039 \| \| No log \| 49.0 \| 343 \| 1.9721 \| 0.565 \| 0.5672 \| 2.5034 \| 0.565 \| 0.5594 \| 0.1686 \| 0.1963 \| \| No log \| 50.0 \| 350 \| 1.9872 \| 0.565 \| 0.5704 \| 2.4067 \| 0.565 \| 0.5620 \| 0.1888 \| 0.2000 \| \| No log \| 51.0 \| 357 \| 1.9668 \| 0.5725 \| 0.5695 \| 2.3935 \| 0.5725 \| 0.5711 \| 0.1534 \| 0.1981 \| \| No log \| 52.0 \| 364 \| 1.9796 \| 0.5525 \| 0.5742 \| 2.3977 \| 0.5525 \| 0.5504 \| 0.1614 \| 0.2023 \| \| No log \| 53.0 \| 371 \| 2.0086 \| 0.56 \| 0.5835 \| 2.4361 \| 0.56 \| 0.5510 \| 0.1912 \| 0.2098 \| \| No log \| 54.0 \| 378 \| 1.9998 \| 0.54 \| 0.5776 \| 2.4292 \| 0.54 \| 0.5270 \| 0.1679 \| 0.2042 \| \| No log \| 55.0 \| 385 \| 1.9736 \| 0.555 \| 0.5732 \| 2.3619 \| 0.555 \| 0.5427 \| 0.1830 \| 0.2024 \| \| No log \| 56.0 \| 392 \| 1.9850 \| 0.57 \| 0.5691 \| 2.4491 \| 0.57 \| 0.5670 \| 0.1798 \| 0.1967 \| \| No log \| 57.0 \| 399 \| 1.9680 \| 0.5675 \| 0.5680 \| 2.4863 \| 0.5675 \| 0.5624 \| 0.1644 \| 0.1967 \| \| No log \| 58.0 \| 406 \| 1.9633 \| 0.565 \| 0.5682 \| 2.4407 \| 0.565 \| 0.5573 \| 0.1576 \| 0.1960 \| \| No log \| 59.0 \| 413 \| 1.9715 \| 0.555 \| 0.5750 \| 2.4037 \| 0.555 \| 0.5459 \| 0.1753 \| 0.2027 \| \| No log \| 60.0 \| 420 \| 1.9791 \| 0.575 \| 0.5684 \| 2.3886 \| 0.575 \| 0.5732 \| 0.1647 \| 0.1969 \| \| No log \| 61.0 \| 427 \| 1.9587 \| 0.5675 \| 0.5669 \| 2.4318 \| 0.5675 \| 0.5616 \| 0.1583 \| 0.1948 \| \| No log \| 62.0 \| 434 \| 1.9735 \| 0.5575 \| 0.5699 \| 2.3758 \| 0.5575 \| 0.5550 \| 0.1442 \| 0.1978 \| \| No log \| 63.0 \| 441 \| 1.9720 \| 0.5475 \| 0.5748 \| 2.4373 \| 0.5475 \| 0.5405 \| 0.1660 \| 0.2004 \| \| No log \| 64.0 \| 448 \| 1.9825 \| 0.55 \| 0.5710 \| 2.3517 \| 0.55 \| 0.5490 \| 0.1810 \| 0.2030 \| \| No log \| 65.0 \| 455 \| 1.9679 \| 0.5725 \| 0.5705 \| 2.4311 \| 0.5725 \| 0.5718 \| 0.1690 \| 0.1980 \| \| No log \| 66.0 \| 462 \| 1.9735 \| 0.565 \| 0.5706 \| 2.4810 \| 0.565 \| 0.5624 \| 0.1740 \| 0.1976 \| \| No log \| 67.0 \| 469 \| 1.9752 \| 0.5675 \| 0.5689 \| 2.3718 \| 0.5675 \| 0.5615 \| 0.1681 \| 0.1980 \| \| No log \| 68.0 \| 476 \| 1.9798 \| 0.565 \| 0.5690 \| 2.4235 \| 0.565 \| 0.5602 \| 0.1671 \| 0.1980 \| \| No log \| 69.0 \| 483 \| 1.9720 \| 0.5625 \| 0.5681 \| 2.3483 \| 0.5625 \| 0.5576 \| 0.1845 \| 0.1970 \| \| No log \| 70.0 \| 490 \| 1.9640 \| 0.5675 \| 0.5673 \| 2.3772 \| 0.5675 \| 0.5639 \| 0.1621 \| 0.1962 \| \| No log \| 71.0 \| 497 \| 1.9641 \| 0.5625 \| 0.5680 \| 2.3925 \| 0.5625 \| 0.5567 \| 0.1670 \| 0.1963 \| \| 0.4915 \| 72.0 \| 504 \| 1.9753 \| 0.5625 \| 0.5707 \| 2.4507 \| 0.5625 \| 0.5566 \| 0.1780 \| 0.1986 \| \| 0.4915 \| 73.0 \| 511 \| 1.9792 \| 0.56 \| 0.5700 \| 2.3604 \| 0.56 \| 0.5545 \| 0.1580 \| 0.1990 \| \| 0.4915 \| 74.0 \| 518 \| 1.9679 \| 0.55 \| 0.5700 \| 2.3519 \| 0.55 \| 0.5412 \| 0.1781 \| 0.1981 \| \| 0.4915 \| 75.0 \| 525 \| 1.9711 \| 0.57 \| 0.5685 \| 2.4204 \| 0.57 \| 0.5676 \| 0.1891 \| 0.1962 \| \| 0.4915 \| 76.0 \| 532 \| 1.9705 \| 0.565 \| 0.5686 \| 2.3512 \| 0.565 \| 0.5600 \| 0.1684 \| 0.1967 \| \| 0.4915 \| 77.0 \| 539 \| 1.9673 \| 0.5625 \| 0.5685 \| 2.3481 \| 0.5625 \| 0.5577 \| 0.1761 \| 0.1968 \| \| 0.4915 \| 78.0 \| 546 \| 1.9641 \| 0.5625 \| 0.5668 \| 2.3519 \| 0.5625 \| 0.5577 \| 0.1747 \| 0.1964 \| \| 0.4915 \| 79.0 \| 553 \| 1.9685 \| 0.5675 \| 0.5677 \| 2.3833 \| 0.5675 \| 0.5630 \| 0.1490 \| 0.1967 \| \| 0.4915 \| 80.0 \| 560 \| 1.9722 \| 0.5625 \| 0.5691 \| 2.3864 \| 0.5625 \| 0.5565 \| 0.1583 \| 0.1979 \| \| 0.4915 \| 81.0 \| 567 \| 1.9641 \| 0.5625 \| 0.5675 \| 2.3528 \| 0.5625 \| 0.5566 \| 0.1820 \| 0.1965 \| \| 0.4915 \| 82.0 \| 574 \| 1.9666 \| 0.5625 \| 0.5678 \| 2.3516 \| 0.5625 \| 0.5564 \| 0.1622 \| 0.1968 \| \| 0.4915 \| 83.0 \| 581 \| 1.9703 \| 0.56 \| 0.5679 \| 2.3564 \| 0.56 \| 0.5550 \| 0.1577 \| 0.1967 \| \| 0.4915 \| 84.0 \| 588 \| 1.9666 \| 0.56 \| 0.5673 \| 2.3532 \| 0.56 \| 0.5546 \| 0.1633 \| 0.1967 \| \| 0.4915 \| 85.0 \| 595 \| 1.9683 \| 0.56 \| 0.5682 \| 2.3506 \| 0.56 \| 0.5539 \| 0.1526 \| 0.1971 \| \| 0.4915 \| 86.0 \| 602 \| 1.9673 \| 0.5575 \| 0.5674 \| 2.3523 \| 0.5575 \| 0.5516 \| 0.1642 \| 0.1972 \| \| 0.4915 \| 87.0 \| 609 \| 1.9678 \| 0.56 \| 0.5678 \| 2.3514 \| 0.56 \| 0.5539 \| 0.1543 \| 0.1970 \| \| 0.4915 \| 88.0 \| 616 \| 1.9680 \| 0.56 \| 0.5678 \| 2.3531 \| 0.56 \| 0.5539 \| 0.1581 \| 0.1970 \| \| 0.4915 \| 89.0 \| 623 \| 1.9683 \| 0.5575 \| 0.5681 \| 2.3532 \| 0.5575 \| 0.5516 \| 0.1732 \| 0.1976 \| \| 0.4915 \| 90.0 \| 630 \| 1.9677 \| 0.56 \| 0.5677 \| 2.3536 \| 0.56 \| 0.5539 \| 0.1702 \| 0.1970 \| \| 0.4915 \| 91.0 \| 637 \| 1.9680 \| 0.5575 \| 0.5679 \| 2.3528 \| 0.5575 \| 0.5515 \| 0.1760 \| 0.1974 \| \| 0.4915 \| 92.0 \| 644 \| 1.9681 \| 0.5575 \| 0.5679 \| 2.3522 \| 0.5575 \| 0.5515 \| 0.1584 \| 0.1975 \| \| 0.4915 \| 93.0 \| 651 \| 1.9680 \| 0.5575 \| 0.5678 \| 2.3524 \| 0.5575 \| 0.5515 \| 0.1610 \| 0.1973 \| \| 0.4915 \| 94.0 \| 658 \| 1.9680 \| 0.5575 \| 0.5680 \| 2.3521 \| 0.5575 \| 0.5515 \| 0.1653 \| 0.1973 \| \| 0.4915 \| 95.0 \| 665 \| 1.9681 \| 0.5575 \| 0.5679 \| 2.3524 \| 0.5575 \| 0.5515 \| 0.1663 \| 0.1973 \| \| 0.4915 \| 96.0 \| 672 \| 1.9682 \| 0.5575 \| 0.5680 \| 2.3528 \| 0.5575 \| 0.5516 \| 0.1663 \| 0.1973 \| \| 0.4915 \| 97.0 \| 679 \| 1.9682 \| 0.5575 \| 0.5680 \| 2.3526 \| 0.5575 \| 0.5515 \| 0.1625 \| 0.1973 \| \| 0.4915 \| 98.0 \| 686 \| 1.9681 \| 0.5575 \| 0.5679 \| 2.3526 \| 0.5575 \| 0.5516 \| 0.1676 \| 0.1973 \| \| 0.4915 \| 99.0 \| 693 \| 1.9682 \| 0.5575 \| 0.5679 \| 2.3526 \| 0.5575 \| 0.5516 \| 0.1635 \| 0.1973 \| \| 0.4915 \| 100.0 \| 700 \| 1.9682 \| 0.5575 \| 0.5680 \| 2.3526 \| 0.5575 \| 0.5516 \| 0.1676 \| 0.1973 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
sbaner24/vit-base-patch16-224-Soybean_11-46	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Soybean_11-46 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2058 - Accuracy: 0.9306 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3661 \| 1.0 \| 11 \| 1.3698 \| 0.5069 \| \| 0.9979 \| 2.0 \| 22 \| 0.9817 \| 0.6632 \| \| 0.6746 \| 3.0 \| 33 \| 0.7423 \| 0.7396 \| \| 0.6364 \| 4.0 \| 44 \| 0.6075 \| 0.7569 \| \| 0.5425 \| 5.0 \| 55 \| 0.5500 \| 0.7951 \| \| 0.5001 \| 6.0 \| 66 \| 0.4883 \| 0.8160 \| \| 0.3519 \| 7.0 \| 77 \| 0.4539 \| 0.8264 \| \| 0.4421 \| 8.0 \| 88 \| 0.4483 \| 0.8194 \| \| 0.3207 \| 9.0 \| 99 \| 0.3785 \| 0.8438 \| \| 0.3682 \| 10.0 \| 110 \| 0.3385 \| 0.8646 \| \| 0.2642 \| 11.0 \| 121 \| 0.3827 \| 0.8403 \| \| 0.3444 \| 12.0 \| 132 \| 0.3462 \| 0.8507 \| \| 0.2423 \| 13.0 \| 143 \| 0.3170 \| 0.8681 \| \| 0.3168 \| 14.0 \| 154 \| 0.3168 \| 0.8715 \| \| 0.2781 \| 15.0 \| 165 \| 0.3323 \| 0.8333 \| \| 0.2411 \| 16.0 \| 176 \| 0.3200 \| 0.8715 \| \| 0.2276 \| 17.0 \| 187 \| 0.3296 \| 0.875 \| \| 0.192 \| 18.0 \| 198 \| 0.3119 \| 0.8854 \| \| 0.1612 \| 19.0 \| 209 \| 0.3647 \| 0.875 \| \| 0.1084 \| 20.0 \| 220 \| 0.2641 \| 0.8993 \| \| 0.2099 \| 21.0 \| 231 \| 0.2807 \| 0.8958 \| \| 0.1666 \| 22.0 \| 242 \| 0.2595 \| 0.9097 \| \| 0.1355 \| 23.0 \| 253 \| 0.2735 \| 0.8924 \| \| 0.1165 \| 24.0 \| 264 \| 0.3238 \| 0.8785 \| \| 0.112 \| 25.0 \| 275 \| 0.3066 \| 0.8889 \| \| 0.1191 \| 26.0 \| 286 \| 0.2427 \| 0.9062 \| \| 0.1293 \| 27.0 \| 297 \| 0.2536 \| 0.9201 \| \| 0.2932 \| 28.0 \| 308 \| 0.2707 \| 0.8924 \| \| 0.0918 \| 29.0 \| 319 \| 0.2688 \| 0.8924 \| \| 0.1529 \| 30.0 \| 330 \| 0.2715 \| 0.8889 \| \| 0.227 \| 31.0 \| 341 \| 0.2664 \| 0.9028 \| \| 0.1044 \| 32.0 \| 352 \| 0.2809 \| 0.8993 \| \| 0.0894 \| 33.0 \| 363 \| 0.2863 \| 0.8924 \| \| 0.0566 \| 34.0 \| 374 \| 0.2474 \| 0.9201 \| \| 0.0915 \| 35.0 \| 385 \| 0.2428 \| 0.9097 \| \| 0.1136 \| 36.0 \| 396 \| 0.2545 \| 0.9097 \| \| 0.0947 \| 37.0 \| 407 \| 0.2599 \| 0.9097 \| \| 0.1012 \| 38.0 \| 418 \| 0.2454 \| 0.9167 \| \| 0.0465 \| 39.0 \| 429 \| 0.2435 \| 0.9201 \| \| 0.0299 \| 40.0 \| 440 \| 0.2532 \| 0.9062 \| \| 0.0311 \| 41.0 \| 451 \| 0.2298 \| 0.9271 \| \| 0.0796 \| 42.0 \| 462 \| 0.2422 \| 0.9167 \| \| 0.058 \| 43.0 \| 473 \| 0.2058 \| 0.9306 \| \| 0.0853 \| 44.0 \| 484 \| 0.2266 \| 0.9306 \| \| 0.0868 \| 45.0 \| 495 \| 0.2266 \| 0.9236 \| \| 0.0554 \| 46.0 \| 506 \| 0.2163 \| 0.9271 \| \| 0.0508 \| 47.0 \| 517 \| 0.2104 \| 0.9306 \| \| 0.0589 \| 48.0 \| 528 \| 0.2172 \| 0.9271 \| \| 0.0369 \| 49.0 \| 539 \| 0.2214 \| 0.9271 \| \| 0.0852 \| 50.0 \| 550 \| 0.2241 \| 0.9271 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1	[ "0", "1", "2", "3", "4" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7789 - Accuracy: 0.565 - Brier Loss: 0.5798 - Nll: 2.3548 - F1 Micro: 0.565 - F1 Macro: 0.5569 - Ece: 0.1677 - Aurc: 0.2032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.7719 \| 0.04 \| 1.0712 \| 7.3456 \| 0.04 \| 0.0315 \| 0.2816 \| 0.9543 \| \| No log \| 2.0 \| 14 \| 3.7636 \| 0.1 \| 0.9486 \| 5.7989 \| 0.1000 \| 0.0863 \| 0.1625 \| 0.8854 \| \| No log \| 3.0 \| 21 \| 3.3871 \| 0.235 \| 0.8838 \| 5.3315 \| 0.235 \| 0.1857 \| 0.1589 \| 0.6274 \| \| No log \| 4.0 \| 28 \| 2.8780 \| 0.2975 \| 0.7900 \| 3.5467 \| 0.2975 \| 0.2884 \| 0.1672 \| 0.4712 \| \| No log \| 5.0 \| 35 \| 2.5223 \| 0.3875 \| 0.7164 \| 3.0188 \| 0.3875 \| 0.3630 \| 0.1596 \| 0.3495 \| \| No log \| 6.0 \| 42 \| 2.3634 \| 0.43 \| 0.6927 \| 3.1378 \| 0.4300 \| 0.4031 \| 0.1877 \| 0.3055 \| \| No log \| 7.0 \| 49 \| 2.3487 \| 0.445 \| 0.7009 \| 3.0084 \| 0.445 \| 0.4123 \| 0.2026 \| 0.3003 \| \| No log \| 8.0 \| 56 \| 2.2521 \| 0.47 \| 0.6857 \| 2.8634 \| 0.47 \| 0.4607 \| 0.1977 \| 0.2918 \| \| No log \| 9.0 \| 63 \| 2.3597 \| 0.4775 \| 0.6955 \| 2.6768 \| 0.4775 \| 0.4642 \| 0.2215 \| 0.2787 \| \| No log \| 10.0 \| 70 \| 2.3524 \| 0.46 \| 0.7130 \| 2.7830 \| 0.46 \| 0.4441 \| 0.2429 \| 0.2941 \| \| No log \| 11.0 \| 77 \| 2.4233 \| 0.46 \| 0.7108 \| 3.0867 \| 0.46 \| 0.4418 \| 0.2247 \| 0.2828 \| \| No log \| 12.0 \| 84 \| 2.2723 \| 0.485 \| 0.6901 \| 2.7313 \| 0.485 \| 0.4742 \| 0.2293 \| 0.2774 \| \| No log \| 13.0 \| 91 \| 2.2818 \| 0.49 \| 0.7007 \| 2.5829 \| 0.49 \| 0.4859 \| 0.2473 \| 0.2791 \| \| No log \| 14.0 \| 98 \| 2.1682 \| 0.4975 \| 0.6695 \| 2.8292 \| 0.4975 \| 0.4815 \| 0.2170 \| 0.2653 \| \| No log \| 15.0 \| 105 \| 2.0652 \| 0.52 \| 0.6520 \| 2.4319 \| 0.52 \| 0.5120 \| 0.2079 \| 0.2617 \| \| No log \| 16.0 \| 112 \| 2.0524 \| 0.5225 \| 0.6384 \| 2.5273 \| 0.5225 \| 0.5128 \| 0.1980 \| 0.2391 \| \| No log \| 17.0 \| 119 \| 2.1736 \| 0.48 \| 0.6765 \| 2.4887 \| 0.48 \| 0.4809 \| 0.2112 \| 0.2834 \| \| No log \| 18.0 \| 126 \| 2.0096 \| 0.515 \| 0.6496 \| 2.4230 \| 0.515 \| 0.5215 \| 0.1957 \| 0.2513 \| \| No log \| 19.0 \| 133 \| 2.0207 \| 0.5175 \| 0.6417 \| 2.4591 \| 0.5175 \| 0.5104 \| 0.2011 \| 0.2526 \| \| No log \| 20.0 \| 140 \| 1.9152 \| 0.5425 \| 0.6044 \| 2.5069 \| 0.5425 \| 0.5405 \| 0.1826 \| 0.2147 \| \| No log \| 21.0 \| 147 \| 1.9600 \| 0.52 \| 0.6332 \| 2.5016 \| 0.52 \| 0.5204 \| 0.1871 \| 0.2427 \| \| No log \| 22.0 \| 154 \| 1.9325 \| 0.515 \| 0.6226 \| 2.4981 \| 0.515 \| 0.5078 \| 0.1860 \| 0.2323 \| \| No log \| 23.0 \| 161 \| 1.9172 \| 0.53 \| 0.6144 \| 2.4601 \| 0.53 \| 0.5221 \| 0.1941 \| 0.2311 \| \| No log \| 24.0 \| 168 \| 1.8891 \| 0.5425 \| 0.6091 \| 2.4653 \| 0.5425 \| 0.5399 \| 0.1933 \| 0.2260 \| \| No log \| 25.0 \| 175 \| 1.9460 \| 0.5175 \| 0.6214 \| 2.4785 \| 0.5175 \| 0.5168 \| 0.1694 \| 0.2335 \| \| No log \| 26.0 \| 182 \| 1.9060 \| 0.5525 \| 0.5970 \| 2.4789 \| 0.5525 \| 0.5430 \| 0.1934 \| 0.2137 \| \| No log \| 27.0 \| 189 \| 1.9421 \| 0.5375 \| 0.6205 \| 2.4814 \| 0.5375 \| 0.5313 \| 0.2135 \| 0.2402 \| \| No log \| 28.0 \| 196 \| 2.0195 \| 0.545 \| 0.6187 \| 2.5330 \| 0.545 \| 0.5256 \| 0.1800 \| 0.2360 \| \| No log \| 29.0 \| 203 \| 1.9428 \| 0.535 \| 0.6167 \| 2.4894 \| 0.535 \| 0.5111 \| 0.1862 \| 0.2322 \| \| No log \| 30.0 \| 210 \| 1.8996 \| 0.5225 \| 0.6207 \| 2.4810 \| 0.5225 \| 0.5137 \| 0.1994 \| 0.2330 \| \| No log \| 31.0 \| 217 \| 1.8462 \| 0.54 \| 0.6119 \| 2.4201 \| 0.54 \| 0.5341 \| 0.1817 \| 0.2190 \| \| No log \| 32.0 \| 224 \| 1.8324 \| 0.55 \| 0.5988 \| 2.4230 \| 0.55 \| 0.5427 \| 0.1888 \| 0.2171 \| \| No log \| 33.0 \| 231 \| 1.8393 \| 0.545 \| 0.5977 \| 2.3943 \| 0.545 \| 0.5339 \| 0.1838 \| 0.2172 \| \| No log \| 34.0 \| 238 \| 1.8704 \| 0.5475 \| 0.6081 \| 2.4488 \| 0.5475 \| 0.5427 \| 0.1768 \| 0.2200 \| \| No log \| 35.0 \| 245 \| 1.8546 \| 0.54 \| 0.6022 \| 2.3273 \| 0.54 \| 0.5316 \| 0.1847 \| 0.2226 \| \| No log \| 36.0 \| 252 \| 1.8608 \| 0.53 \| 0.5972 \| 2.5153 \| 0.53 \| 0.5139 \| 0.1810 \| 0.2202 \| \| No log \| 37.0 \| 259 \| 1.8663 \| 0.5325 \| 0.6057 \| 2.4642 \| 0.5325 \| 0.5243 \| 0.1836 \| 0.2205 \| \| No log \| 38.0 \| 266 \| 1.8300 \| 0.545 \| 0.5954 \| 2.5101 \| 0.545 \| 0.5418 \| 0.1890 \| 0.2141 \| \| No log \| 39.0 \| 273 \| 1.8121 \| 0.5625 \| 0.5853 \| 2.4397 \| 0.5625 \| 0.5550 \| 0.1704 \| 0.2110 \| \| No log \| 40.0 \| 280 \| 1.7916 \| 0.54 \| 0.5884 \| 2.3565 \| 0.54 \| 0.5361 \| 0.1685 \| 0.2135 \| \| No log \| 41.0 \| 287 \| 1.8353 \| 0.5575 \| 0.5929 \| 2.4252 \| 0.5575 \| 0.5451 \| 0.1823 \| 0.2116 \| \| No log \| 42.0 \| 294 \| 1.7999 \| 0.5675 \| 0.5839 \| 2.4820 \| 0.5675 \| 0.5631 \| 0.1729 \| 0.2045 \| \| No log \| 43.0 \| 301 \| 1.8622 \| 0.52 \| 0.6106 \| 2.4823 \| 0.52 \| 0.5028 \| 0.1948 \| 0.2270 \| \| No log \| 44.0 \| 308 \| 1.7892 \| 0.55 \| 0.5892 \| 2.3342 \| 0.55 \| 0.5450 \| 0.1798 \| 0.2126 \| \| No log \| 45.0 \| 315 \| 1.7978 \| 0.545 \| 0.5868 \| 2.4345 \| 0.545 \| 0.5439 \| 0.1894 \| 0.2094 \| \| No log \| 46.0 \| 322 \| 1.7697 \| 0.56 \| 0.5772 \| 2.4272 \| 0.56 \| 0.5585 \| 0.1601 \| 0.1997 \| \| No log \| 47.0 \| 329 \| 1.7754 \| 0.5475 \| 0.5835 \| 2.3977 \| 0.5475 \| 0.5438 \| 0.1759 \| 0.2059 \| \| No log \| 48.0 \| 336 \| 1.7922 \| 0.545 \| 0.5929 \| 2.4119 \| 0.545 \| 0.5390 \| 0.1891 \| 0.2131 \| \| No log \| 49.0 \| 343 \| 1.8055 \| 0.5625 \| 0.5872 \| 2.3654 \| 0.5625 \| 0.5497 \| 0.1759 \| 0.2073 \| \| No log \| 50.0 \| 350 \| 1.7972 \| 0.56 \| 0.5894 \| 2.3366 \| 0.56 \| 0.5487 \| 0.1803 \| 0.2083 \| \| No log \| 51.0 \| 357 \| 1.7890 \| 0.555 \| 0.5815 \| 2.3858 \| 0.555 \| 0.5501 \| 0.1693 \| 0.2067 \| \| No log \| 52.0 \| 364 \| 1.7958 \| 0.5475 \| 0.5883 \| 2.4244 \| 0.5475 \| 0.5355 \| 0.1910 \| 0.2105 \| \| No log \| 53.0 \| 371 \| 1.7881 \| 0.5675 \| 0.5834 \| 2.4135 \| 0.5675 \| 0.5603 \| 0.1836 \| 0.2028 \| \| No log \| 54.0 \| 378 \| 1.7675 \| 0.555 \| 0.5766 \| 2.4043 \| 0.555 \| 0.5563 \| 0.1653 \| 0.2047 \| \| No log \| 55.0 \| 385 \| 1.7688 \| 0.55 \| 0.5843 \| 2.3641 \| 0.55 \| 0.5505 \| 0.1729 \| 0.2092 \| \| No log \| 56.0 \| 392 \| 1.7796 \| 0.55 \| 0.5861 \| 2.3404 \| 0.55 \| 0.5458 \| 0.1808 \| 0.2114 \| \| No log \| 57.0 \| 399 \| 1.7861 \| 0.54 \| 0.5885 \| 2.3460 \| 0.54 \| 0.5323 \| 0.1902 \| 0.2073 \| \| No log \| 58.0 \| 406 \| 1.7746 \| 0.56 \| 0.5818 \| 2.3715 \| 0.56 \| 0.5557 \| 0.1643 \| 0.2034 \| \| No log \| 59.0 \| 413 \| 1.7828 \| 0.5575 \| 0.5868 \| 2.3086 \| 0.5575 \| 0.5526 \| 0.1956 \| 0.2088 \| \| No log \| 60.0 \| 420 \| 1.7735 \| 0.565 \| 0.5825 \| 2.3405 \| 0.565 \| 0.5619 \| 0.1696 \| 0.2058 \| \| No log \| 61.0 \| 427 \| 1.7651 \| 0.5675 \| 0.5760 \| 2.4771 \| 0.5675 \| 0.5636 \| 0.1847 \| 0.2027 \| \| No log \| 62.0 \| 434 \| 1.7751 \| 0.5575 \| 0.5834 \| 2.3727 \| 0.5575 \| 0.5524 \| 0.1638 \| 0.2052 \| \| No log \| 63.0 \| 441 \| 1.7900 \| 0.56 \| 0.5834 \| 2.3635 \| 0.56 \| 0.5502 \| 0.1789 \| 0.2061 \| \| No log \| 64.0 \| 448 \| 1.7729 \| 0.56 \| 0.5821 \| 2.3797 \| 0.56 \| 0.5554 \| 0.1676 \| 0.2046 \| \| No log \| 65.0 \| 455 \| 1.7743 \| 0.5625 \| 0.5826 \| 2.4174 \| 0.5625 \| 0.5581 \| 0.1538 \| 0.2052 \| \| No log \| 66.0 \| 462 \| 1.7749 \| 0.5625 \| 0.5801 \| 2.3799 \| 0.5625 \| 0.5592 \| 0.1709 \| 0.2036 \| \| No log \| 67.0 \| 469 \| 1.7795 \| 0.5625 \| 0.5814 \| 2.3169 \| 0.5625 \| 0.5533 \| 0.1883 \| 0.2037 \| \| No log \| 68.0 \| 476 \| 1.7773 \| 0.5675 \| 0.5794 \| 2.3588 \| 0.5675 \| 0.5622 \| 0.1779 \| 0.2013 \| \| No log \| 69.0 \| 483 \| 1.7762 \| 0.56 \| 0.5793 \| 2.3514 \| 0.56 \| 0.5566 \| 0.1707 \| 0.2039 \| \| No log \| 70.0 \| 490 \| 1.7762 \| 0.5625 \| 0.5787 \| 2.3620 \| 0.5625 \| 0.5529 \| 0.1607 \| 0.2017 \| \| No log \| 71.0 \| 497 \| 1.7740 \| 0.5675 \| 0.5798 \| 2.3235 \| 0.5675 \| 0.5612 \| 0.1637 \| 0.2046 \| \| 0.4215 \| 72.0 \| 504 \| 1.7739 \| 0.56 \| 0.5790 \| 2.3235 \| 0.56 \| 0.5542 \| 0.1583 \| 0.2023 \| \| 0.4215 \| 73.0 \| 511 \| 1.7783 \| 0.56 \| 0.5806 \| 2.4187 \| 0.56 \| 0.5545 \| 0.1674 \| 0.2040 \| \| 0.4215 \| 74.0 \| 518 \| 1.7785 \| 0.56 \| 0.5805 \| 2.3302 \| 0.56 \| 0.5544 \| 0.1748 \| 0.2033 \| \| 0.4215 \| 75.0 \| 525 \| 1.7777 \| 0.5625 \| 0.5795 \| 2.3321 \| 0.5625 \| 0.5548 \| 0.1754 \| 0.2029 \| \| 0.4215 \| 76.0 \| 532 \| 1.7785 \| 0.565 \| 0.5799 \| 2.3249 \| 0.565 \| 0.5586 \| 0.1696 \| 0.2023 \| \| 0.4215 \| 77.0 \| 539 \| 1.7763 \| 0.565 \| 0.5790 \| 2.3561 \| 0.565 \| 0.5573 \| 0.1574 \| 0.2022 \| \| 0.4215 \| 78.0 \| 546 \| 1.7767 \| 0.565 \| 0.5790 \| 2.3296 \| 0.565 \| 0.5572 \| 0.1633 \| 0.2024 \| \| 0.4215 \| 79.0 \| 553 \| 1.7763 \| 0.565 \| 0.5790 \| 2.3555 \| 0.565 \| 0.5580 \| 0.1687 \| 0.2016 \| \| 0.4215 \| 80.0 \| 560 \| 1.7783 \| 0.565 \| 0.5800 \| 2.3254 \| 0.565 \| 0.5576 \| 0.1752 \| 0.2032 \| \| 0.4215 \| 81.0 \| 567 \| 1.7773 \| 0.5675 \| 0.5796 \| 2.3530 \| 0.5675 \| 0.5605 \| 0.1519 \| 0.2023 \| \| 0.4215 \| 82.0 \| 574 \| 1.7774 \| 0.5625 \| 0.5797 \| 2.3253 \| 0.5625 \| 0.5549 \| 0.1911 \| 0.2028 \| \| 0.4215 \| 83.0 \| 581 \| 1.7784 \| 0.5625 \| 0.5794 \| 2.3554 \| 0.5625 \| 0.5544 \| 0.1659 \| 0.2030 \| \| 0.4215 \| 84.0 \| 588 \| 1.7769 \| 0.565 \| 0.5793 \| 2.3527 \| 0.565 \| 0.5585 \| 0.1588 \| 0.2024 \| \| 0.4215 \| 85.0 \| 595 \| 1.7787 \| 0.565 \| 0.5799 \| 2.3549 \| 0.565 \| 0.5576 \| 0.1687 \| 0.2032 \| \| 0.4215 \| 86.0 \| 602 \| 1.7778 \| 0.565 \| 0.5795 \| 2.3548 \| 0.565 \| 0.5574 \| 0.1577 \| 0.2029 \| \| 0.4215 \| 87.0 \| 609 \| 1.7787 \| 0.5625 \| 0.5798 \| 2.3545 \| 0.5625 \| 0.5549 \| 0.1643 \| 0.2032 \| \| 0.4215 \| 88.0 \| 616 \| 1.7786 \| 0.565 \| 0.5796 \| 2.3554 \| 0.565 \| 0.5574 \| 0.1667 \| 0.2031 \| \| 0.4215 \| 89.0 \| 623 \| 1.7785 \| 0.565 \| 0.5799 \| 2.3546 \| 0.565 \| 0.5574 \| 0.1691 \| 0.2032 \| \| 0.4215 \| 90.0 \| 630 \| 1.7784 \| 0.565 \| 0.5797 \| 2.3548 \| 0.565 \| 0.5574 \| 0.1656 \| 0.2031 \| \| 0.4215 \| 91.0 \| 637 \| 1.7784 \| 0.565 \| 0.5797 \| 2.3550 \| 0.565 \| 0.5569 \| 0.1753 \| 0.2032 \| \| 0.4215 \| 92.0 \| 644 \| 1.7786 \| 0.565 \| 0.5797 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1744 \| 0.2030 \| \| 0.4215 \| 93.0 \| 651 \| 1.7785 \| 0.565 \| 0.5797 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1709 \| 0.2031 \| \| 0.4215 \| 94.0 \| 658 \| 1.7787 \| 0.565 \| 0.5797 \| 2.3543 \| 0.565 \| 0.5574 \| 0.1704 \| 0.2032 \| \| 0.4215 \| 95.0 \| 665 \| 1.7787 \| 0.565 \| 0.5798 \| 2.3545 \| 0.565 \| 0.5574 \| 0.1713 \| 0.2032 \| \| 0.4215 \| 96.0 \| 672 \| 1.7788 \| 0.565 \| 0.5798 \| 2.3549 \| 0.565 \| 0.5569 \| 0.1777 \| 0.2031 \| \| 0.4215 \| 97.0 \| 679 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3550 \| 0.565 \| 0.5569 \| 0.1677 \| 0.2032 \| \| 0.4215 \| 98.0 \| 686 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3549 \| 0.565 \| 0.5569 \| 0.1648 \| 0.2032 \| \| 0.4215 \| 99.0 \| 693 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3548 \| 0.565 \| 0.5569 \| 0.1728 \| 0.2032 \| \| 0.4215 \| 100.0 \| 700 \| 1.7789 \| 0.565 \| 0.5798 \| 2.3548 \| 0.565 \| 0.5569 \| 0.1677 \| 0.2032 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
ayanban011/vit-base_tobacco_lr5e-6_e_200	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_lr5e-6_e_200 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7893 - Accuracy: 0.78 - Brier Loss: 0.3364 - Nll: 1.4430 - F1 Micro: 0.78 - F1 Macro: 0.7488 - Ece: 0.1922 - Aurc: 0.1018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7450 \| 0.815 \| 0.3079 \| 1.1882 \| 0.815 \| 0.7942 \| 0.2383 \| 0.0734 \| \| No log \| 2.0 \| 25 \| 0.7440 \| 0.815 \| 0.3074 \| 1.1877 \| 0.815 \| 0.7922 \| 0.2303 \| 0.0734 \| \| No log \| 2.96 \| 37 \| 0.7429 \| 0.81 \| 0.3071 \| 1.1883 \| 0.81 \| 0.7914 \| 0.2367 \| 0.0714 \| \| No log \| 4.0 \| 50 \| 0.7463 \| 0.815 \| 0.3083 \| 1.1912 \| 0.815 \| 0.7942 \| 0.2334 \| 0.0768 \| \| No log \| 4.96 \| 62 \| 0.7453 \| 0.815 \| 0.3080 \| 1.1927 \| 0.815 \| 0.7922 \| 0.2224 \| 0.0751 \| \| No log \| 6.0 \| 75 \| 0.7416 \| 0.81 \| 0.3067 \| 1.1870 \| 0.81 \| 0.7914 \| 0.2232 \| 0.0716 \| \| No log \| 6.96 \| 87 \| 0.7420 \| 0.81 \| 0.3070 \| 1.1858 \| 0.81 \| 0.7914 \| 0.2309 \| 0.0730 \| \| No log \| 8.0 \| 100 \| 0.7409 \| 0.81 \| 0.3062 \| 1.1879 \| 0.81 \| 0.7871 \| 0.2186 \| 0.0749 \| \| No log \| 8.96 \| 112 \| 0.7444 \| 0.815 \| 0.3079 \| 1.1984 \| 0.815 \| 0.8059 \| 0.2342 \| 0.0766 \| \| No log \| 10.0 \| 125 \| 0.7445 \| 0.81 \| 0.3079 \| 1.1918 \| 0.81 \| 0.7894 \| 0.2353 \| 0.0775 \| \| No log \| 10.96 \| 137 \| 0.7451 \| 0.81 \| 0.3085 \| 1.1888 \| 0.81 \| 0.7871 \| 0.2319 \| 0.0784 \| \| No log \| 12.0 \| 150 \| 0.7411 \| 0.805 \| 0.3060 \| 1.1914 \| 0.805 \| 0.7829 \| 0.2186 \| 0.0779 \| \| No log \| 12.96 \| 162 \| 0.7406 \| 0.815 \| 0.3075 \| 1.1967 \| 0.815 \| 0.8057 \| 0.2472 \| 0.0746 \| \| No log \| 14.0 \| 175 \| 0.7360 \| 0.805 \| 0.3048 \| 1.2494 \| 0.805 \| 0.7841 \| 0.2383 \| 0.0770 \| \| No log \| 14.96 \| 187 \| 0.7331 \| 0.81 \| 0.3037 \| 1.1896 \| 0.81 \| 0.7912 \| 0.2306 \| 0.0721 \| \| No log \| 16.0 \| 200 \| 0.7345 \| 0.81 \| 0.3042 \| 1.2488 \| 0.81 \| 0.7956 \| 0.2064 \| 0.0794 \| \| No log \| 16.96 \| 212 \| 0.7329 \| 0.815 \| 0.3018 \| 1.2595 \| 0.815 \| 0.8043 \| 0.2331 \| 0.0797 \| \| No log \| 18.0 \| 225 \| 0.7354 \| 0.795 \| 0.3055 \| 1.1743 \| 0.795 \| 0.7765 \| 0.2154 \| 0.0742 \| \| No log \| 18.96 \| 237 \| 0.7282 \| 0.805 \| 0.3001 \| 1.1920 \| 0.805 \| 0.7844 \| 0.2444 \| 0.0748 \| \| No log \| 20.0 \| 250 \| 0.7225 \| 0.805 \| 0.2981 \| 1.1804 \| 0.805 \| 0.7874 \| 0.2041 \| 0.0744 \| \| No log \| 20.96 \| 262 \| 0.7250 \| 0.81 \| 0.3000 \| 1.2516 \| 0.81 \| 0.7900 \| 0.2148 \| 0.0761 \| \| No log \| 22.0 \| 275 \| 0.7252 \| 0.81 \| 0.3009 \| 1.3033 \| 0.81 \| 0.7954 \| 0.2375 \| 0.0782 \| \| No log \| 22.96 \| 287 \| 0.7293 \| 0.8 \| 0.3016 \| 1.2557 \| 0.8000 \| 0.7796 \| 0.2243 \| 0.0810 \| \| No log \| 24.0 \| 300 \| 0.7344 \| 0.805 \| 0.3057 \| 1.3122 \| 0.805 \| 0.7853 \| 0.2096 \| 0.0815 \| \| No log \| 24.96 \| 312 \| 0.7314 \| 0.81 \| 0.3044 \| 1.2501 \| 0.81 \| 0.7909 \| 0.2068 \| 0.0808 \| \| No log \| 26.0 \| 325 \| 0.7293 \| 0.81 \| 0.3036 \| 1.3066 \| 0.81 \| 0.7909 \| 0.2015 \| 0.0804 \| \| No log \| 26.96 \| 337 \| 0.7323 \| 0.805 \| 0.3042 \| 1.3147 \| 0.805 \| 0.7853 \| 0.2242 \| 0.0827 \| \| No log \| 28.0 \| 350 \| 0.7288 \| 0.805 \| 0.3029 \| 1.3109 \| 0.805 \| 0.7853 \| 0.1976 \| 0.0826 \| \| No log \| 28.96 \| 362 \| 0.7343 \| 0.805 \| 0.3058 \| 1.3108 \| 0.805 \| 0.7853 \| 0.2128 \| 0.0851 \| \| No log \| 30.0 \| 375 \| 0.7351 \| 0.8 \| 0.3062 \| 1.3129 \| 0.8000 \| 0.7733 \| 0.2088 \| 0.0845 \| \| No log \| 30.96 \| 387 \| 0.7301 \| 0.8 \| 0.3044 \| 1.2452 \| 0.8000 \| 0.7733 \| 0.2272 \| 0.0836 \| \| No log \| 32.0 \| 400 \| 0.7340 \| 0.8 \| 0.3055 \| 1.3769 \| 0.8000 \| 0.7806 \| 0.2207 \| 0.0855 \| \| No log \| 32.96 \| 412 \| 0.7322 \| 0.805 \| 0.3052 \| 1.3132 \| 0.805 \| 0.7784 \| 0.2470 \| 0.0865 \| \| No log \| 34.0 \| 425 \| 0.7301 \| 0.8 \| 0.3045 \| 1.2543 \| 0.8000 \| 0.7733 \| 0.2021 \| 0.0863 \| \| No log \| 34.96 \| 437 \| 0.7297 \| 0.8 \| 0.3046 \| 1.3056 \| 0.8000 \| 0.7731 \| 0.1886 \| 0.0836 \| \| No log \| 36.0 \| 450 \| 0.7326 \| 0.805 \| 0.3056 \| 1.3102 \| 0.805 \| 0.7784 \| 0.2256 \| 0.0892 \| \| No log \| 36.96 \| 462 \| 0.7311 \| 0.8 \| 0.3046 \| 1.3681 \| 0.8000 \| 0.7733 \| 0.2065 \| 0.0880 \| \| No log \| 38.0 \| 475 \| 0.7356 \| 0.8 \| 0.3077 \| 1.3666 \| 0.8000 \| 0.7733 \| 0.2034 \| 0.0895 \| \| No log \| 38.96 \| 487 \| 0.7302 \| 0.8 \| 0.3042 \| 1.3114 \| 0.8000 \| 0.7733 \| 0.1996 \| 0.0894 \| \| 0.1528 \| 40.0 \| 500 \| 0.7289 \| 0.805 \| 0.3035 \| 1.3669 \| 0.805 \| 0.7797 \| 0.1951 \| 0.0874 \| \| 0.1528 \| 40.96 \| 512 \| 0.7292 \| 0.8 \| 0.3037 \| 1.3685 \| 0.8000 \| 0.7737 \| 0.2103 \| 0.0878 \| \| 0.1528 \| 42.0 \| 525 \| 0.7347 \| 0.805 \| 0.3077 \| 1.3640 \| 0.805 \| 0.7784 \| 0.2005 \| 0.0894 \| \| 0.1528 \| 42.96 \| 537 \| 0.7343 \| 0.805 \| 0.3063 \| 1.3684 \| 0.805 \| 0.7784 \| 0.2055 \| 0.0899 \| \| 0.1528 \| 44.0 \| 550 \| 0.7283 \| 0.805 \| 0.3038 \| 1.3660 \| 0.805 \| 0.7797 \| 0.2142 \| 0.0881 \| \| 0.1528 \| 44.96 \| 562 \| 0.7325 \| 0.805 \| 0.3061 \| 1.3658 \| 0.805 \| 0.7784 \| 0.1967 \| 0.0896 \| \| 0.1528 \| 46.0 \| 575 \| 0.7303 \| 0.81 \| 0.3049 \| 1.3659 \| 0.81 \| 0.7844 \| 0.1985 \| 0.0883 \| \| 0.1528 \| 46.96 \| 587 \| 0.7321 \| 0.805 \| 0.3054 \| 1.3657 \| 0.805 \| 0.7797 \| 0.1925 \| 0.0888 \| \| 0.1528 \| 48.0 \| 600 \| 0.7322 \| 0.8 \| 0.3054 \| 1.3680 \| 0.8000 \| 0.7704 \| 0.1837 \| 0.0903 \| \| 0.1528 \| 48.96 \| 612 \| 0.7368 \| 0.8 \| 0.3079 \| 1.3234 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0905 \| \| 0.1528 \| 50.0 \| 625 \| 0.7358 \| 0.8 \| 0.3074 \| 1.3682 \| 0.8000 \| 0.7704 \| 0.1971 \| 0.0899 \| \| 0.1528 \| 50.96 \| 637 \| 0.7318 \| 0.8 \| 0.3054 \| 1.3661 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0889 \| \| 0.1528 \| 52.0 \| 650 \| 0.7473 \| 0.795 \| 0.3133 \| 1.3707 \| 0.795 \| 0.7660 \| 0.2032 \| 0.0954 \| \| 0.1528 \| 52.96 \| 662 \| 0.7299 \| 0.805 \| 0.3050 \| 1.3631 \| 0.805 \| 0.7797 \| 0.1977 \| 0.0891 \| \| 0.1528 \| 54.0 \| 675 \| 0.7427 \| 0.795 \| 0.3116 \| 1.3673 \| 0.795 \| 0.7644 \| 0.2181 \| 0.0923 \| \| 0.1528 \| 54.96 \| 687 \| 0.7371 \| 0.8 \| 0.3087 \| 1.3648 \| 0.8000 \| 0.7704 \| 0.1984 \| 0.0902 \| \| 0.1528 \| 56.0 \| 700 \| 0.7399 \| 0.8 \| 0.3100 \| 1.3671 \| 0.8000 \| 0.7704 \| 0.2178 \| 0.0920 \| \| 0.1528 \| 56.96 \| 712 \| 0.7420 \| 0.8 \| 0.3114 \| 1.3671 \| 0.8000 \| 0.7689 \| 0.2035 \| 0.0925 \| \| 0.1528 \| 58.0 \| 725 \| 0.7380 \| 0.8 \| 0.3088 \| 1.3646 \| 0.8000 \| 0.7704 \| 0.2016 \| 0.0916 \| \| 0.1528 \| 58.96 \| 737 \| 0.7390 \| 0.8 \| 0.3091 \| 1.3671 \| 0.8000 \| 0.7704 \| 0.2005 \| 0.0921 \| \| 0.1528 \| 60.0 \| 750 \| 0.7370 \| 0.8 \| 0.3085 \| 1.3651 \| 0.8000 \| 0.7704 \| 0.1891 \| 0.0908 \| \| 0.1528 \| 60.96 \| 762 \| 0.7388 \| 0.8 \| 0.3097 \| 1.3622 \| 0.8000 \| 0.7704 \| 0.2010 \| 0.0906 \| \| 0.1528 \| 62.0 \| 775 \| 0.7403 \| 0.795 \| 0.3102 \| 1.3691 \| 0.795 \| 0.7654 \| 0.2142 \| 0.0910 \| \| 0.1528 \| 62.96 \| 787 \| 0.7390 \| 0.8 \| 0.3088 \| 1.3950 \| 0.8000 \| 0.7684 \| 0.2058 \| 0.0910 \| \| 0.1528 \| 64.0 \| 800 \| 0.7431 \| 0.795 \| 0.3110 \| 1.3722 \| 0.795 \| 0.7656 \| 0.1959 \| 0.0914 \| \| 0.1528 \| 64.96 \| 812 \| 0.7429 \| 0.8 \| 0.3117 \| 1.3673 \| 0.8000 \| 0.7704 \| 0.1933 \| 0.0925 \| \| 0.1528 \| 66.0 \| 825 \| 0.7419 \| 0.8 \| 0.3115 \| 1.3663 \| 0.8000 \| 0.7704 \| 0.1986 \| 0.0913 \| \| 0.1528 \| 66.96 \| 837 \| 0.7434 \| 0.8 \| 0.3120 \| 1.3672 \| 0.8000 \| 0.7704 \| 0.1929 \| 0.0927 \| \| 0.1528 \| 68.0 \| 850 \| 0.7414 \| 0.8 \| 0.3115 \| 1.3649 \| 0.8000 \| 0.7704 \| 0.1988 \| 0.0923 \| \| 0.1528 \| 68.96 \| 862 \| 0.7448 \| 0.8 \| 0.3129 \| 1.3685 \| 0.8000 \| 0.7704 \| 0.2251 \| 0.0928 \| \| 0.1528 \| 70.0 \| 875 \| 0.7450 \| 0.8 \| 0.3130 \| 1.3657 \| 0.8000 \| 0.7704 \| 0.1969 \| 0.0934 \| \| 0.1528 \| 70.96 \| 887 \| 0.7464 \| 0.8 \| 0.3132 \| 1.3686 \| 0.8000 \| 0.7704 \| 0.1988 \| 0.0946 \| \| 0.1528 \| 72.0 \| 900 \| 0.7465 \| 0.8 \| 0.3138 \| 1.3682 \| 0.8000 \| 0.7707 \| 0.2015 \| 0.0935 \| \| 0.1528 \| 72.96 \| 912 \| 0.7471 \| 0.8 \| 0.3142 \| 1.3685 \| 0.8000 \| 0.7696 \| 0.2093 \| 0.0936 \| \| 0.1528 \| 74.0 \| 925 \| 0.7476 \| 0.8 \| 0.3145 \| 1.3685 \| 0.8000 \| 0.7704 \| 0.2120 \| 0.0942 \| \| 0.1528 \| 74.96 \| 937 \| 0.7433 \| 0.8 \| 0.3126 \| 1.3655 \| 0.8000 \| 0.7696 \| 0.1907 \| 0.0918 \| \| 0.1528 \| 76.0 \| 950 \| 0.7519 \| 0.8 \| 0.3166 \| 1.3683 \| 0.8000 \| 0.7704 \| 0.2111 \| 0.0949 \| \| 0.1528 \| 76.96 \| 962 \| 0.7485 \| 0.8 \| 0.3152 \| 1.3679 \| 0.8000 \| 0.7696 \| 0.2035 \| 0.0942 \| \| 0.1528 \| 78.0 \| 975 \| 0.7496 \| 0.8 \| 0.3155 \| 1.3716 \| 0.8000 \| 0.7704 \| 0.1931 \| 0.0940 \| \| 0.1528 \| 78.96 \| 987 \| 0.7513 \| 0.8 \| 0.3163 \| 1.3684 \| 0.8000 \| 0.7704 \| 0.1888 \| 0.0952 \| \| 0.1059 \| 80.0 \| 1000 \| 0.7490 \| 0.8 \| 0.3157 \| 1.3707 \| 0.8000 \| 0.7691 \| 0.2118 \| 0.0943 \| \| 0.1059 \| 80.96 \| 1012 \| 0.7482 \| 0.8 \| 0.3151 \| 1.3687 \| 0.8000 \| 0.7696 \| 0.2060 \| 0.0945 \| \| 0.1059 \| 82.0 \| 1025 \| 0.7516 \| 0.8 \| 0.3163 \| 1.3682 \| 0.8000 \| 0.7704 \| 0.2248 \| 0.0950 \| \| 0.1059 \| 82.96 \| 1037 \| 0.7527 \| 0.8 \| 0.3174 \| 1.3660 \| 0.8000 \| 0.7707 \| 0.2204 \| 0.0948 \| \| 0.1059 \| 84.0 \| 1050 \| 0.7495 \| 0.8 \| 0.3156 \| 1.3663 \| 0.8000 \| 0.7704 \| 0.2067 \| 0.0939 \| \| 0.1059 \| 84.96 \| 1062 \| 0.7506 \| 0.8 \| 0.3161 \| 1.3659 \| 0.8000 \| 0.7707 \| 0.2086 \| 0.0947 \| \| 0.1059 \| 86.0 \| 1075 \| 0.7537 \| 0.8 \| 0.3179 \| 1.3687 \| 0.8000 \| 0.7698 \| 0.2001 \| 0.0955 \| \| 0.1059 \| 86.96 \| 1087 \| 0.7525 \| 0.8 \| 0.3172 \| 1.3693 \| 0.8000 \| 0.7696 \| 0.2096 \| 0.0947 \| \| 0.1059 \| 88.0 \| 1100 \| 0.7535 \| 0.8 \| 0.3175 \| 1.3675 \| 0.8000 \| 0.7704 \| 0.2091 \| 0.0951 \| \| 0.1059 \| 88.96 \| 1112 \| 0.7525 \| 0.8 \| 0.3172 \| 1.3680 \| 0.8000 \| 0.7696 \| 0.2009 \| 0.0946 \| \| 0.1059 \| 90.0 \| 1125 \| 0.7566 \| 0.8 \| 0.3190 \| 1.3725 \| 0.8000 \| 0.7698 \| 0.1881 \| 0.0964 \| \| 0.1059 \| 90.96 \| 1137 \| 0.7578 \| 0.8 \| 0.3195 \| 1.3726 \| 0.8000 \| 0.7704 \| 0.1880 \| 0.0968 \| \| 0.1059 \| 92.0 \| 1150 \| 0.7560 \| 0.8 \| 0.3186 \| 1.3715 \| 0.8000 \| 0.7707 \| 0.2095 \| 0.0969 \| \| 0.1059 \| 92.96 \| 1162 \| 0.7623 \| 0.795 \| 0.3219 \| 1.3765 \| 0.795 \| 0.7681 \| 0.1907 \| 0.0979 \| \| 0.1059 \| 94.0 \| 1175 \| 0.7567 \| 0.8 \| 0.3192 \| 1.3709 \| 0.8000 \| 0.7698 \| 0.2000 \| 0.0953 \| \| 0.1059 \| 94.96 \| 1187 \| 0.7538 \| 0.8 \| 0.3181 \| 1.3708 \| 0.8000 \| 0.7691 \| 0.1986 \| 0.0958 \| \| 0.1059 \| 96.0 \| 1200 \| 0.7530 \| 0.8 \| 0.3175 \| 1.3725 \| 0.8000 \| 0.7693 \| 0.2122 \| 0.0958 \| \| 0.1059 \| 96.96 \| 1212 \| 0.7607 \| 0.8 \| 0.3207 \| 1.3730 \| 0.8000 \| 0.7709 \| 0.1906 \| 0.0972 \| \| 0.1059 \| 98.0 \| 1225 \| 0.7647 \| 0.79 \| 0.3229 \| 1.3411 \| 0.79 \| 0.7633 \| 0.1889 \| 0.0984 \| \| 0.1059 \| 98.96 \| 1237 \| 0.7568 \| 0.79 \| 0.3191 \| 1.4425 \| 0.79 \| 0.7644 \| 0.1911 \| 0.0976 \| \| 0.1059 \| 100.0 \| 1250 \| 0.7586 \| 0.8 \| 0.3200 \| 1.4486 \| 0.8000 \| 0.7696 \| 0.2009 \| 0.0952 \| \| 0.1059 \| 100.96 \| 1262 \| 0.7552 \| 0.8 \| 0.3192 \| 1.3728 \| 0.8000 \| 0.7696 \| 0.1962 \| 0.0953 \| \| 0.1059 \| 102.0 \| 1275 \| 0.7601 \| 0.8 \| 0.3217 \| 1.4309 \| 0.8000 \| 0.7691 \| 0.2071 \| 0.0969 \| \| 0.1059 \| 102.96 \| 1287 \| 0.7608 \| 0.795 \| 0.3213 \| 1.3702 \| 0.795 \| 0.7668 \| 0.2068 \| 0.0967 \| \| 0.1059 \| 104.0 \| 1300 \| 0.7590 \| 0.795 \| 0.3202 \| 1.3748 \| 0.795 \| 0.7668 \| 0.1922 \| 0.0967 \| \| 0.1059 \| 104.96 \| 1312 \| 0.7626 \| 0.795 \| 0.3222 \| 1.3775 \| 0.795 \| 0.7673 \| 0.1917 \| 0.0974 \| \| 0.1059 \| 106.0 \| 1325 \| 0.7632 \| 0.795 \| 0.3228 \| 1.3765 \| 0.795 \| 0.7664 \| 0.2072 \| 0.0977 \| \| 0.1059 \| 106.96 \| 1337 \| 0.7612 \| 0.795 \| 0.3223 \| 1.3764 \| 0.795 \| 0.7666 \| 0.2011 \| 0.0974 \| \| 0.1059 \| 108.0 \| 1350 \| 0.7669 \| 0.79 \| 0.3246 \| 1.3777 \| 0.79 \| 0.7639 \| 0.2005 \| 0.0981 \| \| 0.1059 \| 108.96 \| 1362 \| 0.7658 \| 0.795 \| 0.3238 \| 1.3782 \| 0.795 \| 0.7673 \| 0.2099 \| 0.0983 \| \| 0.1059 \| 110.0 \| 1375 \| 0.7632 \| 0.79 \| 0.3232 \| 1.3766 \| 0.79 \| 0.7639 \| 0.2002 \| 0.0978 \| \| 0.1059 \| 110.96 \| 1387 \| 0.7651 \| 0.79 \| 0.3236 \| 1.3758 \| 0.79 \| 0.7580 \| 0.2134 \| 0.0980 \| \| 0.1059 \| 112.0 \| 1400 \| 0.7649 \| 0.79 \| 0.3235 \| 1.3765 \| 0.79 \| 0.7583 \| 0.1975 \| 0.0982 \| \| 0.1059 \| 112.96 \| 1412 \| 0.7683 \| 0.79 \| 0.3253 \| 1.3759 \| 0.79 \| 0.7639 \| 0.1913 \| 0.0986 \| \| 0.1059 \| 114.0 \| 1425 \| 0.7682 \| 0.795 \| 0.3253 \| 1.3778 \| 0.795 \| 0.7668 \| 0.1986 \| 0.0984 \| \| 0.1059 \| 114.96 \| 1437 \| 0.7666 \| 0.79 \| 0.3245 \| 1.3771 \| 0.79 \| 0.7639 \| 0.1838 \| 0.0976 \| \| 0.1059 \| 116.0 \| 1450 \| 0.7685 \| 0.79 \| 0.3255 \| 1.3802 \| 0.79 \| 0.7639 \| 0.1962 \| 0.0988 \| \| 0.1059 \| 116.96 \| 1462 \| 0.7676 \| 0.785 \| 0.3249 \| 1.3783 \| 0.785 \| 0.7554 \| 0.2011 \| 0.0991 \| \| 0.1059 \| 118.0 \| 1475 \| 0.7704 \| 0.785 \| 0.3262 \| 1.3789 \| 0.785 \| 0.7516 \| 0.2098 \| 0.0995 \| \| 0.1059 \| 118.96 \| 1487 \| 0.7701 \| 0.785 \| 0.3262 \| 1.3806 \| 0.785 \| 0.7554 \| 0.2167 \| 0.0991 \| \| 0.0842 \| 120.0 \| 1500 \| 0.7708 \| 0.79 \| 0.3267 \| 1.3815 \| 0.79 \| 0.7639 \| 0.1934 \| 0.0990 \| \| 0.0842 \| 120.96 \| 1512 \| 0.7710 \| 0.785 \| 0.3265 \| 1.3790 \| 0.785 \| 0.7516 \| 0.1928 \| 0.0995 \| \| 0.0842 \| 122.0 \| 1525 \| 0.7728 \| 0.79 \| 0.3274 \| 1.3830 \| 0.79 \| 0.7639 \| 0.1917 \| 0.0996 \| \| 0.0842 \| 122.96 \| 1537 \| 0.7708 \| 0.785 \| 0.3267 \| 1.3826 \| 0.785 \| 0.7554 \| 0.2057 \| 0.0992 \| \| 0.0842 \| 124.0 \| 1550 \| 0.7698 \| 0.785 \| 0.3262 \| 1.3777 \| 0.785 \| 0.7554 \| 0.2021 \| 0.0996 \| \| 0.0842 \| 124.96 \| 1562 \| 0.7706 \| 0.785 \| 0.3267 \| 1.3825 \| 0.785 \| 0.7554 \| 0.1949 \| 0.0994 \| \| 0.0842 \| 126.0 \| 1575 \| 0.7751 \| 0.79 \| 0.3290 \| 1.3827 \| 0.79 \| 0.7639 \| 0.1945 \| 0.1002 \| \| 0.0842 \| 126.96 \| 1587 \| 0.7734 \| 0.79 \| 0.3282 \| 1.3855 \| 0.79 \| 0.7639 \| 0.1882 \| 0.0998 \| \| 0.0842 \| 128.0 \| 1600 \| 0.7753 \| 0.785 \| 0.3290 \| 1.3842 \| 0.785 \| 0.7554 \| 0.1926 \| 0.1006 \| \| 0.0842 \| 128.96 \| 1612 \| 0.7731 \| 0.78 \| 0.3278 \| 1.3808 \| 0.78 \| 0.7488 \| 0.2115 \| 0.0994 \| \| 0.0842 \| 130.0 \| 1625 \| 0.7723 \| 0.78 \| 0.3276 \| 1.3830 \| 0.78 \| 0.7486 \| 0.2005 \| 0.0994 \| \| 0.0842 \| 130.96 \| 1637 \| 0.7746 \| 0.78 \| 0.3287 \| 1.3810 \| 0.78 \| 0.7488 \| 0.2103 \| 0.0999 \| \| 0.0842 \| 132.0 \| 1650 \| 0.7758 \| 0.78 \| 0.3291 \| 1.3848 \| 0.78 \| 0.7488 \| 0.2138 \| 0.1004 \| \| 0.0842 \| 132.96 \| 1662 \| 0.7771 \| 0.78 \| 0.3299 \| 1.3833 \| 0.78 \| 0.7488 \| 0.2080 \| 0.1007 \| \| 0.0842 \| 134.0 \| 1675 \| 0.7757 \| 0.78 \| 0.3295 \| 1.3837 \| 0.78 \| 0.7488 \| 0.2060 \| 0.0999 \| \| 0.0842 \| 134.96 \| 1687 \| 0.7754 \| 0.78 \| 0.3292 \| 1.3836 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1001 \| \| 0.0842 \| 136.0 \| 1700 \| 0.7755 \| 0.78 \| 0.3294 \| 1.3834 \| 0.78 \| 0.7488 \| 0.2057 \| 0.0999 \| \| 0.0842 \| 136.96 \| 1712 \| 0.7755 \| 0.78 \| 0.3295 \| 1.3874 \| 0.78 \| 0.7488 \| 0.1999 \| 0.0998 \| \| 0.0842 \| 138.0 \| 1725 \| 0.7759 \| 0.78 \| 0.3296 \| 1.4380 \| 0.78 \| 0.7488 \| 0.2007 \| 0.1002 \| \| 0.0842 \| 138.96 \| 1737 \| 0.7776 \| 0.78 \| 0.3303 \| 1.3868 \| 0.78 \| 0.7488 \| 0.2091 \| 0.1002 \| \| 0.0842 \| 140.0 \| 1750 \| 0.7780 \| 0.78 \| 0.3304 \| 1.3868 \| 0.78 \| 0.7488 \| 0.2086 \| 0.1005 \| \| 0.0842 \| 140.96 \| 1762 \| 0.7780 \| 0.78 \| 0.3306 \| 1.3855 \| 0.78 \| 0.7488 \| 0.2065 \| 0.1001 \| \| 0.0842 \| 142.0 \| 1775 \| 0.7789 \| 0.78 \| 0.3311 \| 1.3855 \| 0.78 \| 0.7488 \| 0.2067 \| 0.1003 \| \| 0.0842 \| 142.96 \| 1787 \| 0.7798 \| 0.78 \| 0.3314 \| 1.3856 \| 0.78 \| 0.7488 \| 0.2083 \| 0.1010 \| \| 0.0842 \| 144.0 \| 1800 \| 0.7799 \| 0.78 \| 0.3315 \| 1.3914 \| 0.78 \| 0.7488 \| 0.2161 \| 0.1004 \| \| 0.0842 \| 144.96 \| 1812 \| 0.7806 \| 0.78 \| 0.3317 \| 1.3857 \| 0.78 \| 0.7488 \| 0.2024 \| 0.1008 \| \| 0.0842 \| 146.0 \| 1825 \| 0.7817 \| 0.78 \| 0.3322 \| 1.3947 \| 0.78 \| 0.7488 \| 0.2043 \| 0.1009 \| \| 0.0842 \| 146.96 \| 1837 \| 0.7815 \| 0.78 \| 0.3324 \| 1.3898 \| 0.78 \| 0.7488 \| 0.2118 \| 0.1006 \| \| 0.0842 \| 148.0 \| 1850 \| 0.7820 \| 0.78 \| 0.3326 \| 1.3874 \| 0.78 \| 0.7488 \| 0.2114 \| 0.1008 \| \| 0.0842 \| 148.96 \| 1862 \| 0.7821 \| 0.78 \| 0.3327 \| 1.4391 \| 0.78 \| 0.7488 \| 0.2087 \| 0.1006 \| \| 0.0842 \| 150.0 \| 1875 \| 0.7816 \| 0.78 \| 0.3324 \| 1.4410 \| 0.78 \| 0.7488 \| 0.2160 \| 0.1006 \| \| 0.0842 \| 150.96 \| 1887 \| 0.7826 \| 0.78 \| 0.3328 \| 1.3927 \| 0.78 \| 0.7488 \| 0.2011 \| 0.1007 \| \| 0.0842 \| 152.0 \| 1900 \| 0.7834 \| 0.78 \| 0.3332 \| 1.4411 \| 0.78 \| 0.7488 \| 0.1994 \| 0.1009 \| \| 0.0842 \| 152.96 \| 1912 \| 0.7830 \| 0.78 \| 0.3331 \| 1.4409 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1008 \| \| 0.0842 \| 154.0 \| 1925 \| 0.7825 \| 0.78 \| 0.3329 \| 1.4412 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1008 \| \| 0.0842 \| 154.96 \| 1937 \| 0.7827 \| 0.78 \| 0.3332 \| 1.4399 \| 0.78 \| 0.7488 \| 0.2097 \| 0.1007 \| \| 0.0842 \| 156.0 \| 1950 \| 0.7822 \| 0.78 \| 0.3329 \| 1.4391 \| 0.78 \| 0.7488 \| 0.1986 \| 0.1006 \| \| 0.0842 \| 156.96 \| 1962 \| 0.7838 \| 0.78 \| 0.3335 \| 1.4414 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1007 \| \| 0.0842 \| 158.0 \| 1975 \| 0.7845 \| 0.78 \| 0.3337 \| 1.3899 \| 0.78 \| 0.7488 \| 0.2087 \| 0.1008 \| \| 0.0842 \| 158.96 \| 1987 \| 0.7846 \| 0.78 \| 0.3340 \| 1.4419 \| 0.78 \| 0.7488 \| 0.2002 \| 0.1007 \| \| 0.0731 \| 160.0 \| 2000 \| 0.7853 \| 0.78 \| 0.3343 \| 1.4419 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1010 \| \| 0.0731 \| 160.96 \| 2012 \| 0.7853 \| 0.78 \| 0.3343 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1963 \| 0.1012 \| \| 0.0731 \| 162.0 \| 2025 \| 0.7846 \| 0.78 \| 0.3341 \| 1.4411 \| 0.78 \| 0.7488 \| 0.1998 \| 0.1008 \| \| 0.0731 \| 162.96 \| 2037 \| 0.7856 \| 0.78 \| 0.3345 \| 1.4419 \| 0.78 \| 0.7488 \| 0.2000 \| 0.1011 \| \| 0.0731 \| 164.0 \| 2050 \| 0.7863 \| 0.78 \| 0.3348 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1991 \| 0.1014 \| \| 0.0731 \| 164.96 \| 2062 \| 0.7863 \| 0.78 \| 0.3349 \| 1.4414 \| 0.78 \| 0.7488 \| 0.1969 \| 0.1013 \| \| 0.0731 \| 166.0 \| 2075 \| 0.7865 \| 0.78 \| 0.3349 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1994 \| 0.1015 \| \| 0.0731 \| 166.96 \| 2087 \| 0.7863 \| 0.78 \| 0.3349 \| 1.4412 \| 0.78 \| 0.7488 \| 0.1966 \| 0.1015 \| \| 0.0731 \| 168.0 \| 2100 \| 0.7870 \| 0.78 \| 0.3352 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1944 \| 0.1016 \| \| 0.0731 \| 168.96 \| 2112 \| 0.7868 \| 0.78 \| 0.3351 \| 1.4425 \| 0.78 \| 0.7488 \| 0.1919 \| 0.1017 \| \| 0.0731 \| 170.0 \| 2125 \| 0.7873 \| 0.78 \| 0.3354 \| 1.4424 \| 0.78 \| 0.7488 \| 0.1965 \| 0.1017 \| \| 0.0731 \| 170.96 \| 2137 \| 0.7870 \| 0.78 \| 0.3352 \| 1.4418 \| 0.78 \| 0.7488 \| 0.2081 \| 0.1014 \| \| 0.0731 \| 172.0 \| 2150 \| 0.7879 \| 0.78 \| 0.3356 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 172.96 \| 2162 \| 0.7874 \| 0.78 \| 0.3355 \| 1.4421 \| 0.78 \| 0.7488 \| 0.1967 \| 0.1015 \| \| 0.0731 \| 174.0 \| 2175 \| 0.7872 \| 0.78 \| 0.3355 \| 1.4418 \| 0.78 \| 0.7488 \| 0.1965 \| 0.1016 \| \| 0.0731 \| 174.96 \| 2187 \| 0.7882 \| 0.78 \| 0.3358 \| 1.4433 \| 0.78 \| 0.7488 \| 0.1945 \| 0.1017 \| \| 0.0731 \| 176.0 \| 2200 \| 0.7882 \| 0.78 \| 0.3358 \| 1.4423 \| 0.78 \| 0.7488 \| 0.1969 \| 0.1016 \| \| 0.0731 \| 176.96 \| 2212 \| 0.7885 \| 0.78 \| 0.3361 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1948 \| 0.1017 \| \| 0.0731 \| 178.0 \| 2225 \| 0.7883 \| 0.78 \| 0.3359 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 178.96 \| 2237 \| 0.7882 \| 0.78 \| 0.3359 \| 1.4426 \| 0.78 \| 0.7488 \| 0.1920 \| 0.1017 \| \| 0.0731 \| 180.0 \| 2250 \| 0.7884 \| 0.78 \| 0.3360 \| 1.4425 \| 0.78 \| 0.7488 \| 0.2000 \| 0.1016 \| \| 0.0731 \| 180.96 \| 2262 \| 0.7886 \| 0.78 \| 0.3361 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 182.0 \| 2275 \| 0.7888 \| 0.78 \| 0.3362 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1977 \| 0.1016 \| \| 0.0731 \| 182.96 \| 2287 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4428 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 184.0 \| 2300 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 184.96 \| 2312 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4427 \| 0.78 \| 0.7488 \| 0.1946 \| 0.1017 \| \| 0.0731 \| 186.0 \| 2325 \| 0.7889 \| 0.78 \| 0.3362 \| 1.4431 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1018 \| \| 0.0731 \| 186.96 \| 2337 \| 0.7892 \| 0.78 \| 0.3364 \| 1.4432 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 188.0 \| 2350 \| 0.7890 \| 0.78 \| 0.3363 \| 1.4427 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 188.96 \| 2362 \| 0.7892 \| 0.78 \| 0.3364 \| 1.4429 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1017 \| \| 0.0731 \| 190.0 \| 2375 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4433 \| 0.78 \| 0.7488 \| 0.1945 \| 0.1017 \| \| 0.0731 \| 190.96 \| 2387 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4430 \| 0.78 \| 0.7488 \| 0.1921 \| 0.1017 \| \| 0.0731 \| 192.0 \| 2400 \| 0.7893 \| 0.78 \| 0.3364 \| 1.4430 \| 0.78 \| 0.7488 \| 0.1922 \| 0.1018 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/vit-base_tobacco_wr_0.01_wd_0.2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9831 - Accuracy: 0.755 - Brier Loss: 0.3936 - Nll: 1.4572 - F1 Micro: 0.755 - F1 Macro: 0.7245 - Ece: 0.2101 - Aurc: 0.1059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7384 \| 0.815 \| 0.3056 \| 1.1863 \| 0.815 \| 0.8021 \| 0.2211 \| 0.0789 \| \| No log \| 2.0 \| 25 \| 0.7504 \| 0.795 \| 0.3109 \| 1.2594 \| 0.795 \| 0.7691 \| 0.2235 \| 0.0789 \| \| No log \| 2.96 \| 37 \| 0.7415 \| 0.765 \| 0.3109 \| 1.0445 \| 0.765 \| 0.7422 \| 0.2323 \| 0.0762 \| \| No log \| 4.0 \| 50 \| 0.7222 \| 0.805 \| 0.2989 \| 1.3200 \| 0.805 \| 0.7951 \| 0.2028 \| 0.0682 \| \| No log \| 4.96 \| 62 \| 0.7160 \| 0.815 \| 0.2989 \| 1.2307 \| 0.815 \| 0.8067 \| 0.2381 \| 0.0688 \| \| No log \| 6.0 \| 75 \| 0.7149 \| 0.825 \| 0.2950 \| 1.3097 \| 0.825 \| 0.8127 \| 0.2110 \| 0.0809 \| \| No log \| 6.96 \| 87 \| 0.7157 \| 0.815 \| 0.2986 \| 1.2310 \| 0.815 \| 0.8007 \| 0.2195 \| 0.0779 \| \| No log \| 8.0 \| 100 \| 0.7245 \| 0.81 \| 0.3020 \| 1.3030 \| 0.81 \| 0.7949 \| 0.1997 \| 0.0834 \| \| No log \| 8.96 \| 112 \| 0.7111 \| 0.815 \| 0.2972 \| 1.2310 \| 0.815 \| 0.8009 \| 0.2090 \| 0.0793 \| \| No log \| 10.0 \| 125 \| 0.7344 \| 0.81 \| 0.3087 \| 1.2974 \| 0.81 \| 0.7956 \| 0.2240 \| 0.0913 \| \| No log \| 10.96 \| 137 \| 0.7264 \| 0.81 \| 0.3057 \| 1.3216 \| 0.81 \| 0.7907 \| 0.2074 \| 0.0879 \| \| No log \| 12.0 \| 150 \| 0.7301 \| 0.805 \| 0.3056 \| 1.2448 \| 0.805 \| 0.7919 \| 0.2112 \| 0.0932 \| \| No log \| 12.96 \| 162 \| 0.7224 \| 0.805 \| 0.3020 \| 1.2950 \| 0.805 \| 0.7899 \| 0.1930 \| 0.0915 \| \| No log \| 14.0 \| 175 \| 0.7235 \| 0.81 \| 0.3045 \| 1.2965 \| 0.81 \| 0.7956 \| 0.2025 \| 0.0902 \| \| No log \| 14.96 \| 187 \| 0.7239 \| 0.81 \| 0.3039 \| 1.2970 \| 0.81 \| 0.7956 \| 0.2156 \| 0.0906 \| \| No log \| 16.0 \| 200 \| 0.7293 \| 0.81 \| 0.3060 \| 1.4336 \| 0.81 \| 0.7924 \| 0.2029 \| 0.0955 \| \| No log \| 16.96 \| 212 \| 0.7253 \| 0.805 \| 0.3044 \| 1.2984 \| 0.805 \| 0.7853 \| 0.1946 \| 0.0911 \| \| No log \| 18.0 \| 225 \| 0.7293 \| 0.805 \| 0.3076 \| 1.1852 \| 0.805 \| 0.7861 \| 0.2015 \| 0.0916 \| \| No log \| 18.96 \| 237 \| 0.7328 \| 0.8 \| 0.3050 \| 1.3844 \| 0.8000 \| 0.7836 \| 0.1900 \| 0.0986 \| \| No log \| 20.0 \| 250 \| 0.7263 \| 0.8 \| 0.3058 \| 1.2943 \| 0.8000 \| 0.7754 \| 0.2003 \| 0.0900 \| \| No log \| 20.96 \| 262 \| 0.7370 \| 0.805 \| 0.3102 \| 1.3612 \| 0.805 \| 0.7818 \| 0.1998 \| 0.0939 \| \| No log \| 22.0 \| 275 \| 0.7412 \| 0.795 \| 0.3118 \| 1.3673 \| 0.795 \| 0.7714 \| 0.1952 \| 0.0957 \| \| No log \| 22.96 \| 287 \| 0.7326 \| 0.795 \| 0.3077 \| 1.3572 \| 0.795 \| 0.7666 \| 0.1935 \| 0.0925 \| \| No log \| 24.0 \| 300 \| 0.7308 \| 0.805 \| 0.3076 \| 1.4265 \| 0.805 \| 0.7818 \| 0.1968 \| 0.0906 \| \| No log \| 24.96 \| 312 \| 0.7424 \| 0.8 \| 0.3122 \| 1.3666 \| 0.8000 \| 0.7774 \| 0.1853 \| 0.0960 \| \| No log \| 26.0 \| 325 \| 0.7383 \| 0.8 \| 0.3097 \| 1.3644 \| 0.8000 \| 0.7774 \| 0.1911 \| 0.0952 \| \| No log \| 26.96 \| 337 \| 0.7468 \| 0.805 \| 0.3143 \| 1.4295 \| 0.805 \| 0.7829 \| 0.1952 \| 0.0971 \| \| No log \| 28.0 \| 350 \| 0.7484 \| 0.795 \| 0.3135 \| 1.4327 \| 0.795 \| 0.7668 \| 0.1906 \| 0.0989 \| \| No log \| 28.96 \| 362 \| 0.7459 \| 0.8 \| 0.3135 \| 1.3624 \| 0.8000 \| 0.7704 \| 0.1945 \| 0.0944 \| \| No log \| 30.0 \| 375 \| 0.7513 \| 0.8 \| 0.3160 \| 1.4282 \| 0.8000 \| 0.7698 \| 0.1991 \| 0.0953 \| \| No log \| 30.96 \| 387 \| 0.7535 \| 0.795 \| 0.3168 \| 1.4336 \| 0.795 \| 0.7656 \| 0.1799 \| 0.0980 \| \| No log \| 32.0 \| 400 \| 0.7540 \| 0.8 \| 0.3171 \| 1.4469 \| 0.8000 \| 0.7774 \| 0.1943 \| 0.0959 \| \| No log \| 32.96 \| 412 \| 0.7566 \| 0.8 \| 0.3180 \| 1.3772 \| 0.8000 \| 0.7704 \| 0.1809 \| 0.0972 \| \| No log \| 34.0 \| 425 \| 0.7641 \| 0.795 \| 0.3220 \| 1.3878 \| 0.795 \| 0.7691 \| 0.1937 \| 0.1021 \| \| No log \| 34.96 \| 437 \| 0.7602 \| 0.795 \| 0.3192 \| 1.3748 \| 0.795 \| 0.7656 \| 0.1908 \| 0.0973 \| \| No log \| 36.0 \| 450 \| 0.7577 \| 0.8 \| 0.3186 \| 1.4397 \| 0.8000 \| 0.7704 \| 0.1858 \| 0.0951 \| \| No log \| 36.96 \| 462 \| 0.7724 \| 0.795 \| 0.3233 \| 1.3800 \| 0.795 \| 0.7656 \| 0.1796 \| 0.1002 \| \| No log \| 38.0 \| 475 \| 0.7675 \| 0.795 \| 0.3223 \| 1.3659 \| 0.795 \| 0.7654 \| 0.1862 \| 0.0976 \| \| No log \| 38.96 \| 487 \| 0.7772 \| 0.79 \| 0.3261 \| 1.4406 \| 0.79 \| 0.7633 \| 0.1877 \| 0.1062 \| \| 0.0949 \| 40.0 \| 500 \| 0.7631 \| 0.795 \| 0.3211 \| 1.4302 \| 0.795 \| 0.7654 \| 0.1896 \| 0.0953 \| \| 0.0949 \| 40.96 \| 512 \| 0.7878 \| 0.79 \| 0.3304 \| 1.5689 \| 0.79 \| 0.7631 \| 0.1770 \| 0.1014 \| \| 0.0949 \| 42.0 \| 525 \| 0.7639 \| 0.815 \| 0.3198 \| 1.5524 \| 0.815 \| 0.7879 \| 0.1890 \| 0.0938 \| \| 0.0949 \| 42.96 \| 537 \| 0.7967 \| 0.785 \| 0.3350 \| 1.4516 \| 0.785 \| 0.7606 \| 0.1809 \| 0.1040 \| \| 0.0949 \| 44.0 \| 550 \| 0.7735 \| 0.81 \| 0.3237 \| 1.4915 \| 0.81 \| 0.7784 \| 0.2079 \| 0.0963 \| \| 0.0949 \| 44.96 \| 562 \| 0.7859 \| 0.79 \| 0.3300 \| 1.5076 \| 0.79 \| 0.7594 \| 0.1851 \| 0.0991 \| \| 0.0949 \| 46.0 \| 575 \| 0.7917 \| 0.79 \| 0.3323 \| 1.5062 \| 0.79 \| 0.7631 \| 0.1944 \| 0.1015 \| \| 0.0949 \| 46.96 \| 587 \| 0.7879 \| 0.795 \| 0.3310 \| 1.4304 \| 0.795 \| 0.7656 \| 0.1761 \| 0.0987 \| \| 0.0949 \| 48.0 \| 600 \| 0.7917 \| 0.79 \| 0.3320 \| 1.4382 \| 0.79 \| 0.7629 \| 0.1828 \| 0.0994 \| \| 0.0949 \| 48.96 \| 612 \| 0.7950 \| 0.79 \| 0.3333 \| 1.4929 \| 0.79 \| 0.7549 \| 0.1811 \| 0.0995 \| \| 0.0949 \| 50.0 \| 625 \| 0.8005 \| 0.79 \| 0.3363 \| 1.4351 \| 0.79 \| 0.7631 \| 0.1953 \| 0.1009 \| \| 0.0949 \| 50.96 \| 637 \| 0.8019 \| 0.79 \| 0.3363 \| 1.4891 \| 0.79 \| 0.7631 \| 0.1837 \| 0.0998 \| \| 0.0949 \| 52.0 \| 650 \| 0.8021 \| 0.79 \| 0.3354 \| 1.4899 \| 0.79 \| 0.7586 \| 0.1990 \| 0.0994 \| \| 0.0949 \| 52.96 \| 662 \| 0.8012 \| 0.795 \| 0.3346 \| 1.4913 \| 0.795 \| 0.7648 \| 0.1878 \| 0.0984 \| \| 0.0949 \| 54.0 \| 675 \| 0.8078 \| 0.785 \| 0.3388 \| 1.4920 \| 0.785 \| 0.7559 \| 0.1938 \| 0.1000 \| \| 0.0949 \| 54.96 \| 687 \| 0.8092 \| 0.785 \| 0.3391 \| 1.5003 \| 0.785 \| 0.7561 \| 0.1878 \| 0.1004 \| \| 0.0949 \| 56.0 \| 700 \| 0.8095 \| 0.795 \| 0.3378 \| 1.4914 \| 0.795 \| 0.7634 \| 0.1879 \| 0.0994 \| \| 0.0949 \| 56.96 \| 712 \| 0.8124 \| 0.785 \| 0.3396 \| 1.4926 \| 0.785 \| 0.7555 \| 0.1996 \| 0.1005 \| \| 0.0949 \| 58.0 \| 725 \| 0.8120 \| 0.78 \| 0.3391 \| 1.4405 \| 0.78 \| 0.7469 \| 0.1924 \| 0.1005 \| \| 0.0949 \| 58.96 \| 737 \| 0.8185 \| 0.785 \| 0.3406 \| 1.5007 \| 0.785 \| 0.7523 \| 0.1910 \| 0.1013 \| \| 0.0949 \| 60.0 \| 750 \| 0.8182 \| 0.785 \| 0.3421 \| 1.4371 \| 0.785 \| 0.7555 \| 0.1727 \| 0.1010 \| \| 0.0949 \| 60.96 \| 762 \| 0.8224 \| 0.78 \| 0.3443 \| 1.4404 \| 0.78 \| 0.7475 \| 0.1941 \| 0.1019 \| \| 0.0949 \| 62.0 \| 775 \| 0.8267 \| 0.78 \| 0.3463 \| 1.4995 \| 0.78 \| 0.7535 \| 0.1927 \| 0.1016 \| \| 0.0949 \| 62.96 \| 787 \| 0.8252 \| 0.775 \| 0.3447 \| 1.4965 \| 0.775 \| 0.7465 \| 0.1798 \| 0.1016 \| \| 0.0949 \| 64.0 \| 800 \| 0.8286 \| 0.78 \| 0.3446 \| 1.4987 \| 0.78 \| 0.7475 \| 0.1911 \| 0.1014 \| \| 0.0949 \| 64.96 \| 812 \| 0.8308 \| 0.78 \| 0.3467 \| 1.4438 \| 0.78 \| 0.7469 \| 0.1866 \| 0.1019 \| \| 0.0949 \| 66.0 \| 825 \| 0.8346 \| 0.775 \| 0.3476 \| 1.4592 \| 0.775 \| 0.7444 \| 0.1878 \| 0.1034 \| \| 0.0949 \| 66.96 \| 837 \| 0.8348 \| 0.78 \| 0.3484 \| 1.4423 \| 0.78 \| 0.7515 \| 0.1821 \| 0.1021 \| \| 0.0949 \| 68.0 \| 850 \| 0.8376 \| 0.78 \| 0.3481 \| 1.4970 \| 0.78 \| 0.7469 \| 0.1922 \| 0.1015 \| \| 0.0949 \| 68.96 \| 862 \| 0.8379 \| 0.78 \| 0.3497 \| 1.4388 \| 0.78 \| 0.7515 \| 0.1850 \| 0.1018 \| \| 0.0949 \| 70.0 \| 875 \| 0.8452 \| 0.78 \| 0.3512 \| 1.4974 \| 0.78 \| 0.7525 \| 0.1828 \| 0.1036 \| \| 0.0949 \| 70.96 \| 887 \| 0.8437 \| 0.78 \| 0.3511 \| 1.4519 \| 0.78 \| 0.7469 \| 0.2076 \| 0.1032 \| \| 0.0949 \| 72.0 \| 900 \| 0.8485 \| 0.775 \| 0.3535 \| 1.5163 \| 0.775 \| 0.7444 \| 0.1863 \| 0.1033 \| \| 0.0949 \| 72.96 \| 912 \| 0.8516 \| 0.77 \| 0.3555 \| 1.5015 \| 0.7700 \| 0.7404 \| 0.1868 \| 0.1030 \| \| 0.0949 \| 74.0 \| 925 \| 0.8507 \| 0.77 \| 0.3541 \| 1.4407 \| 0.7700 \| 0.7404 \| 0.1996 \| 0.1026 \| \| 0.0949 \| 74.96 \| 937 \| 0.8508 \| 0.77 \| 0.3540 \| 1.4424 \| 0.7700 \| 0.7400 \| 0.1996 \| 0.1025 \| \| 0.0949 \| 76.0 \| 950 \| 0.8559 \| 0.77 \| 0.3558 \| 1.4487 \| 0.7700 \| 0.7400 \| 0.2001 \| 0.1031 \| \| 0.0949 \| 76.96 \| 962 \| 0.8564 \| 0.77 \| 0.3568 \| 1.4389 \| 0.7700 \| 0.7400 \| 0.1804 \| 0.1024 \| \| 0.0949 \| 78.0 \| 975 \| 0.8611 \| 0.775 \| 0.3569 \| 1.4966 \| 0.775 \| 0.7440 \| 0.1831 \| 0.1031 \| \| 0.0949 \| 78.96 \| 987 \| 0.8573 \| 0.77 \| 0.3557 \| 1.4946 \| 0.7700 \| 0.7400 \| 0.1933 \| 0.1013 \| \| 0.0366 \| 80.0 \| 1000 \| 0.8640 \| 0.775 \| 0.3587 \| 1.4998 \| 0.775 \| 0.7440 \| 0.1871 \| 0.1032 \| \| 0.0366 \| 80.96 \| 1012 \| 0.8640 \| 0.77 \| 0.3585 \| 1.4983 \| 0.7700 \| 0.7400 \| 0.1921 \| 0.1032 \| \| 0.0366 \| 82.0 \| 1025 \| 0.8696 \| 0.77 \| 0.3608 \| 1.5032 \| 0.7700 \| 0.7400 \| 0.2033 \| 0.1036 \| \| 0.0366 \| 82.96 \| 1037 \| 0.8702 \| 0.77 \| 0.3613 \| 1.4987 \| 0.7700 \| 0.7400 \| 0.2022 \| 0.1029 \| \| 0.0366 \| 84.0 \| 1050 \| 0.8686 \| 0.77 \| 0.3597 \| 1.4446 \| 0.7700 \| 0.7400 \| 0.1887 \| 0.1028 \| \| 0.0366 \| 84.96 \| 1062 \| 0.8700 \| 0.77 \| 0.3607 \| 1.4365 \| 0.7700 \| 0.7400 \| 0.1900 \| 0.1025 \| \| 0.0366 \| 86.0 \| 1075 \| 0.8756 \| 0.765 \| 0.3621 \| 1.5009 \| 0.765 \| 0.7308 \| 0.1983 \| 0.1040 \| \| 0.0366 \| 86.96 \| 1087 \| 0.8768 \| 0.76 \| 0.3623 \| 1.5035 \| 0.76 \| 0.7282 \| 0.1956 \| 0.1040 \| \| 0.0366 \| 88.0 \| 1100 \| 0.8762 \| 0.765 \| 0.3618 \| 1.4409 \| 0.765 \| 0.7308 \| 0.1957 \| 0.1033 \| \| 0.0366 \| 88.96 \| 1112 \| 0.8777 \| 0.765 \| 0.3629 \| 1.4427 \| 0.765 \| 0.7308 \| 0.1974 \| 0.1036 \| \| 0.0366 \| 90.0 \| 1125 \| 0.8854 \| 0.76 \| 0.3661 \| 1.4681 \| 0.76 \| 0.7282 \| 0.2023 \| 0.1049 \| \| 0.0366 \| 90.96 \| 1137 \| 0.8867 \| 0.76 \| 0.3660 \| 1.5057 \| 0.76 \| 0.7282 \| 0.1995 \| 0.1049 \| \| 0.0366 \| 92.0 \| 1150 \| 0.8849 \| 0.765 \| 0.3648 \| 1.5011 \| 0.765 \| 0.7308 \| 0.1953 \| 0.1039 \| \| 0.0366 \| 92.96 \| 1162 \| 0.8898 \| 0.76 \| 0.3670 \| 1.5077 \| 0.76 \| 0.7282 \| 0.2083 \| 0.1045 \| \| 0.0366 \| 94.0 \| 1175 \| 0.8891 \| 0.765 \| 0.3662 \| 1.4520 \| 0.765 \| 0.7308 \| 0.2091 \| 0.1040 \| \| 0.0366 \| 94.96 \| 1187 \| 0.8910 \| 0.755 \| 0.3679 \| 1.4460 \| 0.755 \| 0.7247 \| 0.2039 \| 0.1043 \| \| 0.0366 \| 96.0 \| 1200 \| 0.8935 \| 0.76 \| 0.3684 \| 1.4435 \| 0.76 \| 0.7273 \| 0.1929 \| 0.1041 \| \| 0.0366 \| 96.96 \| 1212 \| 0.8964 \| 0.755 \| 0.3689 \| 1.4526 \| 0.755 \| 0.7247 \| 0.1980 \| 0.1048 \| \| 0.0366 \| 98.0 \| 1225 \| 0.8979 \| 0.755 \| 0.3701 \| 1.4507 \| 0.755 \| 0.7247 \| 0.2017 \| 0.1045 \| \| 0.0366 \| 98.96 \| 1237 \| 0.8965 \| 0.755 \| 0.3692 \| 1.4474 \| 0.755 \| 0.7247 \| 0.2057 \| 0.1044 \| \| 0.0366 \| 100.0 \| 1250 \| 0.9019 \| 0.755 \| 0.3716 \| 1.4526 \| 0.755 \| 0.7247 \| 0.2051 \| 0.1047 \| \| 0.0366 \| 100.96 \| 1262 \| 0.8994 \| 0.755 \| 0.3694 \| 1.4485 \| 0.755 \| 0.7247 \| 0.1979 \| 0.1041 \| \| 0.0366 \| 102.0 \| 1275 \| 0.9023 \| 0.755 \| 0.3715 \| 1.4465 \| 0.755 \| 0.7247 \| 0.2140 \| 0.1044 \| \| 0.0366 \| 102.96 \| 1287 \| 0.9048 \| 0.755 \| 0.3720 \| 1.4472 \| 0.755 \| 0.7247 \| 0.2066 \| 0.1045 \| \| 0.0366 \| 104.0 \| 1300 \| 0.9060 \| 0.755 \| 0.3719 \| 1.4565 \| 0.755 \| 0.7247 \| 0.2003 \| 0.1044 \| \| 0.0366 \| 104.96 \| 1312 \| 0.9105 \| 0.755 \| 0.3735 \| 1.4625 \| 0.755 \| 0.7247 \| 0.2094 \| 0.1053 \| \| 0.0366 \| 106.0 \| 1325 \| 0.9099 \| 0.76 \| 0.3738 \| 1.4463 \| 0.76 \| 0.7273 \| 0.2050 \| 0.1044 \| \| 0.0366 \| 106.96 \| 1337 \| 0.9111 \| 0.755 \| 0.3751 \| 1.4486 \| 0.755 \| 0.7247 \| 0.2173 \| 0.1041 \| \| 0.0366 \| 108.0 \| 1350 \| 0.9149 \| 0.755 \| 0.3745 \| 1.5081 \| 0.755 \| 0.7247 \| 0.2062 \| 0.1052 \| \| 0.0366 \| 108.96 \| 1362 \| 0.9146 \| 0.755 \| 0.3744 \| 1.4513 \| 0.755 \| 0.7247 \| 0.2073 \| 0.1047 \| \| 0.0366 \| 110.0 \| 1375 \| 0.9157 \| 0.755 \| 0.3764 \| 1.4486 \| 0.755 \| 0.7247 \| 0.2094 \| 0.1046 \| \| 0.0366 \| 110.96 \| 1387 \| 0.9201 \| 0.755 \| 0.3767 \| 1.4529 \| 0.755 \| 0.7247 \| 0.2093 \| 0.1051 \| \| 0.0366 \| 112.0 \| 1400 \| 0.9190 \| 0.755 \| 0.3759 \| 1.4533 \| 0.755 \| 0.7247 \| 0.2020 \| 0.1049 \| \| 0.0366 \| 112.96 \| 1412 \| 0.9230 \| 0.755 \| 0.3782 \| 1.4529 \| 0.755 \| 0.7247 \| 0.2054 \| 0.1050 \| \| 0.0366 \| 114.0 \| 1425 \| 0.9234 \| 0.755 \| 0.3778 \| 1.4505 \| 0.755 \| 0.7247 \| 0.2009 \| 0.1049 \| \| 0.0366 \| 114.96 \| 1437 \| 0.9238 \| 0.755 \| 0.3780 \| 1.4469 \| 0.755 \| 0.7247 \| 0.1974 \| 0.1051 \| \| 0.0366 \| 116.0 \| 1450 \| 0.9264 \| 0.755 \| 0.3786 \| 1.4538 \| 0.755 \| 0.7247 \| 0.2016 \| 0.1051 \| \| 0.0366 \| 116.96 \| 1462 \| 0.9275 \| 0.755 \| 0.3787 \| 1.4553 \| 0.755 \| 0.7247 \| 0.2012 \| 0.1054 \| \| 0.0366 \| 118.0 \| 1475 \| 0.9306 \| 0.755 \| 0.3798 \| 1.4595 \| 0.755 \| 0.7247 \| 0.2139 \| 0.1057 \| \| 0.0366 \| 118.96 \| 1487 \| 0.9291 \| 0.76 \| 0.3789 \| 1.4529 \| 0.76 \| 0.7273 \| 0.2014 \| 0.1054 \| \| 0.0214 \| 120.0 \| 1500 \| 0.9318 \| 0.755 \| 0.3804 \| 1.4520 \| 0.755 \| 0.7247 \| 0.2017 \| 0.1050 \| \| 0.0214 \| 120.96 \| 1512 \| 0.9325 \| 0.755 \| 0.3796 \| 1.4535 \| 0.755 \| 0.7247 \| 0.2087 \| 0.1053 \| \| 0.0214 \| 122.0 \| 1525 \| 0.9339 \| 0.755 \| 0.3804 \| 1.4534 \| 0.755 \| 0.7247 \| 0.2068 \| 0.1056 \| \| 0.0214 \| 122.96 \| 1537 \| 0.9342 \| 0.755 \| 0.3807 \| 1.4519 \| 0.755 \| 0.7247 \| 0.1986 \| 0.1052 \| \| 0.0214 \| 124.0 \| 1550 \| 0.9357 \| 0.755 \| 0.3808 \| 1.4524 \| 0.755 \| 0.7247 \| 0.2058 \| 0.1054 \| \| 0.0214 \| 124.96 \| 1562 \| 0.9360 \| 0.755 \| 0.3808 \| 1.4514 \| 0.755 \| 0.7247 \| 0.2061 \| 0.1052 \| \| 0.0214 \| 126.0 \| 1575 \| 0.9409 \| 0.755 \| 0.3828 \| 1.4557 \| 0.755 \| 0.7247 \| 0.2044 \| 0.1058 \| \| 0.0214 \| 126.96 \| 1587 \| 0.9390 \| 0.755 \| 0.3819 \| 1.4523 \| 0.755 \| 0.7247 \| 0.2062 \| 0.1052 \| \| 0.0214 \| 128.0 \| 1600 \| 0.9425 \| 0.755 \| 0.3833 \| 1.4559 \| 0.755 \| 0.7247 \| 0.2049 \| 0.1058 \| \| 0.0214 \| 128.96 \| 1612 \| 0.9421 \| 0.755 \| 0.3824 \| 1.4534 \| 0.755 \| 0.7247 \| 0.1978 \| 0.1055 \| \| 0.0214 \| 130.0 \| 1625 \| 0.9433 \| 0.755 \| 0.3831 \| 1.4530 \| 0.755 \| 0.7247 \| 0.2069 \| 0.1056 \| \| 0.0214 \| 130.96 \| 1637 \| 0.9463 \| 0.755 \| 0.3842 \| 1.4535 \| 0.755 \| 0.7247 \| 0.2127 \| 0.1058 \| \| 0.0214 \| 132.0 \| 1650 \| 0.9462 \| 0.755 \| 0.3835 \| 1.4546 \| 0.755 \| 0.7247 \| 0.1975 \| 0.1057 \| \| 0.0214 \| 132.96 \| 1662 \| 0.9479 \| 0.755 \| 0.3842 \| 1.4562 \| 0.755 \| 0.7247 \| 0.2012 \| 0.1059 \| \| 0.0214 \| 134.0 \| 1675 \| 0.9493 \| 0.755 \| 0.3852 \| 1.4547 \| 0.755 \| 0.7247 \| 0.2161 \| 0.1057 \| \| 0.0214 \| 134.96 \| 1687 \| 0.9484 \| 0.755 \| 0.3842 \| 1.4563 \| 0.755 \| 0.7247 \| 0.2046 \| 0.1055 \| \| 0.0214 \| 136.0 \| 1700 \| 0.9500 \| 0.755 \| 0.3849 \| 1.4528 \| 0.755 \| 0.7247 \| 0.2081 \| 0.1058 \| \| 0.0214 \| 136.96 \| 1712 \| 0.9510 \| 0.755 \| 0.3854 \| 1.4510 \| 0.755 \| 0.7247 \| 0.1976 \| 0.1053 \| \| 0.0214 \| 138.0 \| 1725 \| 0.9519 \| 0.755 \| 0.3855 \| 1.4516 \| 0.755 \| 0.7247 \| 0.2090 \| 0.1056 \| \| 0.0214 \| 138.96 \| 1737 \| 0.9535 \| 0.755 \| 0.3857 \| 1.4537 \| 0.755 \| 0.7247 \| 0.2091 \| 0.1057 \| \| 0.0214 \| 140.0 \| 1750 \| 0.9546 \| 0.755 \| 0.3862 \| 1.4557 \| 0.755 \| 0.7247 \| 0.2020 \| 0.1057 \| \| 0.0214 \| 140.96 \| 1762 \| 0.9558 \| 0.755 \| 0.3867 \| 1.4530 \| 0.755 \| 0.7247 \| 0.2018 \| 0.1059 \| \| 0.0214 \| 142.0 \| 1775 \| 0.9567 \| 0.755 \| 0.3870 \| 1.4522 \| 0.755 \| 0.7247 \| 0.2059 \| 0.1055 \| \| 0.0214 \| 142.96 \| 1787 \| 0.9589 \| 0.755 \| 0.3876 \| 1.4555 \| 0.755 \| 0.7247 \| 0.2063 \| 0.1060 \| \| 0.0214 \| 144.0 \| 1800 \| 0.9584 \| 0.755 \| 0.3872 \| 1.4568 \| 0.755 \| 0.7245 \| 0.2162 \| 0.1056 \| \| 0.0214 \| 144.96 \| 1812 \| 0.9613 \| 0.755 \| 0.3881 \| 1.4594 \| 0.755 \| 0.7247 \| 0.2085 \| 0.1063 \| \| 0.0214 \| 146.0 \| 1825 \| 0.9604 \| 0.755 \| 0.3873 \| 1.5132 \| 0.755 \| 0.7247 \| 0.2047 \| 0.1056 \| \| 0.0214 \| 146.96 \| 1837 \| 0.9627 \| 0.755 \| 0.3887 \| 1.4573 \| 0.755 \| 0.7247 \| 0.2107 \| 0.1059 \| \| 0.0214 \| 148.0 \| 1850 \| 0.9643 \| 0.755 \| 0.3890 \| 1.4570 \| 0.755 \| 0.7247 \| 0.2041 \| 0.1065 \| \| 0.0214 \| 148.96 \| 1862 \| 0.9633 \| 0.755 \| 0.3886 \| 1.4526 \| 0.755 \| 0.7247 \| 0.2085 \| 0.1055 \| \| 0.0214 \| 150.0 \| 1875 \| 0.9637 \| 0.755 \| 0.3887 \| 1.4551 \| 0.755 \| 0.7245 \| 0.2096 \| 0.1054 \| \| 0.0214 \| 150.96 \| 1887 \| 0.9645 \| 0.755 \| 0.3886 \| 1.4534 \| 0.755 \| 0.7247 \| 0.2100 \| 0.1058 \| \| 0.0214 \| 152.0 \| 1900 \| 0.9661 \| 0.755 \| 0.3891 \| 1.4550 \| 0.755 \| 0.7247 \| 0.2072 \| 0.1060 \| \| 0.0214 \| 152.96 \| 1912 \| 0.9665 \| 0.755 \| 0.3894 \| 1.4543 \| 0.755 \| 0.7247 \| 0.2092 \| 0.1057 \| \| 0.0214 \| 154.0 \| 1925 \| 0.9667 \| 0.755 \| 0.3894 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2019 \| 0.1056 \| \| 0.0214 \| 154.96 \| 1937 \| 0.9681 \| 0.755 \| 0.3900 \| 1.4537 \| 0.755 \| 0.7245 \| 0.2098 \| 0.1059 \| \| 0.0214 \| 156.0 \| 1950 \| 0.9689 \| 0.755 \| 0.3903 \| 1.4533 \| 0.755 \| 0.7245 \| 0.2022 \| 0.1056 \| \| 0.0214 \| 156.96 \| 1962 \| 0.9689 \| 0.755 \| 0.3898 \| 1.4553 \| 0.755 \| 0.7247 \| 0.2074 \| 0.1058 \| \| 0.0214 \| 158.0 \| 1975 \| 0.9705 \| 0.755 \| 0.3901 \| 1.4566 \| 0.755 \| 0.7247 \| 0.2072 \| 0.1062 \| \| 0.0214 \| 158.96 \| 1987 \| 0.9721 \| 0.755 \| 0.3911 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2055 \| 0.1062 \| \| 0.0155 \| 160.0 \| 2000 \| 0.9712 \| 0.755 \| 0.3904 \| 1.4551 \| 0.755 \| 0.7245 \| 0.2070 \| 0.1060 \| \| 0.0155 \| 160.96 \| 2012 \| 0.9721 \| 0.755 \| 0.3906 \| 1.4563 \| 0.755 \| 0.7245 \| 0.2108 \| 0.1059 \| \| 0.0155 \| 162.0 \| 2025 \| 0.9734 \| 0.755 \| 0.3914 \| 1.4550 \| 0.755 \| 0.7245 \| 0.2071 \| 0.1057 \| \| 0.0155 \| 162.96 \| 2037 \| 0.9740 \| 0.755 \| 0.3913 \| 1.4565 \| 0.755 \| 0.7245 \| 0.2073 \| 0.1062 \| \| 0.0155 \| 164.0 \| 2050 \| 0.9744 \| 0.755 \| 0.3915 \| 1.4563 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 164.96 \| 2062 \| 0.9752 \| 0.755 \| 0.3917 \| 1.4558 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1059 \| \| 0.0155 \| 166.0 \| 2075 \| 0.9755 \| 0.755 \| 0.3916 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1062 \| \| 0.0155 \| 166.96 \| 2087 \| 0.9755 \| 0.755 \| 0.3917 \| 1.4542 \| 0.755 \| 0.7245 \| 0.2100 \| 0.1057 \| \| 0.0155 \| 168.0 \| 2100 \| 0.9768 \| 0.755 \| 0.3921 \| 1.4578 \| 0.755 \| 0.7245 \| 0.2103 \| 0.1059 \| \| 0.0155 \| 168.96 \| 2112 \| 0.9767 \| 0.755 \| 0.3919 \| 1.4575 \| 0.755 \| 0.7245 \| 0.2106 \| 0.1057 \| \| 0.0155 \| 170.0 \| 2125 \| 0.9776 \| 0.755 \| 0.3922 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 170.96 \| 2137 \| 0.9783 \| 0.755 \| 0.3925 \| 1.4560 \| 0.755 \| 0.7245 \| 0.2080 \| 0.1062 \| \| 0.0155 \| 172.0 \| 2150 \| 0.9793 \| 0.755 \| 0.3927 \| 1.4578 \| 0.755 \| 0.7245 \| 0.2102 \| 0.1064 \| \| 0.0155 \| 172.96 \| 2162 \| 0.9783 \| 0.755 \| 0.3923 \| 1.4557 \| 0.755 \| 0.7245 \| 0.2098 \| 0.1058 \| \| 0.0155 \| 174.0 \| 2175 \| 0.9790 \| 0.755 \| 0.3927 \| 1.4550 \| 0.755 \| 0.7245 \| 0.2087 \| 0.1057 \| \| 0.0155 \| 174.96 \| 2187 \| 0.9801 \| 0.755 \| 0.3929 \| 1.4577 \| 0.755 \| 0.7245 \| 0.2100 \| 0.1059 \| \| 0.0155 \| 176.0 \| 2200 \| 0.9800 \| 0.755 \| 0.3928 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1058 \| \| 0.0155 \| 176.96 \| 2212 \| 0.9809 \| 0.755 \| 0.3932 \| 1.4568 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 178.0 \| 2225 \| 0.9808 \| 0.755 \| 0.3930 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 178.96 \| 2237 \| 0.9808 \| 0.755 \| 0.3930 \| 1.4566 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1057 \| \| 0.0155 \| 180.0 \| 2250 \| 0.9813 \| 0.755 \| 0.3931 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1058 \| \| 0.0155 \| 180.96 \| 2262 \| 0.9817 \| 0.755 \| 0.3933 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2032 \| 0.1059 \| \| 0.0155 \| 182.0 \| 2275 \| 0.9819 \| 0.755 \| 0.3933 \| 1.4570 \| 0.755 \| 0.7245 \| 0.2103 \| 0.1058 \| \| 0.0155 \| 182.96 \| 2287 \| 0.9822 \| 0.755 \| 0.3934 \| 1.4567 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1058 \| \| 0.0155 \| 184.0 \| 2300 \| 0.9824 \| 0.755 \| 0.3934 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 184.96 \| 2312 \| 0.9825 \| 0.755 \| 0.3935 \| 1.4569 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1060 \| \| 0.0155 \| 186.0 \| 2325 \| 0.9825 \| 0.755 \| 0.3934 \| 1.4574 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 186.96 \| 2337 \| 0.9829 \| 0.755 \| 0.3935 \| 1.4571 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 188.0 \| 2350 \| 0.9830 \| 0.755 \| 0.3936 \| 1.4569 \| 0.755 \| 0.7245 \| 0.2081 \| 0.1060 \| \| 0.0155 \| 188.96 \| 2362 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2082 \| 0.1059 \| \| 0.0155 \| 190.0 \| 2375 \| 0.9830 \| 0.755 \| 0.3935 \| 1.4575 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 190.96 \| 2387 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| \| 0.0155 \| 192.0 \| 2400 \| 0.9831 \| 0.755 \| 0.3936 \| 1.4572 \| 0.755 \| 0.7245 \| 0.2101 \| 0.1059 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0644 - Accuracy: 0.86 - Brier Loss: 0.2705 - Nll: 1.3085 - F1 Micro: 0.8600 - F1 Macro: 0.8552 - Ece: 0.1378 - Aurc: 0.0461 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7869 \| 0.78 \| 0.3223 \| 1.3413 \| 0.78 \| 0.7402 \| 0.2269 \| 0.0782 \| \| No log \| 2.0 \| 25 \| 0.9889 \| 0.715 \| 0.4300 \| 1.9648 \| 0.715 \| 0.6881 \| 0.2669 \| 0.1397 \| \| No log \| 2.96 \| 37 \| 0.7053 \| 0.82 \| 0.2995 \| 1.2578 \| 0.82 \| 0.8270 \| 0.2253 \| 0.0758 \| \| No log \| 4.0 \| 50 \| 0.7535 \| 0.78 \| 0.3225 \| 1.3427 \| 0.78 \| 0.7395 \| 0.2159 \| 0.0616 \| \| No log \| 4.96 \| 62 \| 0.8538 \| 0.775 \| 0.3634 \| 1.5684 \| 0.775 \| 0.7523 \| 0.2181 \| 0.1149 \| \| No log \| 6.0 \| 75 \| 0.7825 \| 0.77 \| 0.3557 \| 1.3406 \| 0.7700 \| 0.7663 \| 0.2136 \| 0.0625 \| \| No log \| 6.96 \| 87 \| 1.0777 \| 0.67 \| 0.4896 \| 1.5465 \| 0.67 \| 0.6728 \| 0.2540 \| 0.1106 \| \| No log \| 8.0 \| 100 \| 1.1030 \| 0.73 \| 0.4453 \| 2.5744 \| 0.7300 \| 0.6939 \| 0.2294 \| 0.1423 \| \| No log \| 8.96 \| 112 \| 1.0215 \| 0.725 \| 0.4339 \| 2.0485 \| 0.7250 \| 0.7012 \| 0.2348 \| 0.1278 \| \| No log \| 10.0 \| 125 \| 0.7940 \| 0.795 \| 0.3378 \| 1.3057 \| 0.795 \| 0.7911 \| 0.1828 \| 0.0750 \| \| No log \| 10.96 \| 137 \| 0.7648 \| 0.82 \| 0.2963 \| 1.3907 \| 0.82 \| 0.8022 \| 0.1597 \| 0.0744 \| \| No log \| 12.0 \| 150 \| 1.0755 \| 0.74 \| 0.4383 \| 2.1271 \| 0.74 \| 0.7182 \| 0.2281 \| 0.0847 \| \| No log \| 12.96 \| 162 \| 1.0091 \| 0.775 \| 0.3856 \| 1.7383 \| 0.775 \| 0.7339 \| 0.1969 \| 0.1029 \| \| No log \| 14.0 \| 175 \| 1.0531 \| 0.77 \| 0.4027 \| 1.5532 \| 0.7700 \| 0.7592 \| 0.2152 \| 0.0888 \| \| No log \| 14.96 \| 187 \| 1.0221 \| 0.77 \| 0.4027 \| 1.5199 \| 0.7700 \| 0.7259 \| 0.2059 \| 0.1031 \| \| No log \| 16.0 \| 200 \| 1.1795 \| 0.735 \| 0.4435 \| 2.0739 \| 0.735 \| 0.7063 \| 0.2262 \| 0.1305 \| \| No log \| 16.96 \| 212 \| 1.1560 \| 0.745 \| 0.4379 \| 2.0155 \| 0.745 \| 0.7240 \| 0.2207 \| 0.1273 \| \| No log \| 18.0 \| 225 \| 1.0635 \| 0.76 \| 0.4159 \| 1.5491 \| 0.76 \| 0.7508 \| 0.2124 \| 0.0879 \| \| No log \| 18.96 \| 237 \| 1.2639 \| 0.73 \| 0.4649 \| 1.9828 \| 0.7300 \| 0.7276 \| 0.2298 \| 0.1079 \| \| No log \| 20.0 \| 250 \| 1.0598 \| 0.78 \| 0.3866 \| 1.5139 \| 0.78 \| 0.7676 \| 0.1885 \| 0.0914 \| \| No log \| 20.96 \| 262 \| 0.8900 \| 0.81 \| 0.3241 \| 1.8355 \| 0.81 \| 0.7925 \| 0.1691 \| 0.0648 \| \| No log \| 22.0 \| 275 \| 1.0617 \| 0.79 \| 0.3788 \| 1.8951 \| 0.79 \| 0.7783 \| 0.1893 \| 0.0676 \| \| No log \| 22.96 \| 287 \| 1.0362 \| 0.785 \| 0.3646 \| 1.9399 \| 0.785 \| 0.7653 \| 0.1914 \| 0.0816 \| \| No log \| 24.0 \| 300 \| 1.1701 \| 0.775 \| 0.4060 \| 2.1593 \| 0.775 \| 0.7718 \| 0.2114 \| 0.0842 \| \| No log \| 24.96 \| 312 \| 1.0841 \| 0.79 \| 0.3799 \| 1.8773 \| 0.79 \| 0.7795 \| 0.2016 \| 0.0775 \| \| No log \| 26.0 \| 325 \| 1.0064 \| 0.785 \| 0.3650 \| 1.7371 \| 0.785 \| 0.7674 \| 0.1915 \| 0.0813 \| \| No log \| 26.96 \| 337 \| 0.8886 \| 0.825 \| 0.3114 \| 1.4858 \| 0.825 \| 0.8116 \| 0.1609 \| 0.0636 \| \| No log \| 28.0 \| 350 \| 1.1174 \| 0.8 \| 0.3751 \| 1.9584 \| 0.8000 \| 0.7869 \| 0.1928 \| 0.0930 \| \| No log \| 28.96 \| 362 \| 1.0922 \| 0.8 \| 0.3672 \| 1.8702 \| 0.8000 \| 0.7673 \| 0.1954 \| 0.0771 \| \| No log \| 30.0 \| 375 \| 1.0281 \| 0.805 \| 0.3506 \| 1.6105 \| 0.805 \| 0.7809 \| 0.1773 \| 0.0936 \| \| No log \| 30.96 \| 387 \| 0.9041 \| 0.82 \| 0.3210 \| 1.3323 \| 0.82 \| 0.8148 \| 0.1627 \| 0.0651 \| \| No log \| 32.0 \| 400 \| 1.1018 \| 0.79 \| 0.3804 \| 1.9928 \| 0.79 \| 0.7962 \| 0.1859 \| 0.0574 \| \| No log \| 32.96 \| 412 \| 1.1973 \| 0.765 \| 0.4156 \| 1.4304 \| 0.765 \| 0.7682 \| 0.2147 \| 0.0760 \| \| No log \| 34.0 \| 425 \| 1.0216 \| 0.805 \| 0.3605 \| 1.4476 \| 0.805 \| 0.7864 \| 0.1830 \| 0.0633 \| \| No log \| 34.96 \| 437 \| 1.2356 \| 0.755 \| 0.4237 \| 1.9897 \| 0.755 \| 0.7350 \| 0.2214 \| 0.0890 \| \| No log \| 36.0 \| 450 \| 1.0881 \| 0.8 \| 0.3757 \| 1.3848 \| 0.8000 \| 0.7810 \| 0.1960 \| 0.0703 \| \| No log \| 36.96 \| 462 \| 1.1133 \| 0.795 \| 0.3687 \| 2.0286 \| 0.795 \| 0.7707 \| 0.1790 \| 0.0652 \| \| No log \| 38.0 \| 475 \| 1.1243 \| 0.78 \| 0.3839 \| 1.5683 \| 0.78 \| 0.7699 \| 0.1905 \| 0.0704 \| \| No log \| 38.96 \| 487 \| 1.1351 \| 0.785 \| 0.3983 \| 1.4970 \| 0.785 \| 0.7647 \| 0.1969 \| 0.0666 \| \| 0.0934 \| 40.0 \| 500 \| 1.2551 \| 0.775 \| 0.4089 \| 2.0438 \| 0.775 \| 0.7688 \| 0.2082 \| 0.1007 \| \| 0.0934 \| 40.96 \| 512 \| 1.1739 \| 0.775 \| 0.4003 \| 1.3286 \| 0.775 \| 0.7654 \| 0.2056 \| 0.0819 \| \| 0.0934 \| 42.0 \| 525 \| 1.0007 \| 0.83 \| 0.3207 \| 1.2576 \| 0.83 \| 0.8345 \| 0.1579 \| 0.0677 \| \| 0.0934 \| 42.96 \| 537 \| 1.0509 \| 0.805 \| 0.3580 \| 1.2330 \| 0.805 \| 0.7933 \| 0.1884 \| 0.0716 \| \| 0.0934 \| 44.0 \| 550 \| 1.0830 \| 0.805 \| 0.3537 \| 1.7652 \| 0.805 \| 0.7871 \| 0.1740 \| 0.0688 \| \| 0.0934 \| 44.96 \| 562 \| 0.8544 \| 0.83 \| 0.2957 \| 1.4716 \| 0.83 \| 0.8039 \| 0.1560 \| 0.0532 \| \| 0.0934 \| 46.0 \| 575 \| 1.0803 \| 0.815 \| 0.3549 \| 1.5802 \| 0.815 \| 0.7951 \| 0.1840 \| 0.0691 \| \| 0.0934 \| 46.96 \| 587 \| 0.9441 \| 0.815 \| 0.3318 \| 1.2883 \| 0.815 \| 0.7924 \| 0.1709 \| 0.0514 \| \| 0.0934 \| 48.0 \| 600 \| 0.9007 \| 0.845 \| 0.2765 \| 1.3443 \| 0.845 \| 0.8353 \| 0.1402 \| 0.0539 \| \| 0.0934 \| 48.96 \| 612 \| 0.9601 \| 0.84 \| 0.2952 \| 1.4755 \| 0.8400 \| 0.8306 \| 0.1499 \| 0.0565 \| \| 0.0934 \| 50.0 \| 625 \| 0.9801 \| 0.84 \| 0.2992 \| 1.4646 \| 0.8400 \| 0.8306 \| 0.1529 \| 0.0559 \| \| 0.0934 \| 50.96 \| 637 \| 0.9747 \| 0.845 \| 0.2950 \| 1.4544 \| 0.845 \| 0.8338 \| 0.1526 \| 0.0546 \| \| 0.0934 \| 52.0 \| 650 \| 0.9651 \| 0.845 \| 0.2895 \| 1.4442 \| 0.845 \| 0.8338 \| 0.1469 \| 0.0537 \| \| 0.0934 \| 52.96 \| 662 \| 0.9583 \| 0.85 \| 0.2848 \| 1.4367 \| 0.85 \| 0.8370 \| 0.1465 \| 0.0525 \| \| 0.0934 \| 54.0 \| 675 \| 0.9534 \| 0.85 \| 0.2805 \| 1.4300 \| 0.85 \| 0.8370 \| 0.1455 \| 0.0514 \| \| 0.0934 \| 54.96 \| 687 \| 0.9503 \| 0.855 \| 0.2776 \| 1.4252 \| 0.855 \| 0.8425 \| 0.1408 \| 0.0510 \| \| 0.0934 \| 56.0 \| 700 \| 0.9480 \| 0.855 \| 0.2754 \| 1.4207 \| 0.855 \| 0.8425 \| 0.1407 \| 0.0506 \| \| 0.0934 \| 56.96 \| 712 \| 0.9471 \| 0.855 \| 0.2739 \| 1.4175 \| 0.855 \| 0.8425 \| 0.1442 \| 0.0504 \| \| 0.0934 \| 58.0 \| 725 \| 0.9471 \| 0.855 \| 0.2729 \| 1.4147 \| 0.855 \| 0.8442 \| 0.1435 \| 0.0501 \| \| 0.0934 \| 58.96 \| 737 \| 0.9474 \| 0.855 \| 0.2720 \| 1.4125 \| 0.855 \| 0.8442 \| 0.1432 \| 0.0497 \| \| 0.0934 \| 60.0 \| 750 \| 0.9482 \| 0.855 \| 0.2713 \| 1.4101 \| 0.855 \| 0.8442 \| 0.1420 \| 0.0497 \| \| 0.0934 \| 60.96 \| 762 \| 0.9490 \| 0.855 \| 0.2708 \| 1.4082 \| 0.855 \| 0.8442 \| 0.1421 \| 0.0493 \| \| 0.0934 \| 62.0 \| 775 \| 0.9500 \| 0.86 \| 0.2703 \| 1.4063 \| 0.8600 \| 0.8534 \| 0.1411 \| 0.0493 \| \| 0.0934 \| 62.96 \| 787 \| 0.9512 \| 0.86 \| 0.2702 \| 1.4046 \| 0.8600 \| 0.8534 \| 0.1410 \| 0.0492 \| \| 0.0934 \| 64.0 \| 800 \| 0.9528 \| 0.86 \| 0.2699 \| 1.4032 \| 0.8600 \| 0.8534 \| 0.1408 \| 0.0489 \| \| 0.0934 \| 64.96 \| 812 \| 0.9541 \| 0.86 \| 0.2697 \| 1.3472 \| 0.8600 \| 0.8534 \| 0.1349 \| 0.0487 \| \| 0.0934 \| 66.0 \| 825 \| 0.9558 \| 0.86 \| 0.2696 \| 1.3431 \| 0.8600 \| 0.8534 \| 0.1408 \| 0.0487 \| \| 0.0934 \| 66.96 \| 837 \| 0.9574 \| 0.86 \| 0.2697 \| 1.3403 \| 0.8600 \| 0.8534 \| 0.1405 \| 0.0486 \| \| 0.0934 \| 68.0 \| 850 \| 0.9591 \| 0.86 \| 0.2698 \| 1.3375 \| 0.8600 \| 0.8534 \| 0.1402 \| 0.0486 \| \| 0.0934 \| 68.96 \| 862 \| 0.9605 \| 0.86 \| 0.2698 \| 1.3355 \| 0.8600 \| 0.8552 \| 0.1394 \| 0.0486 \| \| 0.0934 \| 70.0 \| 875 \| 0.9624 \| 0.86 \| 0.2698 \| 1.3338 \| 0.8600 \| 0.8552 \| 0.1394 \| 0.0486 \| \| 0.0934 \| 70.96 \| 887 \| 0.9638 \| 0.86 \| 0.2700 \| 1.3322 \| 0.8600 \| 0.8552 \| 0.1397 \| 0.0485 \| \| 0.0934 \| 72.0 \| 900 \| 0.9657 \| 0.86 \| 0.2701 \| 1.3310 \| 0.8600 \| 0.8552 \| 0.1397 \| 0.0485 \| \| 0.0934 \| 72.96 \| 912 \| 0.9673 \| 0.86 \| 0.2702 \| 1.3299 \| 0.8600 \| 0.8552 \| 0.1397 \| 0.0484 \| \| 0.0934 \| 74.0 \| 925 \| 0.9691 \| 0.86 \| 0.2703 \| 1.3289 \| 0.8600 \| 0.8552 \| 0.1397 \| 0.0484 \| \| 0.0934 \| 74.96 \| 937 \| 0.9708 \| 0.86 \| 0.2704 \| 1.3280 \| 0.8600 \| 0.8552 \| 0.1398 \| 0.0485 \| \| 0.0934 \| 76.0 \| 950 \| 0.9725 \| 0.86 \| 0.2706 \| 1.3271 \| 0.8600 \| 0.8552 \| 0.1398 \| 0.0485 \| \| 0.0934 \| 76.96 \| 962 \| 0.9740 \| 0.86 \| 0.2707 \| 1.3263 \| 0.8600 \| 0.8552 \| 0.1398 \| 0.0485 \| \| 0.0934 \| 78.0 \| 975 \| 0.9757 \| 0.86 \| 0.2707 \| 1.3256 \| 0.8600 \| 0.8552 \| 0.1383 \| 0.0485 \| \| 0.0934 \| 78.96 \| 987 \| 0.9772 \| 0.86 \| 0.2708 \| 1.3248 \| 0.8600 \| 0.8552 \| 0.1357 \| 0.0484 \| \| 0.0038 \| 80.0 \| 1000 \| 0.9789 \| 0.86 \| 0.2709 \| 1.3243 \| 0.8600 \| 0.8552 \| 0.1359 \| 0.0485 \| \| 0.0038 \| 80.96 \| 1012 \| 0.9806 \| 0.86 \| 0.2710 \| 1.3238 \| 0.8600 \| 0.8552 \| 0.1360 \| 0.0484 \| \| 0.0038 \| 82.0 \| 1025 \| 0.9820 \| 0.86 \| 0.2711 \| 1.3232 \| 0.8600 \| 0.8552 \| 0.1361 \| 0.0482 \| \| 0.0038 \| 82.96 \| 1037 \| 0.9837 \| 0.86 \| 0.2712 \| 1.3227 \| 0.8600 \| 0.8552 \| 0.1361 \| 0.0481 \| \| 0.0038 \| 84.0 \| 1050 \| 0.9853 \| 0.86 \| 0.2713 \| 1.3222 \| 0.8600 \| 0.8552 \| 0.1362 \| 0.0480 \| \| 0.0038 \| 84.96 \| 1062 \| 0.9867 \| 0.86 \| 0.2713 \| 1.3216 \| 0.8600 \| 0.8552 \| 0.1363 \| 0.0481 \| \| 0.0038 \| 86.0 \| 1075 \| 0.9883 \| 0.86 \| 0.2714 \| 1.3212 \| 0.8600 \| 0.8552 \| 0.1364 \| 0.0479 \| \| 0.0038 \| 86.96 \| 1087 \| 0.9896 \| 0.86 \| 0.2714 \| 1.3208 \| 0.8600 \| 0.8552 \| 0.1365 \| 0.0477 \| \| 0.0038 \| 88.0 \| 1100 \| 0.9911 \| 0.86 \| 0.2715 \| 1.3203 \| 0.8600 \| 0.8552 \| 0.1366 \| 0.0478 \| \| 0.0038 \| 88.96 \| 1112 \| 0.9925 \| 0.86 \| 0.2715 \| 1.3200 \| 0.8600 \| 0.8552 \| 0.1369 \| 0.0478 \| \| 0.0038 \| 90.0 \| 1125 \| 0.9940 \| 0.86 \| 0.2715 \| 1.3196 \| 0.8600 \| 0.8552 \| 0.1369 \| 0.0477 \| \| 0.0038 \| 90.96 \| 1137 \| 0.9954 \| 0.86 \| 0.2715 \| 1.3194 \| 0.8600 \| 0.8552 \| 0.1369 \| 0.0476 \| \| 0.0038 \| 92.0 \| 1150 \| 0.9968 \| 0.86 \| 0.2716 \| 1.3190 \| 0.8600 \| 0.8552 \| 0.1368 \| 0.0476 \| \| 0.0038 \| 92.96 \| 1162 \| 0.9983 \| 0.86 \| 0.2716 \| 1.3187 \| 0.8600 \| 0.8552 \| 0.1368 \| 0.0476 \| \| 0.0038 \| 94.0 \| 1175 \| 0.9996 \| 0.86 \| 0.2716 \| 1.3184 \| 0.8600 \| 0.8552 \| 0.1394 \| 0.0476 \| \| 0.0038 \| 94.96 \| 1187 \| 1.0009 \| 0.86 \| 0.2716 \| 1.3182 \| 0.8600 \| 0.8552 \| 0.1393 \| 0.0475 \| \| 0.0038 \| 96.0 \| 1200 \| 1.0023 \| 0.86 \| 0.2717 \| 1.3179 \| 0.8600 \| 0.8552 \| 0.1392 \| 0.0475 \| \| 0.0038 \| 96.96 \| 1212 \| 1.0035 \| 0.86 \| 0.2717 \| 1.3176 \| 0.8600 \| 0.8552 \| 0.1391 \| 0.0475 \| \| 0.0038 \| 98.0 \| 1225 \| 1.0049 \| 0.86 \| 0.2717 \| 1.3175 \| 0.8600 \| 0.8552 \| 0.1391 \| 0.0474 \| \| 0.0038 \| 98.96 \| 1237 \| 1.0062 \| 0.86 \| 0.2717 \| 1.3172 \| 0.8600 \| 0.8552 \| 0.1391 \| 0.0475 \| \| 0.0038 \| 100.0 \| 1250 \| 1.0075 \| 0.86 \| 0.2717 \| 1.3169 \| 0.8600 \| 0.8552 \| 0.1367 \| 0.0475 \| \| 0.0038 \| 100.96 \| 1262 \| 1.0087 \| 0.86 \| 0.2717 \| 1.3167 \| 0.8600 \| 0.8552 \| 0.1368 \| 0.0475 \| \| 0.0038 \| 102.0 \| 1275 \| 1.0099 \| 0.86 \| 0.2717 \| 1.3164 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0474 \| \| 0.0038 \| 102.96 \| 1287 \| 1.0111 \| 0.86 \| 0.2717 \| 1.3162 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0473 \| \| 0.0038 \| 104.0 \| 1300 \| 1.0122 \| 0.86 \| 0.2717 \| 1.3159 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0471 \| \| 0.0038 \| 104.96 \| 1312 \| 1.0134 \| 0.86 \| 0.2716 \| 1.3158 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0473 \| \| 0.0038 \| 106.0 \| 1325 \| 1.0146 \| 0.86 \| 0.2717 \| 1.3155 \| 0.8600 \| 0.8552 \| 0.1379 \| 0.0472 \| \| 0.0038 \| 106.96 \| 1337 \| 1.0158 \| 0.86 \| 0.2717 \| 1.3153 \| 0.8600 \| 0.8552 \| 0.1379 \| 0.0471 \| \| 0.0038 \| 108.0 \| 1350 \| 1.0169 \| 0.86 \| 0.2716 \| 1.3151 \| 0.8600 \| 0.8552 \| 0.1380 \| 0.0471 \| \| 0.0038 \| 108.96 \| 1362 \| 1.0180 \| 0.86 \| 0.2716 \| 1.3149 \| 0.8600 \| 0.8552 \| 0.1381 \| 0.0471 \| \| 0.0038 \| 110.0 \| 1375 \| 1.0191 \| 0.86 \| 0.2716 \| 1.3146 \| 0.8600 \| 0.8552 \| 0.1381 \| 0.0471 \| \| 0.0038 \| 110.96 \| 1387 \| 1.0201 \| 0.86 \| 0.2716 \| 1.3144 \| 0.8600 \| 0.8552 \| 0.1382 \| 0.0471 \| \| 0.0038 \| 112.0 \| 1400 \| 1.0211 \| 0.86 \| 0.2716 \| 1.3142 \| 0.8600 \| 0.8552 \| 0.1382 \| 0.0470 \| \| 0.0038 \| 112.96 \| 1412 \| 1.0222 \| 0.86 \| 0.2716 \| 1.3141 \| 0.8600 \| 0.8552 \| 0.1382 \| 0.0471 \| \| 0.0038 \| 114.0 \| 1425 \| 1.0233 \| 0.86 \| 0.2715 \| 1.3139 \| 0.8600 \| 0.8552 \| 0.1383 \| 0.0470 \| \| 0.0038 \| 114.96 \| 1437 \| 1.0242 \| 0.86 \| 0.2715 \| 1.3138 \| 0.8600 \| 0.8552 \| 0.1383 \| 0.0470 \| \| 0.0038 \| 116.0 \| 1450 \| 1.0253 \| 0.86 \| 0.2715 \| 1.3136 \| 0.8600 \| 0.8552 \| 0.1383 \| 0.0469 \| \| 0.0038 \| 116.96 \| 1462 \| 1.0263 \| 0.86 \| 0.2715 \| 1.3134 \| 0.8600 \| 0.8552 \| 0.1383 \| 0.0470 \| \| 0.0038 \| 118.0 \| 1475 \| 1.0273 \| 0.86 \| 0.2715 \| 1.3133 \| 0.8600 \| 0.8552 \| 0.1384 \| 0.0470 \| \| 0.0038 \| 118.96 \| 1487 \| 1.0282 \| 0.86 \| 0.2714 \| 1.3131 \| 0.8600 \| 0.8552 \| 0.1384 \| 0.0468 \| \| 0.0006 \| 120.0 \| 1500 \| 1.0292 \| 0.86 \| 0.2714 \| 1.3130 \| 0.8600 \| 0.8552 \| 0.1385 \| 0.0468 \| \| 0.0006 \| 120.96 \| 1512 \| 1.0301 \| 0.86 \| 0.2714 \| 1.3128 \| 0.8600 \| 0.8552 \| 0.1385 \| 0.0468 \| \| 0.0006 \| 122.0 \| 1525 \| 1.0311 \| 0.86 \| 0.2714 \| 1.3127 \| 0.8600 \| 0.8552 \| 0.1386 \| 0.0467 \| \| 0.0006 \| 122.96 \| 1537 \| 1.0319 \| 0.86 \| 0.2714 \| 1.3126 \| 0.8600 \| 0.8552 \| 0.1386 \| 0.0467 \| \| 0.0006 \| 124.0 \| 1550 \| 1.0329 \| 0.86 \| 0.2714 \| 1.3124 \| 0.8600 \| 0.8552 \| 0.1387 \| 0.0467 \| \| 0.0006 \| 124.96 \| 1562 \| 1.0337 \| 0.86 \| 0.2713 \| 1.3123 \| 0.8600 \| 0.8552 \| 0.1393 \| 0.0467 \| \| 0.0006 \| 126.0 \| 1575 \| 1.0346 \| 0.86 \| 0.2713 \| 1.3122 \| 0.8600 \| 0.8552 \| 0.1374 \| 0.0466 \| \| 0.0006 \| 126.96 \| 1587 \| 1.0354 \| 0.86 \| 0.2713 \| 1.3120 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0466 \| \| 0.0006 \| 128.0 \| 1600 \| 1.0363 \| 0.86 \| 0.2713 \| 1.3119 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0467 \| \| 0.0006 \| 128.96 \| 1612 \| 1.0372 \| 0.86 \| 0.2713 \| 1.3118 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0466 \| \| 0.0006 \| 130.0 \| 1625 \| 1.0380 \| 0.86 \| 0.2712 \| 1.3117 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0466 \| \| 0.0006 \| 130.96 \| 1637 \| 1.0388 \| 0.86 \| 0.2712 \| 1.3116 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0467 \| \| 0.0006 \| 132.0 \| 1650 \| 1.0396 \| 0.86 \| 0.2712 \| 1.3115 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0465 \| \| 0.0006 \| 132.96 \| 1662 \| 1.0403 \| 0.86 \| 0.2712 \| 1.3113 \| 0.8600 \| 0.8552 \| 0.1375 \| 0.0466 \| \| 0.0006 \| 134.0 \| 1675 \| 1.0411 \| 0.86 \| 0.2712 \| 1.3113 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0466 \| \| 0.0006 \| 134.96 \| 1687 \| 1.0419 \| 0.86 \| 0.2711 \| 1.3112 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0466 \| \| 0.0006 \| 136.0 \| 1700 \| 1.0426 \| 0.86 \| 0.2711 \| 1.3111 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0465 \| \| 0.0006 \| 136.96 \| 1712 \| 1.0433 \| 0.86 \| 0.2711 \| 1.3110 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0465 \| \| 0.0006 \| 138.0 \| 1725 \| 1.0441 \| 0.86 \| 0.2711 \| 1.3109 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0465 \| \| 0.0006 \| 138.96 \| 1737 \| 1.0448 \| 0.86 \| 0.2711 \| 1.3108 \| 0.8600 \| 0.8552 \| 0.1376 \| 0.0465 \| \| 0.0006 \| 140.0 \| 1750 \| 1.0455 \| 0.86 \| 0.2710 \| 1.3107 \| 0.8600 \| 0.8552 \| 0.1377 \| 0.0465 \| \| 0.0006 \| 140.96 \| 1762 \| 1.0461 \| 0.86 \| 0.2710 \| 1.3106 \| 0.8600 \| 0.8552 \| 0.1377 \| 0.0465 \| \| 0.0006 \| 142.0 \| 1775 \| 1.0468 \| 0.86 \| 0.2710 \| 1.3106 \| 0.8600 \| 0.8552 \| 0.1377 \| 0.0465 \| \| 0.0006 \| 142.96 \| 1787 \| 1.0474 \| 0.86 \| 0.2710 \| 1.3105 \| 0.8600 \| 0.8552 \| 0.1377 \| 0.0465 \| \| 0.0006 \| 144.0 \| 1800 \| 1.0481 \| 0.86 \| 0.2710 \| 1.3104 \| 0.8600 \| 0.8552 \| 0.1377 \| 0.0465 \| \| 0.0006 \| 144.96 \| 1812 \| 1.0487 \| 0.86 \| 0.2710 \| 1.3103 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0465 \| \| 0.0006 \| 146.0 \| 1825 \| 1.0494 \| 0.86 \| 0.2709 \| 1.3102 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0465 \| \| 0.0006 \| 146.96 \| 1837 \| 1.0500 \| 0.86 \| 0.2709 \| 1.3102 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0465 \| \| 0.0006 \| 148.0 \| 1850 \| 1.0506 \| 0.86 \| 0.2709 \| 1.3101 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0465 \| \| 0.0006 \| 148.96 \| 1862 \| 1.0511 \| 0.86 \| 0.2709 \| 1.3100 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 150.0 \| 1875 \| 1.0517 \| 0.86 \| 0.2709 \| 1.3099 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 150.96 \| 1887 \| 1.0523 \| 0.86 \| 0.2709 \| 1.3099 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 152.0 \| 1900 \| 1.0529 \| 0.86 \| 0.2708 \| 1.3098 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 152.96 \| 1912 \| 1.0534 \| 0.86 \| 0.2708 \| 1.3097 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 154.0 \| 1925 \| 1.0539 \| 0.86 \| 0.2708 \| 1.3096 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 154.96 \| 1937 \| 1.0544 \| 0.86 \| 0.2708 \| 1.3096 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 156.0 \| 1950 \| 1.0550 \| 0.86 \| 0.2708 \| 1.3095 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 156.96 \| 1962 \| 1.0554 \| 0.86 \| 0.2708 \| 1.3094 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 158.0 \| 1975 \| 1.0559 \| 0.86 \| 0.2707 \| 1.3094 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0464 \| \| 0.0006 \| 158.96 \| 1987 \| 1.0563 \| 0.86 \| 0.2707 \| 1.3093 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 160.0 \| 2000 \| 1.0568 \| 0.86 \| 0.2707 \| 1.3093 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 160.96 \| 2012 \| 1.0573 \| 0.86 \| 0.2707 \| 1.3092 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 162.0 \| 2025 \| 1.0577 \| 0.86 \| 0.2707 \| 1.3092 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 162.96 \| 2037 \| 1.0581 \| 0.86 \| 0.2707 \| 1.3091 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 164.0 \| 2050 \| 1.0585 \| 0.86 \| 0.2707 \| 1.3091 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 164.96 \| 2062 \| 1.0589 \| 0.86 \| 0.2707 \| 1.3090 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 166.0 \| 2075 \| 1.0593 \| 0.86 \| 0.2707 \| 1.3090 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 166.96 \| 2087 \| 1.0597 \| 0.86 \| 0.2706 \| 1.3089 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 168.0 \| 2100 \| 1.0600 \| 0.86 \| 0.2706 \| 1.3089 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 168.96 \| 2112 \| 1.0603 \| 0.86 \| 0.2706 \| 1.3089 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 170.0 \| 2125 \| 1.0607 \| 0.86 \| 0.2706 \| 1.3088 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 170.96 \| 2137 \| 1.0610 \| 0.86 \| 0.2706 \| 1.3088 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 172.0 \| 2150 \| 1.0613 \| 0.86 \| 0.2706 \| 1.3088 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 172.96 \| 2162 \| 1.0616 \| 0.86 \| 0.2706 \| 1.3087 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 174.0 \| 2175 \| 1.0619 \| 0.86 \| 0.2706 \| 1.3087 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 174.96 \| 2187 \| 1.0621 \| 0.86 \| 0.2706 \| 1.3087 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 176.0 \| 2200 \| 1.0624 \| 0.86 \| 0.2706 \| 1.3087 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 176.96 \| 2212 \| 1.0626 \| 0.86 \| 0.2706 \| 1.3086 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 178.0 \| 2225 \| 1.0629 \| 0.86 \| 0.2706 \| 1.3086 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 178.96 \| 2237 \| 1.0630 \| 0.86 \| 0.2706 \| 1.3086 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 180.0 \| 2250 \| 1.0632 \| 0.86 \| 0.2706 \| 1.3086 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 180.96 \| 2262 \| 1.0634 \| 0.86 \| 0.2706 \| 1.3086 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0463 \| \| 0.0004 \| 182.0 \| 2275 \| 1.0636 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 182.96 \| 2287 \| 1.0637 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 184.0 \| 2300 \| 1.0639 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 184.96 \| 2312 \| 1.0640 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 186.0 \| 2325 \| 1.0641 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 186.96 \| 2337 \| 1.0642 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0462 \| \| 0.0004 \| 188.0 \| 2350 \| 1.0643 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0461 \| \| 0.0004 \| 188.96 \| 2362 \| 1.0643 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0461 \| \| 0.0004 \| 190.0 \| 2375 \| 1.0644 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0461 \| \| 0.0004 \| 190.96 \| 2387 \| 1.0644 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0461 \| \| 0.0004 \| 192.0 \| 2400 \| 1.0644 \| 0.86 \| 0.2705 \| 1.3085 \| 0.8600 \| 0.8552 \| 0.1378 \| 0.0461 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6228 - Accuracy: 0.5375 - Brier Loss: 0.6009 - Nll: 2.3362 - F1 Micro: 0.5375 - F1 Macro: 0.5332 - Ece: 0.1886 - Aurc: 0.2188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.0859 \| 0.0425 \| 1.0718 \| 7.3724 \| 0.0425 \| 0.0340 \| 0.2811 \| 0.9554 \| \| No log \| 2.0 \| 14 \| 3.1575 \| 0.0925 \| 0.9517 \| 5.8523 \| 0.0925 \| 0.0709 \| 0.1660 \| 0.8903 \| \| No log \| 3.0 \| 21 \| 2.8291 \| 0.2325 \| 0.8866 \| 5.4080 \| 0.2325 \| 0.1824 \| 0.1601 \| 0.6363 \| \| No log \| 4.0 \| 28 \| 2.4189 \| 0.2875 \| 0.7987 \| 3.5373 \| 0.2875 \| 0.2714 \| 0.1822 \| 0.4858 \| \| No log \| 5.0 \| 35 \| 2.1338 \| 0.3775 \| 0.7203 \| 2.9804 \| 0.3775 \| 0.3569 \| 0.1731 \| 0.3581 \| \| No log \| 6.0 \| 42 \| 2.0168 \| 0.44 \| 0.6910 \| 2.9903 \| 0.44 \| 0.4255 \| 0.1854 \| 0.3060 \| \| No log \| 7.0 \| 49 \| 2.0458 \| 0.4575 \| 0.6988 \| 3.1988 \| 0.4575 \| 0.4286 \| 0.2078 \| 0.3000 \| \| No log \| 8.0 \| 56 \| 1.9688 \| 0.4575 \| 0.6928 \| 2.8691 \| 0.4575 \| 0.4542 \| 0.2122 \| 0.2986 \| \| No log \| 9.0 \| 63 \| 2.1143 \| 0.4625 \| 0.7081 \| 2.8507 \| 0.4625 \| 0.4496 \| 0.2232 \| 0.2940 \| \| No log \| 10.0 \| 70 \| 2.1352 \| 0.455 \| 0.7242 \| 2.9232 \| 0.455 \| 0.4317 \| 0.2581 \| 0.2923 \| \| No log \| 11.0 \| 77 \| 2.2760 \| 0.44 \| 0.7545 \| 2.9083 \| 0.44 \| 0.4144 \| 0.2835 \| 0.3144 \| \| No log \| 12.0 \| 84 \| 2.1320 \| 0.4675 \| 0.7389 \| 2.6277 \| 0.4675 \| 0.4607 \| 0.2715 \| 0.3142 \| \| No log \| 13.0 \| 91 \| 2.0889 \| 0.4825 \| 0.7170 \| 2.6786 \| 0.4825 \| 0.4755 \| 0.2522 \| 0.2933 \| \| No log \| 14.0 \| 98 \| 2.0173 \| 0.475 \| 0.7020 \| 2.7765 \| 0.4750 \| 0.4555 \| 0.2593 \| 0.2791 \| \| No log \| 15.0 \| 105 \| 2.1265 \| 0.4875 \| 0.7124 \| 3.0127 \| 0.4875 \| 0.4753 \| 0.2458 \| 0.3069 \| \| No log \| 16.0 \| 112 \| 2.1517 \| 0.4825 \| 0.7184 \| 2.7981 \| 0.4825 \| 0.4719 \| 0.2505 \| 0.2964 \| \| No log \| 17.0 \| 119 \| 1.9485 \| 0.49 \| 0.6828 \| 2.6670 \| 0.49 \| 0.4773 \| 0.2430 \| 0.2649 \| \| No log \| 18.0 \| 126 \| 1.9434 \| 0.5 \| 0.6802 \| 2.6131 \| 0.5 \| 0.4941 \| 0.2267 \| 0.2741 \| \| No log \| 19.0 \| 133 \| 1.9055 \| 0.4925 \| 0.6741 \| 2.7035 \| 0.4925 \| 0.4953 \| 0.2201 \| 0.2700 \| \| No log \| 20.0 \| 140 \| 1.8119 \| 0.5 \| 0.6520 \| 2.4537 \| 0.5 \| 0.4937 \| 0.2016 \| 0.2449 \| \| No log \| 21.0 \| 147 \| 1.8420 \| 0.49 \| 0.6593 \| 2.6244 \| 0.49 \| 0.4798 \| 0.2100 \| 0.2668 \| \| No log \| 22.0 \| 154 \| 1.9282 \| 0.5 \| 0.6739 \| 2.6650 \| 0.5 \| 0.4837 \| 0.2166 \| 0.2652 \| \| No log \| 23.0 \| 161 \| 1.8139 \| 0.5125 \| 0.6543 \| 2.4043 \| 0.5125 \| 0.5009 \| 0.2275 \| 0.2604 \| \| No log \| 24.0 \| 168 \| 1.6997 \| 0.5325 \| 0.6124 \| 2.3950 \| 0.5325 \| 0.5271 \| 0.1873 \| 0.2248 \| \| No log \| 25.0 \| 175 \| 1.8370 \| 0.5025 \| 0.6526 \| 2.4967 \| 0.5025 \| 0.4926 \| 0.2249 \| 0.2513 \| \| No log \| 26.0 \| 182 \| 1.7508 \| 0.5025 \| 0.6388 \| 2.4321 \| 0.5025 \| 0.4947 \| 0.2135 \| 0.2416 \| \| No log \| 27.0 \| 189 \| 1.7354 \| 0.52 \| 0.6368 \| 2.3706 \| 0.52 \| 0.5149 \| 0.2101 \| 0.2440 \| \| No log \| 28.0 \| 196 \| 1.7809 \| 0.52 \| 0.6421 \| 2.4895 \| 0.52 \| 0.5076 \| 0.2033 \| 0.2446 \| \| No log \| 29.0 \| 203 \| 1.6770 \| 0.55 \| 0.6046 \| 2.3913 \| 0.55 \| 0.5409 \| 0.1849 \| 0.2189 \| \| No log \| 30.0 \| 210 \| 1.6794 \| 0.5325 \| 0.6127 \| 2.5677 \| 0.5325 \| 0.5263 \| 0.2095 \| 0.2247 \| \| No log \| 31.0 \| 217 \| 1.7352 \| 0.525 \| 0.6325 \| 2.5068 \| 0.525 \| 0.5062 \| 0.1865 \| 0.2320 \| \| No log \| 32.0 \| 224 \| 1.7396 \| 0.5225 \| 0.6379 \| 2.4798 \| 0.5225 \| 0.5169 \| 0.1829 \| 0.2402 \| \| No log \| 33.0 \| 231 \| 1.7013 \| 0.52 \| 0.6188 \| 2.5032 \| 0.52 \| 0.5029 \| 0.1986 \| 0.2233 \| \| No log \| 34.0 \| 238 \| 1.7121 \| 0.5075 \| 0.6313 \| 2.5680 \| 0.5075 \| 0.5074 \| 0.2249 \| 0.2482 \| \| No log \| 35.0 \| 245 \| 1.7053 \| 0.5175 \| 0.6368 \| 2.3967 \| 0.5175 \| 0.5064 \| 0.2088 \| 0.2389 \| \| No log \| 36.0 \| 252 \| 1.6616 \| 0.55 \| 0.6058 \| 2.5726 \| 0.55 \| 0.5357 \| 0.1762 \| 0.2174 \| \| No log \| 37.0 \| 259 \| 1.7302 \| 0.5075 \| 0.6350 \| 2.3745 \| 0.5075 \| 0.4993 \| 0.1939 \| 0.2481 \| \| No log \| 38.0 \| 266 \| 1.6741 \| 0.5225 \| 0.6152 \| 2.5862 \| 0.5225 \| 0.5211 \| 0.2042 \| 0.2234 \| \| No log \| 39.0 \| 273 \| 1.6960 \| 0.515 \| 0.6216 \| 2.4210 \| 0.515 \| 0.5049 \| 0.1776 \| 0.2270 \| \| No log \| 40.0 \| 280 \| 1.6512 \| 0.54 \| 0.6075 \| 2.4495 \| 0.54 \| 0.5377 \| 0.1848 \| 0.2217 \| \| No log \| 41.0 \| 287 \| 1.6477 \| 0.52 \| 0.6086 \| 2.5354 \| 0.52 \| 0.5177 \| 0.1976 \| 0.2219 \| \| No log \| 42.0 \| 294 \| 1.6680 \| 0.52 \| 0.6198 \| 2.2815 \| 0.52 \| 0.5215 \| 0.1951 \| 0.2314 \| \| No log \| 43.0 \| 301 \| 1.6153 \| 0.545 \| 0.5935 \| 2.3952 \| 0.545 \| 0.5376 \| 0.1721 \| 0.2088 \| \| No log \| 44.0 \| 308 \| 1.6347 \| 0.5375 \| 0.6023 \| 2.3985 \| 0.5375 \| 0.5340 \| 0.1778 \| 0.2178 \| \| No log \| 45.0 \| 315 \| 1.6132 \| 0.5375 \| 0.6001 \| 2.3149 \| 0.5375 \| 0.5375 \| 0.1902 \| 0.2163 \| \| No log \| 46.0 \| 322 \| 1.6366 \| 0.5275 \| 0.6102 \| 2.3820 \| 0.5275 \| 0.5210 \| 0.2057 \| 0.2239 \| \| No log \| 47.0 \| 329 \| 1.6118 \| 0.55 \| 0.5963 \| 2.3834 \| 0.55 \| 0.5463 \| 0.1748 \| 0.2137 \| \| No log \| 48.0 \| 336 \| 1.6208 \| 0.535 \| 0.6038 \| 2.2855 \| 0.535 \| 0.5340 \| 0.1791 \| 0.2201 \| \| No log \| 49.0 \| 343 \| 1.6006 \| 0.545 \| 0.5960 \| 2.3253 \| 0.545 \| 0.5440 \| 0.2007 \| 0.2158 \| \| No log \| 50.0 \| 350 \| 1.6184 \| 0.5425 \| 0.6005 \| 2.3181 \| 0.5425 \| 0.5432 \| 0.1846 \| 0.2176 \| \| No log \| 51.0 \| 357 \| 1.6215 \| 0.5425 \| 0.6022 \| 2.3219 \| 0.5425 \| 0.5371 \| 0.1776 \| 0.2163 \| \| No log \| 52.0 \| 364 \| 1.6068 \| 0.54 \| 0.5978 \| 2.2928 \| 0.54 \| 0.5383 \| 0.1865 \| 0.2164 \| \| No log \| 53.0 \| 371 \| 1.6128 \| 0.535 \| 0.5979 \| 2.3243 \| 0.535 \| 0.5335 \| 0.1913 \| 0.2189 \| \| No log \| 54.0 \| 378 \| 1.6182 \| 0.545 \| 0.6002 \| 2.3604 \| 0.545 \| 0.5432 \| 0.1740 \| 0.2157 \| \| No log \| 55.0 \| 385 \| 1.6143 \| 0.54 \| 0.5981 \| 2.3632 \| 0.54 \| 0.5348 \| 0.1911 \| 0.2165 \| \| No log \| 56.0 \| 392 \| 1.6163 \| 0.5375 \| 0.6010 \| 2.3551 \| 0.5375 \| 0.5338 \| 0.1950 \| 0.2191 \| \| No log \| 57.0 \| 399 \| 1.6125 \| 0.54 \| 0.5984 \| 2.3597 \| 0.54 \| 0.5378 \| 0.1680 \| 0.2159 \| \| No log \| 58.0 \| 406 \| 1.6159 \| 0.5425 \| 0.5997 \| 2.3564 \| 0.5425 \| 0.5388 \| 0.1796 \| 0.2175 \| \| No log \| 59.0 \| 413 \| 1.6102 \| 0.5475 \| 0.5967 \| 2.3879 \| 0.5475 \| 0.5448 \| 0.1863 \| 0.2152 \| \| No log \| 60.0 \| 420 \| 1.6171 \| 0.535 \| 0.6006 \| 2.3594 \| 0.535 \| 0.5298 \| 0.1987 \| 0.2183 \| \| No log \| 61.0 \| 427 \| 1.6162 \| 0.5375 \| 0.5987 \| 2.3631 \| 0.5375 \| 0.5341 \| 0.1836 \| 0.2172 \| \| No log \| 62.0 \| 434 \| 1.6167 \| 0.5425 \| 0.5992 \| 2.3344 \| 0.5425 \| 0.5395 \| 0.2011 \| 0.2170 \| \| No log \| 63.0 \| 441 \| 1.6123 \| 0.545 \| 0.5984 \| 2.3575 \| 0.545 \| 0.5423 \| 0.1946 \| 0.2174 \| \| No log \| 64.0 \| 448 \| 1.6209 \| 0.535 \| 0.6007 \| 2.3333 \| 0.535 \| 0.5299 \| 0.1973 \| 0.2180 \| \| No log \| 65.0 \| 455 \| 1.6184 \| 0.5475 \| 0.6003 \| 2.3620 \| 0.5475 \| 0.5442 \| 0.1911 \| 0.2167 \| \| No log \| 66.0 \| 462 \| 1.6179 \| 0.5375 \| 0.5992 \| 2.3321 \| 0.5375 \| 0.5333 \| 0.2019 \| 0.2182 \| \| No log \| 67.0 \| 469 \| 1.6179 \| 0.5425 \| 0.5999 \| 2.3309 \| 0.5425 \| 0.5397 \| 0.1780 \| 0.2176 \| \| No log \| 68.0 \| 476 \| 1.6204 \| 0.545 \| 0.6004 \| 2.3282 \| 0.545 \| 0.5408 \| 0.1740 \| 0.2167 \| \| No log \| 69.0 \| 483 \| 1.6195 \| 0.54 \| 0.6005 \| 2.3372 \| 0.54 \| 0.5370 \| 0.1898 \| 0.2187 \| \| No log \| 70.0 \| 490 \| 1.6189 \| 0.5425 \| 0.5996 \| 2.3328 \| 0.5425 \| 0.5398 \| 0.1754 \| 0.2169 \| \| No log \| 71.0 \| 497 \| 1.6202 \| 0.5425 \| 0.6007 \| 2.3352 \| 0.5425 \| 0.5394 \| 0.1973 \| 0.2180 \| \| 0.3297 \| 72.0 \| 504 \| 1.6175 \| 0.5375 \| 0.5997 \| 2.3297 \| 0.5375 \| 0.5348 \| 0.1721 \| 0.2184 \| \| 0.3297 \| 73.0 \| 511 \| 1.6185 \| 0.545 \| 0.6001 \| 2.3339 \| 0.545 \| 0.5414 \| 0.1780 \| 0.2175 \| \| 0.3297 \| 74.0 \| 518 \| 1.6203 \| 0.5425 \| 0.6003 \| 2.3315 \| 0.5425 \| 0.5391 \| 0.1855 \| 0.2175 \| \| 0.3297 \| 75.0 \| 525 \| 1.6199 \| 0.54 \| 0.6002 \| 2.3366 \| 0.54 \| 0.5363 \| 0.1841 \| 0.2180 \| \| 0.3297 \| 76.0 \| 532 \| 1.6203 \| 0.5425 \| 0.6002 \| 2.3332 \| 0.5425 \| 0.5395 \| 0.1736 \| 0.2176 \| \| 0.3297 \| 77.0 \| 539 \| 1.6191 \| 0.5425 \| 0.6000 \| 2.3328 \| 0.5425 \| 0.5395 \| 0.1722 \| 0.2176 \| \| 0.3297 \| 78.0 \| 546 \| 1.6202 \| 0.54 \| 0.6001 \| 2.3317 \| 0.54 \| 0.5362 \| 0.1909 \| 0.2177 \| \| 0.3297 \| 79.0 \| 553 \| 1.6199 \| 0.54 \| 0.6002 \| 2.3335 \| 0.54 \| 0.5370 \| 0.1982 \| 0.2181 \| \| 0.3297 \| 80.0 \| 560 \| 1.6207 \| 0.54 \| 0.6006 \| 2.3336 \| 0.54 \| 0.5363 \| 0.1983 \| 0.2185 \| \| 0.3297 \| 81.0 \| 567 \| 1.6210 \| 0.5425 \| 0.6003 \| 2.3353 \| 0.5425 \| 0.5389 \| 0.1875 \| 0.2174 \| \| 0.3297 \| 82.0 \| 574 \| 1.6216 \| 0.5425 \| 0.6005 \| 2.3340 \| 0.5425 \| 0.5386 \| 0.1792 \| 0.2178 \| \| 0.3297 \| 83.0 \| 581 \| 1.6213 \| 0.5375 \| 0.6007 \| 2.3360 \| 0.5375 \| 0.5340 \| 0.1797 \| 0.2193 \| \| 0.3297 \| 84.0 \| 588 \| 1.6208 \| 0.54 \| 0.6007 \| 2.3345 \| 0.54 \| 0.5363 \| 0.1848 \| 0.2189 \| \| 0.3297 \| 85.0 \| 595 \| 1.6221 \| 0.54 \| 0.6005 \| 2.3360 \| 0.54 \| 0.5369 \| 0.1836 \| 0.2184 \| \| 0.3297 \| 86.0 \| 602 \| 1.6218 \| 0.5425 \| 0.6005 \| 2.3342 \| 0.5425 \| 0.5391 \| 0.1922 \| 0.2178 \| \| 0.3297 \| 87.0 \| 609 \| 1.6224 \| 0.54 \| 0.6006 \| 2.3365 \| 0.54 \| 0.5363 \| 0.1849 \| 0.2185 \| \| 0.3297 \| 88.0 \| 616 \| 1.6216 \| 0.54 \| 0.6005 \| 2.3355 \| 0.54 \| 0.5363 \| 0.1912 \| 0.2188 \| \| 0.3297 \| 89.0 \| 623 \| 1.6222 \| 0.535 \| 0.6008 \| 2.3359 \| 0.535 \| 0.5310 \| 0.1890 \| 0.2195 \| \| 0.3297 \| 90.0 \| 630 \| 1.6218 \| 0.5425 \| 0.6007 \| 2.3356 \| 0.5425 \| 0.5386 \| 0.1930 \| 0.2179 \| \| 0.3297 \| 91.0 \| 637 \| 1.6226 \| 0.54 \| 0.6009 \| 2.3363 \| 0.54 \| 0.5363 \| 0.1941 \| 0.2188 \| \| 0.3297 \| 92.0 \| 644 \| 1.6224 \| 0.54 \| 0.6009 \| 2.3360 \| 0.54 \| 0.5363 \| 0.1890 \| 0.2188 \| \| 0.3297 \| 93.0 \| 651 \| 1.6226 \| 0.5425 \| 0.6009 \| 2.3359 \| 0.5425 \| 0.5386 \| 0.1964 \| 0.2179 \| \| 0.3297 \| 94.0 \| 658 \| 1.6227 \| 0.54 \| 0.6009 \| 2.3363 \| 0.54 \| 0.5363 \| 0.1877 \| 0.2187 \| \| 0.3297 \| 95.0 \| 665 \| 1.6226 \| 0.54 \| 0.6008 \| 2.3360 \| 0.54 \| 0.5363 \| 0.1916 \| 0.2186 \| \| 0.3297 \| 96.0 \| 672 \| 1.6225 \| 0.54 \| 0.6008 \| 2.3362 \| 0.54 \| 0.5363 \| 0.1972 \| 0.2186 \| \| 0.3297 \| 97.0 \| 679 \| 1.6227 \| 0.5375 \| 0.6009 \| 2.3362 \| 0.5375 \| 0.5332 \| 0.1949 \| 0.2188 \| \| 0.3297 \| 98.0 \| 686 \| 1.6228 \| 0.5375 \| 0.6009 \| 2.3362 \| 0.5375 \| 0.5332 \| 0.1924 \| 0.2188 \| \| 0.3297 \| 99.0 \| 693 \| 1.6228 \| 0.5375 \| 0.6009 \| 2.3363 \| 0.5375 \| 0.5332 \| 0.1924 \| 0.2189 \| \| 0.3297 \| 100.0 \| 700 \| 1.6228 \| 0.5375 \| 0.6009 \| 2.3362 \| 0.5375 \| 0.5332 \| 0.1886 \| 0.2188 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6906 - Accuracy: 0.5675 - Brier Loss: 0.5696 - Nll: 2.4654 - F1 Micro: 0.5675 - F1 Macro: 0.5648 - Ece: 0.1638 - Aurc: 0.1969 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.6645 \| 0.0425 \| 1.0732 \| 7.4792 \| 0.0425 \| 0.0329 \| 0.2822 \| 0.9534 \| \| No log \| 2.0 \| 14 \| 3.6090 \| 0.1 \| 0.9462 \| 5.7033 \| 0.1000 \| 0.0909 \| 0.1715 \| 0.8908 \| \| No log \| 3.0 \| 21 \| 3.2938 \| 0.1975 \| 0.8945 \| 5.2434 \| 0.1975 \| 0.1585 \| 0.1594 \| 0.6803 \| \| No log \| 4.0 \| 28 \| 2.8501 \| 0.29 \| 0.7966 \| 3.6125 \| 0.29 \| 0.2703 \| 0.1664 \| 0.4859 \| \| No log \| 5.0 \| 35 \| 2.5122 \| 0.39 \| 0.7162 \| 3.1122 \| 0.39 \| 0.3562 \| 0.1838 \| 0.3484 \| \| No log \| 6.0 \| 42 \| 2.3667 \| 0.4175 \| 0.6955 \| 3.3546 \| 0.4175 \| 0.3757 \| 0.1683 \| 0.3138 \| \| No log \| 7.0 \| 49 \| 2.2629 \| 0.4725 \| 0.6766 \| 2.9737 \| 0.4725 \| 0.4417 \| 0.1917 \| 0.2939 \| \| No log \| 8.0 \| 56 \| 2.1495 \| 0.485 \| 0.6530 \| 2.7257 \| 0.485 \| 0.4798 \| 0.1901 \| 0.2762 \| \| No log \| 9.0 \| 63 \| 2.1491 \| 0.495 \| 0.6517 \| 2.7709 \| 0.495 \| 0.4982 \| 0.1934 \| 0.2664 \| \| No log \| 10.0 \| 70 \| 2.2541 \| 0.465 \| 0.6794 \| 2.6880 \| 0.465 \| 0.4654 \| 0.1923 \| 0.2870 \| \| No log \| 11.0 \| 77 \| 2.1607 \| 0.4925 \| 0.6564 \| 2.7773 \| 0.4925 \| 0.4769 \| 0.1904 \| 0.2545 \| \| No log \| 12.0 \| 84 \| 2.1581 \| 0.505 \| 0.6714 \| 2.7017 \| 0.505 \| 0.4876 \| 0.2023 \| 0.2669 \| \| No log \| 13.0 \| 91 \| 2.0025 \| 0.515 \| 0.6324 \| 2.5557 \| 0.515 \| 0.5048 \| 0.2194 \| 0.2348 \| \| No log \| 14.0 \| 98 \| 2.0267 \| 0.5025 \| 0.6474 \| 2.4764 \| 0.5025 \| 0.4876 \| 0.2071 \| 0.2474 \| \| No log \| 15.0 \| 105 \| 1.9258 \| 0.535 \| 0.6106 \| 2.4995 \| 0.535 \| 0.5310 \| 0.1960 \| 0.2251 \| \| No log \| 16.0 \| 112 \| 1.8650 \| 0.545 \| 0.6031 \| 2.4944 \| 0.545 \| 0.5383 \| 0.1847 \| 0.2187 \| \| No log \| 17.0 \| 119 \| 1.8902 \| 0.5375 \| 0.6118 \| 2.5468 \| 0.5375 \| 0.5284 \| 0.1852 \| 0.2278 \| \| No log \| 18.0 \| 126 \| 1.9187 \| 0.53 \| 0.6188 \| 2.5580 \| 0.53 \| 0.5174 \| 0.1792 \| 0.2285 \| \| No log \| 19.0 \| 133 \| 1.8399 \| 0.54 \| 0.6049 \| 2.5246 \| 0.54 \| 0.5362 \| 0.1758 \| 0.2191 \| \| No log \| 20.0 \| 140 \| 1.8912 \| 0.53 \| 0.6117 \| 2.5403 \| 0.53 \| 0.5139 \| 0.1819 \| 0.2210 \| \| No log \| 21.0 \| 147 \| 1.8930 \| 0.5325 \| 0.6099 \| 2.6739 \| 0.5325 \| 0.5294 \| 0.1736 \| 0.2281 \| \| No log \| 22.0 \| 154 \| 1.8930 \| 0.5275 \| 0.6070 \| 2.6060 \| 0.5275 \| 0.5244 \| 0.1832 \| 0.2214 \| \| No log \| 23.0 \| 161 \| 1.8596 \| 0.5375 \| 0.6030 \| 2.5066 \| 0.5375 \| 0.5260 \| 0.1864 \| 0.2185 \| \| No log \| 24.0 \| 168 \| 1.8328 \| 0.5375 \| 0.5942 \| 2.5127 \| 0.5375 \| 0.5302 \| 0.1807 \| 0.2079 \| \| No log \| 25.0 \| 175 \| 1.8069 \| 0.53 \| 0.6004 \| 2.4747 \| 0.53 \| 0.5241 \| 0.1910 \| 0.2189 \| \| No log \| 26.0 \| 182 \| 1.7937 \| 0.5425 \| 0.5912 \| 2.5083 \| 0.5425 \| 0.5366 \| 0.1841 \| 0.2123 \| \| No log \| 27.0 \| 189 \| 1.7986 \| 0.55 \| 0.5945 \| 2.5267 \| 0.55 \| 0.5476 \| 0.1901 \| 0.2134 \| \| No log \| 28.0 \| 196 \| 1.8090 \| 0.5525 \| 0.5918 \| 2.4467 \| 0.5525 \| 0.5466 \| 0.1765 \| 0.2125 \| \| No log \| 29.0 \| 203 \| 1.7911 \| 0.55 \| 0.5931 \| 2.4536 \| 0.55 \| 0.5462 \| 0.1619 \| 0.2140 \| \| No log \| 30.0 \| 210 \| 1.7702 \| 0.5425 \| 0.5911 \| 2.5037 \| 0.5425 \| 0.5340 \| 0.1819 \| 0.2080 \| \| No log \| 31.0 \| 217 \| 1.8248 \| 0.53 \| 0.6063 \| 2.4965 \| 0.53 \| 0.5145 \| 0.1757 \| 0.2217 \| \| No log \| 32.0 \| 224 \| 1.7866 \| 0.545 \| 0.5920 \| 2.5543 \| 0.545 \| 0.5365 \| 0.1745 \| 0.2082 \| \| No log \| 33.0 \| 231 \| 1.7793 \| 0.555 \| 0.5867 \| 2.4751 \| 0.555 \| 0.5503 \| 0.1657 \| 0.2107 \| \| No log \| 34.0 \| 238 \| 1.7417 \| 0.5525 \| 0.5799 \| 2.4174 \| 0.5525 \| 0.5517 \| 0.1778 \| 0.2049 \| \| No log \| 35.0 \| 245 \| 1.7557 \| 0.54 \| 0.5869 \| 2.4065 \| 0.54 \| 0.5379 \| 0.1640 \| 0.2119 \| \| No log \| 36.0 \| 252 \| 1.7567 \| 0.54 \| 0.5884 \| 2.5031 \| 0.54 \| 0.5374 \| 0.1519 \| 0.2122 \| \| No log \| 37.0 \| 259 \| 1.7306 \| 0.56 \| 0.5781 \| 2.5313 \| 0.56 \| 0.5551 \| 0.1906 \| 0.2042 \| \| No log \| 38.0 \| 266 \| 1.7615 \| 0.5425 \| 0.5888 \| 2.4510 \| 0.5425 \| 0.5423 \| 0.1783 \| 0.2121 \| \| No log \| 39.0 \| 273 \| 1.7607 \| 0.555 \| 0.5855 \| 2.5044 \| 0.555 \| 0.5473 \| 0.1808 \| 0.2062 \| \| No log \| 40.0 \| 280 \| 1.7430 \| 0.5275 \| 0.5840 \| 2.4265 \| 0.5275 \| 0.5228 \| 0.1822 \| 0.2125 \| \| No log \| 41.0 \| 287 \| 1.7515 \| 0.5625 \| 0.5800 \| 2.5432 \| 0.5625 \| 0.5500 \| 0.1812 \| 0.2019 \| \| No log \| 42.0 \| 294 \| 1.7229 \| 0.5475 \| 0.5778 \| 2.4597 \| 0.5475 \| 0.5416 \| 0.1656 \| 0.2042 \| \| No log \| 43.0 \| 301 \| 1.7566 \| 0.555 \| 0.5818 \| 2.4305 \| 0.555 \| 0.5517 \| 0.1521 \| 0.2036 \| \| No log \| 44.0 \| 308 \| 1.7093 \| 0.575 \| 0.5745 \| 2.4087 \| 0.575 \| 0.5761 \| 0.1678 \| 0.2019 \| \| No log \| 45.0 \| 315 \| 1.7319 \| 0.56 \| 0.5788 \| 2.5339 \| 0.56 \| 0.5550 \| 0.1647 \| 0.2028 \| \| No log \| 46.0 \| 322 \| 1.7331 \| 0.5475 \| 0.5863 \| 2.4386 \| 0.5475 \| 0.5470 \| 0.1737 \| 0.2113 \| \| No log \| 47.0 \| 329 \| 1.7243 \| 0.56 \| 0.5776 \| 2.5336 \| 0.56 \| 0.5548 \| 0.1642 \| 0.2037 \| \| No log \| 48.0 \| 336 \| 1.7205 \| 0.5675 \| 0.5817 \| 2.4578 \| 0.5675 \| 0.5661 \| 0.1800 \| 0.2091 \| \| No log \| 49.0 \| 343 \| 1.7210 \| 0.5725 \| 0.5721 \| 2.4792 \| 0.5725 \| 0.5656 \| 0.1550 \| 0.1970 \| \| No log \| 50.0 \| 350 \| 1.6994 \| 0.58 \| 0.5719 \| 2.4645 \| 0.58 \| 0.5773 \| 0.1580 \| 0.1957 \| \| No log \| 51.0 \| 357 \| 1.7082 \| 0.5775 \| 0.5737 \| 2.4805 \| 0.5775 \| 0.5701 \| 0.1734 \| 0.1992 \| \| No log \| 52.0 \| 364 \| 1.7144 \| 0.5575 \| 0.5693 \| 2.4501 \| 0.5575 \| 0.5519 \| 0.1812 \| 0.1974 \| \| No log \| 53.0 \| 371 \| 1.7196 \| 0.57 \| 0.5796 \| 2.4555 \| 0.57 \| 0.5618 \| 0.1728 \| 0.2022 \| \| No log \| 54.0 \| 378 \| 1.7149 \| 0.5675 \| 0.5746 \| 2.4363 \| 0.5675 \| 0.5605 \| 0.1901 \| 0.1999 \| \| No log \| 55.0 \| 385 \| 1.7030 \| 0.555 \| 0.5775 \| 2.4253 \| 0.555 \| 0.5528 \| 0.1823 \| 0.2029 \| \| No log \| 56.0 \| 392 \| 1.7209 \| 0.5675 \| 0.5747 \| 2.4926 \| 0.5675 \| 0.5634 \| 0.1644 \| 0.2008 \| \| No log \| 57.0 \| 399 \| 1.7103 \| 0.5525 \| 0.5760 \| 2.4528 \| 0.5525 \| 0.5487 \| 0.1738 \| 0.2025 \| \| No log \| 58.0 \| 406 \| 1.7005 \| 0.5575 \| 0.5771 \| 2.4678 \| 0.5575 \| 0.5547 \| 0.1514 \| 0.2041 \| \| No log \| 59.0 \| 413 \| 1.7098 \| 0.56 \| 0.5763 \| 2.4368 \| 0.56 \| 0.5585 \| 0.1650 \| 0.2022 \| \| No log \| 60.0 \| 420 \| 1.6976 \| 0.5775 \| 0.5681 \| 2.4633 \| 0.5775 \| 0.5737 \| 0.1729 \| 0.1963 \| \| No log \| 61.0 \| 427 \| 1.7057 \| 0.5575 \| 0.5739 \| 2.4717 \| 0.5575 \| 0.5522 \| 0.1932 \| 0.2007 \| \| No log \| 62.0 \| 434 \| 1.6884 \| 0.5725 \| 0.5646 \| 2.4538 \| 0.5725 \| 0.5693 \| 0.1692 \| 0.1930 \| \| No log \| 63.0 \| 441 \| 1.6979 \| 0.56 \| 0.5731 \| 2.4635 \| 0.56 \| 0.5562 \| 0.1690 \| 0.2016 \| \| No log \| 64.0 \| 448 \| 1.6848 \| 0.55 \| 0.5686 \| 2.4583 \| 0.55 \| 0.5452 \| 0.1782 \| 0.1980 \| \| No log \| 65.0 \| 455 \| 1.7072 \| 0.5575 \| 0.5774 \| 2.4596 \| 0.5575 \| 0.5549 \| 0.1627 \| 0.2036 \| \| No log \| 66.0 \| 462 \| 1.7057 \| 0.5625 \| 0.5759 \| 2.4644 \| 0.5625 \| 0.5586 \| 0.1780 \| 0.2009 \| \| No log \| 67.0 \| 469 \| 1.7016 \| 0.56 \| 0.5719 \| 2.4976 \| 0.56 \| 0.5546 \| 0.1734 \| 0.2004 \| \| No log \| 68.0 \| 476 \| 1.6951 \| 0.5675 \| 0.5725 \| 2.4025 \| 0.5675 \| 0.5648 \| 0.1866 \| 0.1980 \| \| No log \| 69.0 \| 483 \| 1.7012 \| 0.555 \| 0.5750 \| 2.4970 \| 0.555 \| 0.5527 \| 0.1958 \| 0.2022 \| \| No log \| 70.0 \| 490 \| 1.6983 \| 0.575 \| 0.5689 \| 2.4763 \| 0.575 \| 0.5708 \| 0.1648 \| 0.1950 \| \| No log \| 71.0 \| 497 \| 1.6954 \| 0.5675 \| 0.5729 \| 2.4642 \| 0.5675 \| 0.5638 \| 0.1762 \| 0.2004 \| \| 0.437 \| 72.0 \| 504 \| 1.6973 \| 0.5625 \| 0.5718 \| 2.4639 \| 0.5625 \| 0.5605 \| 0.1680 \| 0.1991 \| \| 0.437 \| 73.0 \| 511 \| 1.6942 \| 0.565 \| 0.5714 \| 2.4629 \| 0.565 \| 0.5610 \| 0.1776 \| 0.1980 \| \| 0.437 \| 74.0 \| 518 \| 1.6923 \| 0.5725 \| 0.5694 \| 2.4717 \| 0.5725 \| 0.5698 \| 0.1676 \| 0.1967 \| \| 0.437 \| 75.0 \| 525 \| 1.6926 \| 0.5675 \| 0.5699 \| 2.4674 \| 0.5675 \| 0.5633 \| 0.1741 \| 0.1975 \| \| 0.437 \| 76.0 \| 532 \| 1.6906 \| 0.5675 \| 0.5692 \| 2.4673 \| 0.5675 \| 0.5641 \| 0.1785 \| 0.1962 \| \| 0.437 \| 77.0 \| 539 \| 1.6912 \| 0.565 \| 0.5692 \| 2.4671 \| 0.565 \| 0.5617 \| 0.1568 \| 0.1958 \| \| 0.437 \| 78.0 \| 546 \| 1.6879 \| 0.565 \| 0.5685 \| 2.4629 \| 0.565 \| 0.5620 \| 0.1860 \| 0.1954 \| \| 0.437 \| 79.0 \| 553 \| 1.6886 \| 0.565 \| 0.5695 \| 2.4650 \| 0.565 \| 0.5625 \| 0.1777 \| 0.1968 \| \| 0.437 \| 80.0 \| 560 \| 1.6882 \| 0.5625 \| 0.5700 \| 2.4632 \| 0.5625 \| 0.5583 \| 0.1791 \| 0.1982 \| \| 0.437 \| 81.0 \| 567 \| 1.6918 \| 0.565 \| 0.5704 \| 2.4638 \| 0.565 \| 0.5622 \| 0.1630 \| 0.1978 \| \| 0.437 \| 82.0 \| 574 \| 1.6909 \| 0.5675 \| 0.5697 \| 2.4646 \| 0.5675 \| 0.5641 \| 0.1862 \| 0.1959 \| \| 0.437 \| 83.0 \| 581 \| 1.6881 \| 0.565 \| 0.5687 \| 2.4665 \| 0.565 \| 0.5621 \| 0.1494 \| 0.1956 \| \| 0.437 \| 84.0 \| 588 \| 1.6897 \| 0.565 \| 0.5692 \| 2.4648 \| 0.565 \| 0.5626 \| 0.1716 \| 0.1969 \| \| 0.437 \| 85.0 \| 595 \| 1.6910 \| 0.5675 \| 0.5697 \| 2.4654 \| 0.5675 \| 0.5652 \| 0.1747 \| 0.1971 \| \| 0.437 \| 86.0 \| 602 \| 1.6905 \| 0.57 \| 0.5694 \| 2.4648 \| 0.57 \| 0.5667 \| 0.1659 \| 0.1959 \| \| 0.437 \| 87.0 \| 609 \| 1.6896 \| 0.5675 \| 0.5693 \| 2.4648 \| 0.5675 \| 0.5642 \| 0.1681 \| 0.1958 \| \| 0.437 \| 88.0 \| 616 \| 1.6902 \| 0.5675 \| 0.5695 \| 2.4655 \| 0.5675 \| 0.5642 \| 0.1709 \| 0.1961 \| \| 0.437 \| 89.0 \| 623 \| 1.6907 \| 0.5675 \| 0.5697 \| 2.4655 \| 0.5675 \| 0.5648 \| 0.1676 \| 0.1971 \| \| 0.437 \| 90.0 \| 630 \| 1.6903 \| 0.5675 \| 0.5694 \| 2.4657 \| 0.5675 \| 0.5648 \| 0.1699 \| 0.1966 \| \| 0.437 \| 91.0 \| 637 \| 1.6906 \| 0.565 \| 0.5696 \| 2.4656 \| 0.565 \| 0.5624 \| 0.1689 \| 0.1970 \| \| 0.437 \| 92.0 \| 644 \| 1.6904 \| 0.565 \| 0.5695 \| 2.4654 \| 0.565 \| 0.5622 \| 0.1668 \| 0.1970 \| \| 0.437 \| 93.0 \| 651 \| 1.6904 \| 0.5675 \| 0.5696 \| 2.4655 \| 0.5675 \| 0.5649 \| 0.1619 \| 0.1970 \| \| 0.437 \| 94.0 \| 658 \| 1.6903 \| 0.565 \| 0.5694 \| 2.4655 \| 0.565 \| 0.5622 \| 0.1662 \| 0.1969 \| \| 0.437 \| 95.0 \| 665 \| 1.6905 \| 0.565 \| 0.5695 \| 2.4653 \| 0.565 \| 0.5622 \| 0.1710 \| 0.1969 \| \| 0.437 \| 96.0 \| 672 \| 1.6905 \| 0.5675 \| 0.5695 \| 2.4655 \| 0.5675 \| 0.5648 \| 0.1638 \| 0.1968 \| \| 0.437 \| 97.0 \| 679 \| 1.6906 \| 0.565 \| 0.5695 \| 2.4654 \| 0.565 \| 0.5622 \| 0.1664 \| 0.1970 \| \| 0.437 \| 98.0 \| 686 \| 1.6906 \| 0.5675 \| 0.5695 \| 2.4655 \| 0.5675 \| 0.5648 \| 0.1638 \| 0.1969 \| \| 0.437 \| 99.0 \| 693 \| 1.6906 \| 0.5675 \| 0.5696 \| 2.4654 \| 0.5675 \| 0.5648 \| 0.1638 \| 0.1969 \| \| 0.437 \| 100.0 \| 700 \| 1.6906 \| 0.5675 \| 0.5696 \| 2.4654 \| 0.5675 \| 0.5648 \| 0.1638 \| 0.1969 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6224 - Accuracy: 0.555 - Brier Loss: 0.5813 - Nll: 2.4451 - F1 Micro: 0.555 - F1 Macro: 0.5481 - Ece: 0.1732 - Aurc: 0.2090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.2974 \| 0.04 \| 1.0725 \| 7.3803 \| 0.04 \| 0.0315 \| 0.2825 \| 0.9545 \| \| No log \| 2.0 \| 14 \| 3.3036 \| 0.095 \| 0.9476 \| 5.7516 \| 0.095 \| 0.0792 \| 0.1590 \| 0.8944 \| \| No log \| 3.0 \| 21 \| 2.9979 \| 0.215 \| 0.8918 \| 5.3309 \| 0.2150 \| 0.1763 \| 0.1514 \| 0.6635 \| \| No log \| 4.0 \| 28 \| 2.5846 \| 0.2875 \| 0.7979 \| 3.5812 \| 0.2875 \| 0.2707 \| 0.1619 \| 0.4840 \| \| No log \| 5.0 \| 35 \| 2.2908 \| 0.3925 \| 0.7162 \| 3.1082 \| 0.3925 \| 0.3675 \| 0.1724 \| 0.3500 \| \| No log \| 6.0 \| 42 \| 2.1582 \| 0.4275 \| 0.6903 \| 3.3486 \| 0.4275 \| 0.3933 \| 0.1723 \| 0.3106 \| \| No log \| 7.0 \| 49 \| 2.1910 \| 0.445 \| 0.7011 \| 2.9994 \| 0.445 \| 0.4233 \| 0.1889 \| 0.3105 \| \| No log \| 8.0 \| 56 \| 2.0287 \| 0.485 \| 0.6673 \| 2.8482 \| 0.485 \| 0.4803 \| 0.1781 \| 0.2848 \| \| No log \| 9.0 \| 63 \| 2.1037 \| 0.4775 \| 0.6684 \| 2.8143 \| 0.4775 \| 0.4715 \| 0.2010 \| 0.2690 \| \| No log \| 10.0 \| 70 \| 2.1168 \| 0.4825 \| 0.6846 \| 2.8143 \| 0.4825 \| 0.4703 \| 0.2082 \| 0.2818 \| \| No log \| 11.0 \| 77 \| 2.1094 \| 0.495 \| 0.6825 \| 2.9020 \| 0.495 \| 0.4834 \| 0.1990 \| 0.2632 \| \| No log \| 12.0 \| 84 \| 2.0835 \| 0.48 \| 0.6897 \| 2.7522 \| 0.48 \| 0.4651 \| 0.2261 \| 0.2741 \| \| No log \| 13.0 \| 91 \| 1.9606 \| 0.505 \| 0.6631 \| 2.4794 \| 0.505 \| 0.4988 \| 0.2034 \| 0.2712 \| \| No log \| 14.0 \| 98 \| 1.9519 \| 0.4975 \| 0.6567 \| 2.7608 \| 0.4975 \| 0.4833 \| 0.2042 \| 0.2563 \| \| No log \| 15.0 \| 105 \| 1.8794 \| 0.52 \| 0.6304 \| 2.6588 \| 0.52 \| 0.5121 \| 0.1814 \| 0.2337 \| \| No log \| 16.0 \| 112 \| 1.7934 \| 0.5375 \| 0.6191 \| 2.5142 \| 0.5375 \| 0.5234 \| 0.1853 \| 0.2272 \| \| No log \| 17.0 \| 119 \| 1.8110 \| 0.5225 \| 0.6242 \| 2.5106 \| 0.5225 \| 0.5071 \| 0.1918 \| 0.2336 \| \| No log \| 18.0 \| 126 \| 1.8027 \| 0.515 \| 0.6283 \| 2.4142 \| 0.515 \| 0.4983 \| 0.2020 \| 0.2359 \| \| No log \| 19.0 \| 133 \| 1.8123 \| 0.5375 \| 0.6318 \| 2.5551 \| 0.5375 \| 0.5235 \| 0.2132 \| 0.2358 \| \| No log \| 20.0 \| 140 \| 1.7937 \| 0.5225 \| 0.6292 \| 2.5237 \| 0.5225 \| 0.5145 \| 0.2070 \| 0.2336 \| \| No log \| 21.0 \| 147 \| 1.7272 \| 0.5175 \| 0.6046 \| 2.4278 \| 0.5175 \| 0.5103 \| 0.1806 \| 0.2286 \| \| No log \| 22.0 \| 154 \| 1.8337 \| 0.5325 \| 0.6396 \| 2.5603 \| 0.5325 \| 0.5136 \| 0.1884 \| 0.2405 \| \| No log \| 23.0 \| 161 \| 1.7416 \| 0.5275 \| 0.6102 \| 2.5228 \| 0.5275 \| 0.5077 \| 0.1784 \| 0.2232 \| \| No log \| 24.0 \| 168 \| 1.7036 \| 0.55 \| 0.6063 \| 2.4933 \| 0.55 \| 0.5380 \| 0.1776 \| 0.2230 \| \| No log \| 25.0 \| 175 \| 1.7330 \| 0.545 \| 0.6084 \| 2.4943 \| 0.545 \| 0.5365 \| 0.1989 \| 0.2251 \| \| No log \| 26.0 \| 182 \| 1.6911 \| 0.55 \| 0.5993 \| 2.4401 \| 0.55 \| 0.5416 \| 0.1792 \| 0.2208 \| \| No log \| 27.0 \| 189 \| 1.7329 \| 0.5475 \| 0.6162 \| 2.4824 \| 0.5475 \| 0.5380 \| 0.1830 \| 0.2317 \| \| No log \| 28.0 \| 196 \| 1.6890 \| 0.5475 \| 0.5992 \| 2.4828 \| 0.5475 \| 0.5401 \| 0.1725 \| 0.2178 \| \| No log \| 29.0 \| 203 \| 1.7256 \| 0.5425 \| 0.6124 \| 2.5121 \| 0.5425 \| 0.5299 \| 0.1765 \| 0.2260 \| \| No log \| 30.0 \| 210 \| 1.6854 \| 0.5375 \| 0.5952 \| 2.4275 \| 0.5375 \| 0.5282 \| 0.2015 \| 0.2163 \| \| No log \| 31.0 \| 217 \| 1.7010 \| 0.5475 \| 0.6030 \| 2.4832 \| 0.5475 \| 0.5385 \| 0.1862 \| 0.2236 \| \| No log \| 32.0 \| 224 \| 1.6840 \| 0.535 \| 0.5934 \| 2.4512 \| 0.535 \| 0.5310 \| 0.1794 \| 0.2182 \| \| No log \| 33.0 \| 231 \| 1.6808 \| 0.545 \| 0.6016 \| 2.4424 \| 0.545 \| 0.5396 \| 0.1828 \| 0.2222 \| \| No log \| 34.0 \| 238 \| 1.6965 \| 0.535 \| 0.6000 \| 2.5453 \| 0.535 \| 0.5270 \| 0.1846 \| 0.2243 \| \| No log \| 35.0 \| 245 \| 1.6650 \| 0.545 \| 0.5930 \| 2.4901 \| 0.545 \| 0.5418 \| 0.1706 \| 0.2130 \| \| No log \| 36.0 \| 252 \| 1.6494 \| 0.54 \| 0.5979 \| 2.4011 \| 0.54 \| 0.5319 \| 0.1731 \| 0.2201 \| \| No log \| 37.0 \| 259 \| 1.6738 \| 0.54 \| 0.5892 \| 2.4632 \| 0.54 \| 0.5257 \| 0.1781 \| 0.2088 \| \| No log \| 38.0 \| 266 \| 1.6502 \| 0.55 \| 0.5889 \| 2.4733 \| 0.55 \| 0.5416 \| 0.1957 \| 0.2087 \| \| No log \| 39.0 \| 273 \| 1.6539 \| 0.55 \| 0.5832 \| 2.5105 \| 0.55 \| 0.5424 \| 0.1584 \| 0.2120 \| \| No log \| 40.0 \| 280 \| 1.6399 \| 0.545 \| 0.5919 \| 2.4450 \| 0.545 \| 0.5439 \| 0.1877 \| 0.2168 \| \| No log \| 41.0 \| 287 \| 1.6968 \| 0.535 \| 0.6006 \| 2.5310 \| 0.535 \| 0.5281 \| 0.1895 \| 0.2221 \| \| No log \| 42.0 \| 294 \| 1.6430 \| 0.5425 \| 0.5955 \| 2.4020 \| 0.5425 \| 0.5449 \| 0.1722 \| 0.2203 \| \| No log \| 43.0 \| 301 \| 1.6743 \| 0.535 \| 0.5970 \| 2.5138 \| 0.535 \| 0.5239 \| 0.1869 \| 0.2167 \| \| No log \| 44.0 \| 308 \| 1.6544 \| 0.5475 \| 0.5949 \| 2.4259 \| 0.5475 \| 0.5408 \| 0.1651 \| 0.2164 \| \| No log \| 45.0 \| 315 \| 1.6763 \| 0.535 \| 0.5980 \| 2.4379 \| 0.535 \| 0.5249 \| 0.1868 \| 0.2174 \| \| No log \| 46.0 \| 322 \| 1.6509 \| 0.525 \| 0.5933 \| 2.4351 \| 0.525 \| 0.5137 \| 0.1861 \| 0.2194 \| \| No log \| 47.0 \| 329 \| 1.6530 \| 0.5475 \| 0.5929 \| 2.4628 \| 0.5475 \| 0.5419 \| 0.1859 \| 0.2148 \| \| No log \| 48.0 \| 336 \| 1.6410 \| 0.555 \| 0.5835 \| 2.4992 \| 0.555 \| 0.5490 \| 0.1805 \| 0.2110 \| \| No log \| 49.0 \| 343 \| 1.6398 \| 0.5525 \| 0.5857 \| 2.5060 \| 0.5525 \| 0.5409 \| 0.1706 \| 0.2101 \| \| No log \| 50.0 \| 350 \| 1.6343 \| 0.5525 \| 0.5814 \| 2.4890 \| 0.5525 \| 0.5442 \| 0.1608 \| 0.2065 \| \| No log \| 51.0 \| 357 \| 1.6335 \| 0.5475 \| 0.5846 \| 2.4407 \| 0.5475 \| 0.5392 \| 0.1720 \| 0.2109 \| \| No log \| 52.0 \| 364 \| 1.6309 \| 0.555 \| 0.5844 \| 2.4944 \| 0.555 \| 0.5488 \| 0.1697 \| 0.2091 \| \| No log \| 53.0 \| 371 \| 1.6308 \| 0.5575 \| 0.5826 \| 2.4815 \| 0.5575 \| 0.5505 \| 0.1704 \| 0.2080 \| \| No log \| 54.0 \| 378 \| 1.6279 \| 0.56 \| 0.5832 \| 2.4741 \| 0.56 \| 0.5525 \| 0.1724 \| 0.2067 \| \| No log \| 55.0 \| 385 \| 1.6226 \| 0.55 \| 0.5825 \| 2.4048 \| 0.55 \| 0.5425 \| 0.1656 \| 0.2094 \| \| No log \| 56.0 \| 392 \| 1.6141 \| 0.555 \| 0.5797 \| 2.4716 \| 0.555 \| 0.5474 \| 0.1813 \| 0.2076 \| \| No log \| 57.0 \| 399 \| 1.6179 \| 0.56 \| 0.5760 \| 2.4682 \| 0.56 \| 0.5549 \| 0.1474 \| 0.2030 \| \| No log \| 58.0 \| 406 \| 1.6278 \| 0.56 \| 0.5831 \| 2.4758 \| 0.56 \| 0.5540 \| 0.1681 \| 0.2075 \| \| No log \| 59.0 \| 413 \| 1.6257 \| 0.5525 \| 0.5817 \| 2.4462 \| 0.5525 \| 0.5455 \| 0.1648 \| 0.2084 \| \| No log \| 60.0 \| 420 \| 1.6306 \| 0.5575 \| 0.5861 \| 2.5090 \| 0.5575 \| 0.5505 \| 0.1687 \| 0.2108 \| \| No log \| 61.0 \| 427 \| 1.6314 \| 0.555 \| 0.5821 \| 2.5024 \| 0.555 \| 0.5480 \| 0.1725 \| 0.2092 \| \| No log \| 62.0 \| 434 \| 1.6322 \| 0.545 \| 0.5846 \| 2.4694 \| 0.545 \| 0.5407 \| 0.1848 \| 0.2123 \| \| No log \| 63.0 \| 441 \| 1.6229 \| 0.5575 \| 0.5829 \| 2.4413 \| 0.5575 \| 0.5508 \| 0.1698 \| 0.2098 \| \| No log \| 64.0 \| 448 \| 1.6187 \| 0.56 \| 0.5809 \| 2.4420 \| 0.56 \| 0.5522 \| 0.1848 \| 0.2083 \| \| No log \| 65.0 \| 455 \| 1.6160 \| 0.555 \| 0.5794 \| 2.4349 \| 0.555 \| 0.5506 \| 0.1761 \| 0.2076 \| \| No log \| 66.0 \| 462 \| 1.6254 \| 0.55 \| 0.5822 \| 2.4752 \| 0.55 \| 0.5432 \| 0.1629 \| 0.2086 \| \| No log \| 67.0 \| 469 \| 1.6259 \| 0.55 \| 0.5839 \| 2.4425 \| 0.55 \| 0.5423 \| 0.1769 \| 0.2113 \| \| No log \| 68.0 \| 476 \| 1.6245 \| 0.55 \| 0.5822 \| 2.4382 \| 0.55 \| 0.5421 \| 0.1736 \| 0.2091 \| \| No log \| 69.0 \| 483 \| 1.6243 \| 0.5575 \| 0.5830 \| 2.4422 \| 0.5575 \| 0.5497 \| 0.1808 \| 0.2094 \| \| No log \| 70.0 \| 490 \| 1.6223 \| 0.5575 \| 0.5810 \| 2.4787 \| 0.5575 \| 0.5507 \| 0.1556 \| 0.2089 \| \| No log \| 71.0 \| 497 \| 1.6204 \| 0.5575 \| 0.5809 \| 2.4408 \| 0.5575 \| 0.5515 \| 0.1555 \| 0.2083 \| \| 0.3852 \| 72.0 \| 504 \| 1.6225 \| 0.55 \| 0.5816 \| 2.4404 \| 0.55 \| 0.5424 \| 0.1750 \| 0.2104 \| \| 0.3852 \| 73.0 \| 511 \| 1.6237 \| 0.55 \| 0.5822 \| 2.4403 \| 0.55 \| 0.5429 \| 0.1772 \| 0.2107 \| \| 0.3852 \| 74.0 \| 518 \| 1.6220 \| 0.55 \| 0.5815 \| 2.4441 \| 0.55 \| 0.5420 \| 0.1649 \| 0.2105 \| \| 0.3852 \| 75.0 \| 525 \| 1.6228 \| 0.5475 \| 0.5818 \| 2.4736 \| 0.5475 \| 0.5405 \| 0.1882 \| 0.2109 \| \| 0.3852 \| 76.0 \| 532 \| 1.6224 \| 0.5525 \| 0.5814 \| 2.4442 \| 0.5525 \| 0.5446 \| 0.1817 \| 0.2108 \| \| 0.3852 \| 77.0 \| 539 \| 1.6225 \| 0.5525 \| 0.5815 \| 2.4431 \| 0.5525 \| 0.5448 \| 0.1798 \| 0.2098 \| \| 0.3852 \| 78.0 \| 546 \| 1.6213 \| 0.555 \| 0.5812 \| 2.4417 \| 0.555 \| 0.5471 \| 0.1680 \| 0.2096 \| \| 0.3852 \| 79.0 \| 553 \| 1.6208 \| 0.5575 \| 0.5808 \| 2.4423 \| 0.5575 \| 0.5501 \| 0.1784 \| 0.2082 \| \| 0.3852 \| 80.0 \| 560 \| 1.6218 \| 0.5525 \| 0.5811 \| 2.4425 \| 0.5525 \| 0.5447 \| 0.1683 \| 0.2095 \| \| 0.3852 \| 81.0 \| 567 \| 1.6225 \| 0.5525 \| 0.5814 \| 2.4429 \| 0.5525 \| 0.5447 \| 0.1856 \| 0.2098 \| \| 0.3852 \| 82.0 \| 574 \| 1.6222 \| 0.5575 \| 0.5812 \| 2.4469 \| 0.5575 \| 0.5501 \| 0.1953 \| 0.2085 \| \| 0.3852 \| 83.0 \| 581 \| 1.6219 \| 0.555 \| 0.5811 \| 2.4442 \| 0.555 \| 0.5471 \| 0.1940 \| 0.2093 \| \| 0.3852 \| 84.0 \| 588 \| 1.6220 \| 0.555 \| 0.5813 \| 2.4443 \| 0.555 \| 0.5471 \| 0.1867 \| 0.2095 \| \| 0.3852 \| 85.0 \| 595 \| 1.6223 \| 0.555 \| 0.5813 \| 2.4446 \| 0.555 \| 0.5471 \| 0.1885 \| 0.2094 \| \| 0.3852 \| 86.0 \| 602 \| 1.6222 \| 0.5525 \| 0.5812 \| 2.4448 \| 0.5525 \| 0.5447 \| 0.1749 \| 0.2095 \| \| 0.3852 \| 87.0 \| 609 \| 1.6222 \| 0.555 \| 0.5813 \| 2.4454 \| 0.555 \| 0.5481 \| 0.1745 \| 0.2091 \| \| 0.3852 \| 88.0 \| 616 \| 1.6222 \| 0.5575 \| 0.5813 \| 2.4446 \| 0.5575 \| 0.5504 \| 0.1767 \| 0.2087 \| \| 0.3852 \| 89.0 \| 623 \| 1.6222 \| 0.5575 \| 0.5813 \| 2.4445 \| 0.5575 \| 0.5504 \| 0.1839 \| 0.2087 \| \| 0.3852 \| 90.0 \| 630 \| 1.6221 \| 0.555 \| 0.5812 \| 2.4447 \| 0.555 \| 0.5481 \| 0.1814 \| 0.2091 \| \| 0.3852 \| 91.0 \| 637 \| 1.6222 \| 0.5575 \| 0.5813 \| 2.4446 \| 0.5575 \| 0.5504 \| 0.1790 \| 0.2087 \| \| 0.3852 \| 92.0 \| 644 \| 1.6222 \| 0.555 \| 0.5813 \| 2.4447 \| 0.555 \| 0.5481 \| 0.1755 \| 0.2091 \| \| 0.3852 \| 93.0 \| 651 \| 1.6223 \| 0.5575 \| 0.5813 \| 2.4446 \| 0.5575 \| 0.5504 \| 0.1747 \| 0.2087 \| \| 0.3852 \| 94.0 \| 658 \| 1.6223 \| 0.5575 \| 0.5813 \| 2.4449 \| 0.5575 \| 0.5504 \| 0.1747 \| 0.2087 \| \| 0.3852 \| 95.0 \| 665 \| 1.6223 \| 0.555 \| 0.5813 \| 2.4448 \| 0.555 \| 0.5481 \| 0.1732 \| 0.2090 \| \| 0.3852 \| 96.0 \| 672 \| 1.6224 \| 0.555 \| 0.5813 \| 2.4451 \| 0.555 \| 0.5481 \| 0.1720 \| 0.2091 \| \| 0.3852 \| 97.0 \| 679 \| 1.6223 \| 0.5575 \| 0.5813 \| 2.4451 \| 0.5575 \| 0.5504 \| 0.1735 \| 0.2088 \| \| 0.3852 \| 98.0 \| 686 \| 1.6223 \| 0.5575 \| 0.5813 \| 2.4451 \| 0.5575 \| 0.5504 \| 0.1747 \| 0.2087 \| \| 0.3852 \| 99.0 \| 693 \| 1.6224 \| 0.555 \| 0.5813 \| 2.4451 \| 0.555 \| 0.5481 \| 0.1732 \| 0.2090 \| \| 0.3852 \| 100.0 \| 700 \| 1.6224 \| 0.555 \| 0.5813 \| 2.4451 \| 0.555 \| 0.5481 \| 0.1732 \| 0.2090 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5750 - Accuracy: 0.5325 - Brier Loss: 0.5990 - Nll: 2.5263 - F1 Micro: 0.5325 - F1 Macro: 0.5240 - Ece: 0.1659 - Aurc: 0.2152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 3.9285 \| 0.04 \| 1.0722 \| 7.3572 \| 0.04 \| 0.0319 \| 0.2792 \| 0.9556 \| \| No log \| 2.0 \| 14 \| 3.0027 \| 0.095 \| 0.9510 \| 5.8779 \| 0.095 \| 0.0766 \| 0.1668 \| 0.8900 \| \| No log \| 3.0 \| 21 \| 2.6988 \| 0.225 \| 0.8896 \| 5.3750 \| 0.225 \| 0.1801 \| 0.1669 \| 0.6473 \| \| No log \| 4.0 \| 28 \| 2.3179 \| 0.285 \| 0.8016 \| 3.5658 \| 0.285 \| 0.2657 \| 0.1679 \| 0.4916 \| \| No log \| 5.0 \| 35 \| 2.0566 \| 0.37 \| 0.7203 \| 2.9834 \| 0.37 \| 0.3493 \| 0.1684 \| 0.3612 \| \| No log \| 6.0 \| 42 \| 1.9505 \| 0.4325 \| 0.6892 \| 3.0719 \| 0.4325 \| 0.4127 \| 0.1775 \| 0.3084 \| \| No log \| 7.0 \| 49 \| 1.9995 \| 0.4375 \| 0.7008 \| 3.1569 \| 0.4375 \| 0.4084 \| 0.2032 \| 0.3103 \| \| No log \| 8.0 \| 56 \| 1.9133 \| 0.445 \| 0.6906 \| 2.8574 \| 0.445 \| 0.4464 \| 0.2016 \| 0.3062 \| \| No log \| 9.0 \| 63 \| 1.9876 \| 0.4625 \| 0.6918 \| 2.9267 \| 0.4625 \| 0.4538 \| 0.2228 \| 0.2868 \| \| No log \| 10.0 \| 70 \| 2.0051 \| 0.4725 \| 0.6971 \| 2.9249 \| 0.4725 \| 0.4553 \| 0.2234 \| 0.2814 \| \| No log \| 11.0 \| 77 \| 2.1834 \| 0.465 \| 0.7319 \| 2.9998 \| 0.465 \| 0.4426 \| 0.2444 \| 0.3006 \| \| No log \| 12.0 \| 84 \| 1.9953 \| 0.4825 \| 0.7087 \| 2.7128 \| 0.4825 \| 0.4731 \| 0.2386 \| 0.2980 \| \| No log \| 13.0 \| 91 \| 1.8834 \| 0.4975 \| 0.6771 \| 2.6879 \| 0.4975 \| 0.4954 \| 0.2240 \| 0.2748 \| \| No log \| 14.0 \| 98 \| 1.9647 \| 0.4675 \| 0.6987 \| 2.8305 \| 0.4675 \| 0.4429 \| 0.2409 \| 0.2902 \| \| No log \| 15.0 \| 105 \| 1.8810 \| 0.5 \| 0.6785 \| 2.6402 \| 0.5 \| 0.4847 \| 0.2171 \| 0.2725 \| \| No log \| 16.0 \| 112 \| 1.8777 \| 0.4875 \| 0.6877 \| 2.6940 \| 0.4875 \| 0.4871 \| 0.2210 \| 0.2846 \| \| No log \| 17.0 \| 119 \| 1.9260 \| 0.4925 \| 0.6796 \| 2.7055 \| 0.4925 \| 0.4834 \| 0.2012 \| 0.2744 \| \| No log \| 18.0 \| 126 \| 1.7864 \| 0.505 \| 0.6547 \| 2.6724 \| 0.505 \| 0.4912 \| 0.2081 \| 0.2434 \| \| No log \| 19.0 \| 133 \| 1.7618 \| 0.4975 \| 0.6430 \| 2.5951 \| 0.4975 \| 0.4915 \| 0.2172 \| 0.2490 \| \| No log \| 20.0 \| 140 \| 1.7496 \| 0.515 \| 0.6513 \| 2.5263 \| 0.515 \| 0.5025 \| 0.1975 \| 0.2502 \| \| No log \| 21.0 \| 147 \| 1.7082 \| 0.5275 \| 0.6438 \| 2.4039 \| 0.5275 \| 0.5224 \| 0.2017 \| 0.2450 \| \| No log \| 22.0 \| 154 \| 1.7482 \| 0.4975 \| 0.6682 \| 2.5194 \| 0.4975 \| 0.4911 \| 0.2247 \| 0.2571 \| \| No log \| 23.0 \| 161 \| 1.7377 \| 0.5075 \| 0.6482 \| 2.4136 \| 0.5075 \| 0.4900 \| 0.2221 \| 0.2396 \| \| No log \| 24.0 \| 168 \| 1.7094 \| 0.515 \| 0.6372 \| 2.5605 \| 0.515 \| 0.5083 \| 0.2137 \| 0.2474 \| \| No log \| 25.0 \| 175 \| 1.6884 \| 0.5175 \| 0.6422 \| 2.5270 \| 0.5175 \| 0.5104 \| 0.2111 \| 0.2444 \| \| No log \| 26.0 \| 182 \| 1.6489 \| 0.5275 \| 0.6246 \| 2.5344 \| 0.5275 \| 0.5211 \| 0.2066 \| 0.2333 \| \| No log \| 27.0 \| 189 \| 1.6165 \| 0.53 \| 0.6191 \| 2.5418 \| 0.53 \| 0.5256 \| 0.2021 \| 0.2305 \| \| No log \| 28.0 \| 196 \| 1.6316 \| 0.5275 \| 0.6181 \| 2.6568 \| 0.5275 \| 0.5212 \| 0.2004 \| 0.2300 \| \| No log \| 29.0 \| 203 \| 1.6595 \| 0.5175 \| 0.6306 \| 2.4298 \| 0.5175 \| 0.5096 \| 0.2020 \| 0.2427 \| \| No log \| 30.0 \| 210 \| 1.6193 \| 0.5325 \| 0.6157 \| 2.5455 \| 0.5325 \| 0.5272 \| 0.1779 \| 0.2278 \| \| No log \| 31.0 \| 217 \| 1.6517 \| 0.5325 \| 0.6274 \| 2.4579 \| 0.5325 \| 0.5259 \| 0.2006 \| 0.2362 \| \| No log \| 32.0 \| 224 \| 1.6434 \| 0.5325 \| 0.6167 \| 2.5805 \| 0.5325 \| 0.5229 \| 0.1995 \| 0.2273 \| \| No log \| 33.0 \| 231 \| 1.6660 \| 0.5225 \| 0.6269 \| 2.6794 \| 0.5225 \| 0.5132 \| 0.2244 \| 0.2283 \| \| No log \| 34.0 \| 238 \| 1.6353 \| 0.515 \| 0.6194 \| 2.6085 \| 0.515 \| 0.5069 \| 0.1839 \| 0.2303 \| \| No log \| 35.0 \| 245 \| 1.5920 \| 0.5325 \| 0.6051 \| 2.5645 \| 0.5325 \| 0.5248 \| 0.1868 \| 0.2208 \| \| No log \| 36.0 \| 252 \| 1.5909 \| 0.54 \| 0.6028 \| 2.4786 \| 0.54 \| 0.5323 \| 0.1902 \| 0.2194 \| \| No log \| 37.0 \| 259 \| 1.5730 \| 0.5425 \| 0.5983 \| 2.4877 \| 0.5425 \| 0.5368 \| 0.1799 \| 0.2177 \| \| No log \| 38.0 \| 266 \| 1.5800 \| 0.535 \| 0.6029 \| 2.4736 \| 0.535 \| 0.5282 \| 0.1761 \| 0.2196 \| \| No log \| 39.0 \| 273 \| 1.5594 \| 0.54 \| 0.5955 \| 2.5093 \| 0.54 \| 0.5327 \| 0.1900 \| 0.2126 \| \| No log \| 40.0 \| 280 \| 1.5685 \| 0.53 \| 0.5979 \| 2.6068 \| 0.53 \| 0.5208 \| 0.1893 \| 0.2173 \| \| No log \| 41.0 \| 287 \| 1.5757 \| 0.53 \| 0.5995 \| 2.5655 \| 0.53 \| 0.5218 \| 0.1862 \| 0.2164 \| \| No log \| 42.0 \| 294 \| 1.5797 \| 0.535 \| 0.6039 \| 2.5445 \| 0.535 \| 0.5273 \| 0.1834 \| 0.2182 \| \| No log \| 43.0 \| 301 \| 1.5900 \| 0.53 \| 0.6074 \| 2.5201 \| 0.53 \| 0.5189 \| 0.1747 \| 0.2206 \| \| No log \| 44.0 \| 308 \| 1.5760 \| 0.5325 \| 0.5986 \| 2.4974 \| 0.5325 \| 0.5225 \| 0.1870 \| 0.2148 \| \| No log \| 45.0 \| 315 \| 1.5768 \| 0.53 \| 0.6013 \| 2.5174 \| 0.53 \| 0.5204 \| 0.1979 \| 0.2158 \| \| No log \| 46.0 \| 322 \| 1.5774 \| 0.53 \| 0.6011 \| 2.5199 \| 0.53 \| 0.5206 \| 0.1882 \| 0.2165 \| \| No log \| 47.0 \| 329 \| 1.5714 \| 0.54 \| 0.5983 \| 2.5329 \| 0.54 \| 0.5303 \| 0.1884 \| 0.2135 \| \| No log \| 48.0 \| 336 \| 1.5834 \| 0.5325 \| 0.6026 \| 2.5253 \| 0.5325 \| 0.5238 \| 0.1658 \| 0.2190 \| \| No log \| 49.0 \| 343 \| 1.5724 \| 0.5375 \| 0.5979 \| 2.5569 \| 0.5375 \| 0.5299 \| 0.1617 \| 0.2151 \| \| No log \| 50.0 \| 350 \| 1.5685 \| 0.5375 \| 0.5985 \| 2.5189 \| 0.5375 \| 0.5285 \| 0.1919 \| 0.2151 \| \| No log \| 51.0 \| 357 \| 1.5708 \| 0.54 \| 0.5986 \| 2.5002 \| 0.54 \| 0.5305 \| 0.1755 \| 0.2149 \| \| No log \| 52.0 \| 364 \| 1.5665 \| 0.535 \| 0.5977 \| 2.5224 \| 0.535 \| 0.5267 \| 0.1842 \| 0.2160 \| \| No log \| 53.0 \| 371 \| 1.5713 \| 0.5325 \| 0.5993 \| 2.5515 \| 0.5325 \| 0.5250 \| 0.1753 \| 0.2160 \| \| No log \| 54.0 \| 378 \| 1.5693 \| 0.535 \| 0.5986 \| 2.5516 \| 0.535 \| 0.5276 \| 0.1841 \| 0.2158 \| \| No log \| 55.0 \| 385 \| 1.5693 \| 0.5375 \| 0.5984 \| 2.5190 \| 0.5375 \| 0.5285 \| 0.1842 \| 0.2144 \| \| No log \| 56.0 \| 392 \| 1.5725 \| 0.535 \| 0.5992 \| 2.5527 \| 0.535 \| 0.5262 \| 0.1776 \| 0.2150 \| \| No log \| 57.0 \| 399 \| 1.5674 \| 0.5425 \| 0.5976 \| 2.5502 \| 0.5425 \| 0.5326 \| 0.1902 \| 0.2137 \| \| No log \| 58.0 \| 406 \| 1.5675 \| 0.5375 \| 0.5974 \| 2.5517 \| 0.5375 \| 0.5288 \| 0.1794 \| 0.2139 \| \| No log \| 59.0 \| 413 \| 1.5713 \| 0.535 \| 0.5988 \| 2.5515 \| 0.535 \| 0.5257 \| 0.1791 \| 0.2147 \| \| No log \| 60.0 \| 420 \| 1.5729 \| 0.535 \| 0.5988 \| 2.5512 \| 0.535 \| 0.5262 \| 0.1796 \| 0.2148 \| \| No log \| 61.0 \| 427 \| 1.5702 \| 0.5375 \| 0.5976 \| 2.5521 \| 0.5375 \| 0.5281 \| 0.1817 \| 0.2139 \| \| No log \| 62.0 \| 434 \| 1.5728 \| 0.535 \| 0.5988 \| 2.5514 \| 0.535 \| 0.5266 \| 0.1722 \| 0.2149 \| \| No log \| 63.0 \| 441 \| 1.5720 \| 0.5325 \| 0.5985 \| 2.5206 \| 0.5325 \| 0.5231 \| 0.1790 \| 0.2149 \| \| No log \| 64.0 \| 448 \| 1.5704 \| 0.5325 \| 0.5975 \| 2.5510 \| 0.5325 \| 0.5236 \| 0.1706 \| 0.2139 \| \| No log \| 65.0 \| 455 \| 1.5724 \| 0.5325 \| 0.5986 \| 2.5225 \| 0.5325 \| 0.5236 \| 0.1557 \| 0.2148 \| \| No log \| 66.0 \| 462 \| 1.5718 \| 0.5325 \| 0.5985 \| 2.5246 \| 0.5325 \| 0.5241 \| 0.1772 \| 0.2148 \| \| No log \| 67.0 \| 469 \| 1.5710 \| 0.5325 \| 0.5981 \| 2.5511 \| 0.5325 \| 0.5237 \| 0.1625 \| 0.2146 \| \| No log \| 68.0 \| 476 \| 1.5716 \| 0.54 \| 0.5981 \| 2.5001 \| 0.54 \| 0.5304 \| 0.1622 \| 0.2141 \| \| No log \| 69.0 \| 483 \| 1.5732 \| 0.5325 \| 0.5988 \| 2.5517 \| 0.5325 \| 0.5232 \| 0.1641 \| 0.2150 \| \| No log \| 70.0 \| 490 \| 1.5733 \| 0.5325 \| 0.5987 \| 2.5522 \| 0.5325 \| 0.5237 \| 0.1715 \| 0.2149 \| \| No log \| 71.0 \| 497 \| 1.5729 \| 0.5325 \| 0.5985 \| 2.5523 \| 0.5325 \| 0.5241 \| 0.1670 \| 0.2147 \| \| 0.3153 \| 72.0 \| 504 \| 1.5730 \| 0.5325 \| 0.5987 \| 2.5236 \| 0.5325 \| 0.5237 \| 0.1656 \| 0.2149 \| \| 0.3153 \| 73.0 \| 511 \| 1.5723 \| 0.5325 \| 0.5985 \| 2.5212 \| 0.5325 \| 0.5238 \| 0.1893 \| 0.2145 \| \| 0.3153 \| 74.0 \| 518 \| 1.5738 \| 0.5325 \| 0.5989 \| 2.5515 \| 0.5325 \| 0.5238 \| 0.1744 \| 0.2147 \| \| 0.3153 \| 75.0 \| 525 \| 1.5740 \| 0.5325 \| 0.5988 \| 2.5318 \| 0.5325 \| 0.5237 \| 0.1683 \| 0.2150 \| \| 0.3153 \| 76.0 \| 532 \| 1.5734 \| 0.535 \| 0.5985 \| 2.5525 \| 0.535 \| 0.5261 \| 0.1763 \| 0.2145 \| \| 0.3153 \| 77.0 \| 539 \| 1.5740 \| 0.5325 \| 0.5989 \| 2.5516 \| 0.5325 \| 0.5243 \| 0.1726 \| 0.2149 \| \| 0.3153 \| 78.0 \| 546 \| 1.5738 \| 0.5325 \| 0.5987 \| 2.5289 \| 0.5325 \| 0.5241 \| 0.1692 \| 0.2148 \| \| 0.3153 \| 79.0 \| 553 \| 1.5736 \| 0.5325 \| 0.5987 \| 2.5255 \| 0.5325 \| 0.5242 \| 0.1807 \| 0.2147 \| \| 0.3153 \| 80.0 \| 560 \| 1.5739 \| 0.5325 \| 0.5988 \| 2.5522 \| 0.5325 \| 0.5237 \| 0.1769 \| 0.2150 \| \| 0.3153 \| 81.0 \| 567 \| 1.5743 \| 0.5325 \| 0.5989 \| 2.5519 \| 0.5325 \| 0.5238 \| 0.1837 \| 0.2151 \| \| 0.3153 \| 82.0 \| 574 \| 1.5742 \| 0.5325 \| 0.5989 \| 2.5232 \| 0.5325 \| 0.5240 \| 0.1712 \| 0.2149 \| \| 0.3153 \| 83.0 \| 581 \| 1.5744 \| 0.5325 \| 0.5989 \| 2.5256 \| 0.5325 \| 0.5239 \| 0.1803 \| 0.2151 \| \| 0.3153 \| 84.0 \| 588 \| 1.5741 \| 0.5325 \| 0.5988 \| 2.5233 \| 0.5325 \| 0.5233 \| 0.1655 \| 0.2147 \| \| 0.3153 \| 85.0 \| 595 \| 1.5747 \| 0.5325 \| 0.5990 \| 2.5274 \| 0.5325 \| 0.5237 \| 0.1696 \| 0.2152 \| \| 0.3153 \| 86.0 \| 602 \| 1.5747 \| 0.5325 \| 0.5989 \| 2.5263 \| 0.5325 \| 0.5238 \| 0.1689 \| 0.2150 \| \| 0.3153 \| 87.0 \| 609 \| 1.5745 \| 0.5325 \| 0.5989 \| 2.5251 \| 0.5325 \| 0.5237 \| 0.1654 \| 0.2149 \| \| 0.3153 \| 88.0 \| 616 \| 1.5747 \| 0.5325 \| 0.5989 \| 2.5283 \| 0.5325 \| 0.5241 \| 0.1693 \| 0.2151 \| \| 0.3153 \| 89.0 \| 623 \| 1.5748 \| 0.5325 \| 0.5990 \| 2.5275 \| 0.5325 \| 0.5239 \| 0.1596 \| 0.2152 \| \| 0.3153 \| 90.0 \| 630 \| 1.5749 \| 0.5325 \| 0.5990 \| 2.5278 \| 0.5325 \| 0.5240 \| 0.1602 \| 0.2151 \| \| 0.3153 \| 91.0 \| 637 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5337 \| 0.5325 \| 0.5239 \| 0.1623 \| 0.2152 \| \| 0.3153 \| 92.0 \| 644 \| 1.5749 \| 0.5325 \| 0.5990 \| 2.5272 \| 0.5325 \| 0.5238 \| 0.1653 \| 0.2151 \| \| 0.3153 \| 93.0 \| 651 \| 1.5751 \| 0.5325 \| 0.5990 \| 2.5281 \| 0.5325 \| 0.5240 \| 0.1663 \| 0.2149 \| \| 0.3153 \| 94.0 \| 658 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5249 \| 0.5325 \| 0.5239 \| 0.1715 \| 0.2152 \| \| 0.3153 \| 95.0 \| 665 \| 1.5749 \| 0.535 \| 0.5990 \| 2.5257 \| 0.535 \| 0.5263 \| 0.1625 \| 0.2149 \| \| 0.3153 \| 96.0 \| 672 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5266 \| 0.5325 \| 0.5239 \| 0.1655 \| 0.2151 \| \| 0.3153 \| 97.0 \| 679 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5268 \| 0.5325 \| 0.5239 \| 0.1686 \| 0.2152 \| \| 0.3153 \| 98.0 \| 686 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5275 \| 0.5325 \| 0.5240 \| 0.1664 \| 0.2152 \| \| 0.3153 \| 99.0 \| 693 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5269 \| 0.5325 \| 0.5240 \| 0.1678 \| 0.2152 \| \| 0.3153 \| 100.0 \| 700 \| 1.5750 \| 0.5325 \| 0.5990 \| 2.5263 \| 0.5325 \| 0.5240 \| 0.1659 \| 0.2152 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]
ayanban011/vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0411 - Accuracy: 0.8333 - Brier Loss: 0.3084 - Nll: 1.3568 - F1 Micro: 0.8333 - F1 Macro: 0.8183 - Ece: 0.1563 - Aurc: 0.0847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.99 \| 43 \| 0.7544 \| 0.7960 \| 0.3088 \| 1.3391 \| 0.7960 \| 0.7715 \| 0.1991 \| 0.0817 \| \| No log \| 2.0 \| 87 \| 0.7158 \| 0.8218 \| 0.2920 \| 1.1888 \| 0.8218 \| 0.7941 \| 0.1863 \| 0.0741 \| \| No log \| 2.98 \| 130 \| 0.7144 \| 0.7989 \| 0.2932 \| 1.2958 \| 0.7989 \| 0.7701 \| 0.1628 \| 0.0749 \| \| No log \| 3.99 \| 174 \| 0.6762 \| 0.8305 \| 0.2749 \| 1.1916 \| 0.8305 \| 0.8076 \| 0.1844 \| 0.0678 \| \| No log \| 4.98 \| 217 \| 0.6710 \| 0.8362 \| 0.2745 \| 1.0739 \| 0.8362 \| 0.8076 \| 0.1696 \| 0.0664 \| \| No log \| 5.99 \| 261 \| 0.6532 \| 0.8362 \| 0.2675 \| 1.0011 \| 0.8362 \| 0.8115 \| 0.1750 \| 0.0602 \| \| No log \| 6.98 \| 304 \| 0.6404 \| 0.8362 \| 0.2635 \| 1.0072 \| 0.8362 \| 0.8106 \| 0.1714 \| 0.0633 \| \| No log \| 7.99 \| 348 \| 0.6635 \| 0.8218 \| 0.2707 \| 1.0903 \| 0.8218 \| 0.8030 \| 0.1513 \| 0.0770 \| \| No log \| 9.0 \| 392 \| 0.6167 \| 0.8420 \| 0.2534 \| 1.0176 \| 0.8420 \| 0.8259 \| 0.1613 \| 0.0796 \| \| No log \| 9.99 \| 435 \| 0.6496 \| 0.8276 \| 0.2703 \| 0.9646 \| 0.8276 \| 0.8085 \| 0.1643 \| 0.0588 \| \| No log \| 11.0 \| 479 \| 0.6091 \| 0.8506 \| 0.2467 \| 1.1036 \| 0.8506 \| 0.8308 \| 0.1483 \| 0.0650 \| \| 0.4309 \| 11.98 \| 522 \| 0.6075 \| 0.8420 \| 0.2483 \| 0.9144 \| 0.8420 \| 0.8246 \| 0.1391 \| 0.0519 \| \| 0.4309 \| 12.99 \| 566 \| 0.6164 \| 0.8276 \| 0.2576 \| 0.9703 \| 0.8276 \| 0.8092 \| 0.1467 \| 0.0645 \| \| 0.4309 \| 13.98 \| 609 \| 0.5893 \| 0.8592 \| 0.2347 \| 1.1493 \| 0.8592 \| 0.8483 \| 0.1347 \| 0.0715 \| \| 0.4309 \| 14.99 \| 653 \| 0.6123 \| 0.8477 \| 0.2485 \| 1.1889 \| 0.8477 \| 0.8232 \| 0.1587 \| 0.0764 \| \| 0.4309 \| 16.0 \| 697 \| 0.6352 \| 0.8420 \| 0.2615 \| 1.1999 \| 0.8420 \| 0.8403 \| 0.1368 \| 0.0668 \| \| 0.4309 \| 16.99 \| 740 \| 0.6329 \| 0.8333 \| 0.2625 \| 1.1748 \| 0.8333 \| 0.8249 \| 0.1267 \| 0.0744 \| \| 0.4309 \| 18.0 \| 784 \| 0.6350 \| 0.8448 \| 0.2590 \| 1.2154 \| 0.8448 \| 0.8386 \| 0.1423 \| 0.0688 \| \| 0.4309 \| 18.98 \| 827 \| 0.5892 \| 0.8592 \| 0.2383 \| 1.1001 \| 0.8592 \| 0.8515 \| 0.1293 \| 0.0630 \| \| 0.4309 \| 19.99 \| 871 \| 0.5981 \| 0.8477 \| 0.2476 \| 1.0104 \| 0.8477 \| 0.8375 \| 0.1345 \| 0.0630 \| \| 0.4309 \| 20.98 \| 914 \| 0.6484 \| 0.8420 \| 0.2642 \| 1.3553 \| 0.8420 \| 0.8292 \| 0.1490 \| 0.0770 \| \| 0.4309 \| 21.99 \| 958 \| 0.6298 \| 0.8305 \| 0.2657 \| 1.1220 \| 0.8305 \| 0.8208 \| 0.1292 \| 0.0670 \| \| 0.1285 \| 22.98 \| 1001 \| 0.6325 \| 0.8391 \| 0.2633 \| 1.2549 \| 0.8391 \| 0.8362 \| 0.1328 \| 0.0708 \| \| 0.1285 \| 23.99 \| 1045 \| 0.6032 \| 0.8534 \| 0.2486 \| 1.1258 \| 0.8534 \| 0.8444 \| 0.1229 \| 0.0706 \| \| 0.1285 \| 25.0 \| 1089 \| 0.6080 \| 0.8534 \| 0.2460 \| 1.2033 \| 0.8534 \| 0.8414 \| 0.1257 \| 0.0755 \| \| 0.1285 \| 25.99 \| 1132 \| 0.6321 \| 0.8391 \| 0.2667 \| 1.2242 \| 0.8391 \| 0.8355 \| 0.1349 \| 0.0697 \| \| 0.1285 \| 27.0 \| 1176 \| 0.6325 \| 0.8592 \| 0.2522 \| 1.2029 \| 0.8592 \| 0.8493 \| 0.1278 \| 0.0778 \| \| 0.1285 \| 27.98 \| 1219 \| 0.6585 \| 0.8534 \| 0.2546 \| 1.3669 \| 0.8534 \| 0.8378 \| 0.1368 \| 0.0890 \| \| 0.1285 \| 28.99 \| 1263 \| 0.6302 \| 0.8563 \| 0.2517 \| 1.2419 \| 0.8563 \| 0.8508 \| 0.1294 \| 0.0751 \| \| 0.1285 \| 29.98 \| 1306 \| 0.6663 \| 0.8477 \| 0.2637 \| 1.4132 \| 0.8477 \| 0.8339 \| 0.1399 \| 0.0828 \| \| 0.1285 \| 30.99 \| 1350 \| 0.7063 \| 0.8362 \| 0.2799 \| 1.4323 \| 0.8362 \| 0.8330 \| 0.1441 \| 0.0863 \| \| 0.1285 \| 32.0 \| 1394 \| 0.6564 \| 0.8506 \| 0.2570 \| 1.1583 \| 0.8506 \| 0.8417 \| 0.1358 \| 0.0847 \| \| 0.1285 \| 32.99 \| 1437 \| 0.6738 \| 0.8477 \| 0.2647 \| 1.3855 \| 0.8477 \| 0.8398 \| 0.1305 \| 0.0775 \| \| 0.1285 \| 34.0 \| 1481 \| 0.6528 \| 0.8563 \| 0.2559 \| 1.2601 \| 0.8563 \| 0.8462 \| 0.1310 \| 0.0789 \| \| 0.0385 \| 34.98 \| 1524 \| 0.6534 \| 0.8563 \| 0.2537 \| 1.2931 \| 0.8563 \| 0.8461 \| 0.1241 \| 0.0773 \| \| 0.0385 \| 35.99 \| 1568 \| 0.6541 \| 0.8534 \| 0.2525 \| 1.2589 \| 0.8534 \| 0.8449 \| 0.1315 \| 0.0833 \| \| 0.0385 \| 36.98 \| 1611 \| 0.6769 \| 0.8592 \| 0.2545 \| 1.4351 \| 0.8592 \| 0.8492 \| 0.1242 \| 0.0792 \| \| 0.0385 \| 37.99 \| 1655 \| 0.6824 \| 0.8592 \| 0.2576 \| 1.2241 \| 0.8592 \| 0.8472 \| 0.1327 \| 0.0810 \| \| 0.0385 \| 38.98 \| 1698 \| 0.6843 \| 0.8563 \| 0.2589 \| 1.3394 \| 0.8563 \| 0.8450 \| 0.1311 \| 0.0802 \| \| 0.0385 \| 39.99 \| 1742 \| 0.6964 \| 0.8506 \| 0.2630 \| 1.2625 \| 0.8506 \| 0.8405 \| 0.1310 \| 0.0789 \| \| 0.0385 \| 41.0 \| 1786 \| 0.7051 \| 0.8534 \| 0.2671 \| 1.3296 \| 0.8534 \| 0.8434 \| 0.1353 \| 0.0794 \| \| 0.0385 \| 41.99 \| 1829 \| 0.7006 \| 0.8506 \| 0.2645 \| 1.2965 \| 0.8506 \| 0.8400 \| 0.1373 \| 0.0796 \| \| 0.0385 \| 43.0 \| 1873 \| 0.7054 \| 0.8563 \| 0.2646 \| 1.2973 \| 0.8563 \| 0.8450 \| 0.1313 \| 0.0790 \| \| 0.0385 \| 43.98 \| 1916 \| 0.7143 \| 0.8506 \| 0.2673 \| 1.2640 \| 0.8506 \| 0.8399 \| 0.1359 \| 0.0803 \| \| 0.0385 \| 44.99 \| 1960 \| 0.7168 \| 0.8534 \| 0.2665 \| 1.3058 \| 0.8534 \| 0.8429 \| 0.1389 \| 0.0820 \| \| 0.0206 \| 45.98 \| 2003 \| 0.7204 \| 0.8506 \| 0.2669 \| 1.3009 \| 0.8506 \| 0.8384 \| 0.1336 \| 0.0805 \| \| 0.0206 \| 46.99 \| 2047 \| 0.7265 \| 0.8534 \| 0.2683 \| 1.2633 \| 0.8534 \| 0.8415 \| 0.1319 \| 0.0806 \| \| 0.0206 \| 48.0 \| 2091 \| 0.7311 \| 0.8506 \| 0.2695 \| 1.2725 \| 0.8506 \| 0.8396 \| 0.1372 \| 0.0811 \| \| 0.0206 \| 48.99 \| 2134 \| 0.7384 \| 0.8477 \| 0.2729 \| 1.3385 \| 0.8477 \| 0.8364 \| 0.1387 \| 0.0807 \| \| 0.0206 \| 50.0 \| 2178 \| 0.7383 \| 0.8534 \| 0.2695 \| 1.1951 \| 0.8534 \| 0.8406 \| 0.1344 \| 0.0827 \| \| 0.0206 \| 50.98 \| 2221 \| 0.7440 \| 0.8506 \| 0.2740 \| 1.3360 \| 0.8506 \| 0.8394 \| 0.1418 \| 0.0812 \| \| 0.0206 \| 51.99 \| 2265 \| 0.7455 \| 0.8506 \| 0.2727 \| 1.2704 \| 0.8506 \| 0.8388 \| 0.1351 \| 0.0816 \| \| 0.0206 \| 52.98 \| 2308 \| 0.7474 \| 0.8506 \| 0.2708 \| 1.2622 \| 0.8506 \| 0.8384 \| 0.1334 \| 0.0823 \| \| 0.0206 \| 53.99 \| 2352 \| 0.7581 \| 0.8477 \| 0.2750 \| 1.3446 \| 0.8477 \| 0.8374 \| 0.1406 \| 0.0826 \| \| 0.0206 \| 54.98 \| 2395 \| 0.7571 \| 0.8477 \| 0.2751 \| 1.3703 \| 0.8477 \| 0.8363 \| 0.1378 \| 0.0814 \| \| 0.0206 \| 55.99 \| 2439 \| 0.7618 \| 0.8477 \| 0.2752 \| 1.3702 \| 0.8477 \| 0.8363 \| 0.1363 \| 0.0827 \| \| 0.0206 \| 57.0 \| 2483 \| 0.7638 \| 0.8477 \| 0.2749 \| 1.3774 \| 0.8477 \| 0.8363 \| 0.1394 \| 0.0819 \| \| 0.0135 \| 57.99 \| 2526 \| 0.7693 \| 0.8477 \| 0.2760 \| 1.3370 \| 0.8477 \| 0.8363 \| 0.1378 \| 0.0824 \| \| 0.0135 \| 59.0 \| 2570 \| 0.7724 \| 0.8448 \| 0.2779 \| 1.3710 \| 0.8448 \| 0.8344 \| 0.1431 \| 0.0823 \| \| 0.0135 \| 59.98 \| 2613 \| 0.7780 \| 0.8477 \| 0.2784 \| 1.3328 \| 0.8477 \| 0.8363 \| 0.1463 \| 0.0828 \| \| 0.0135 \| 60.99 \| 2657 \| 0.7818 \| 0.8477 \| 0.2795 \| 1.3289 \| 0.8477 \| 0.8363 \| 0.1466 \| 0.0828 \| \| 0.0135 \| 61.98 \| 2700 \| 0.7847 \| 0.8420 \| 0.2805 \| 1.3308 \| 0.8420 \| 0.8308 \| 0.1418 \| 0.0830 \| \| 0.0135 \| 62.99 \| 2744 \| 0.7851 \| 0.8448 \| 0.2782 \| 1.3650 \| 0.8448 \| 0.8344 \| 0.1411 \| 0.0834 \| \| 0.0135 \| 64.0 \| 2788 \| 0.7925 \| 0.8420 \| 0.2829 \| 1.4383 \| 0.8420 \| 0.8319 \| 0.1425 \| 0.0821 \| \| 0.0135 \| 64.99 \| 2831 \| 0.7959 \| 0.8448 \| 0.2826 \| 1.4130 \| 0.8448 \| 0.8353 \| 0.1431 \| 0.0826 \| \| 0.0135 \| 66.0 \| 2875 \| 0.7989 \| 0.8420 \| 0.2821 \| 1.4040 \| 0.8420 \| 0.8285 \| 0.1446 \| 0.0833 \| \| 0.0135 \| 66.98 \| 2918 \| 0.7996 \| 0.8477 \| 0.2807 \| 1.3296 \| 0.8477 \| 0.8363 \| 0.1464 \| 0.0837 \| \| 0.0135 \| 67.99 \| 2962 \| 0.8042 \| 0.8448 \| 0.2824 \| 1.3637 \| 0.8448 \| 0.8344 \| 0.1434 \| 0.0837 \| \| 0.0097 \| 68.98 \| 3005 \| 0.8095 \| 0.8391 \| 0.2845 \| 1.3635 \| 0.8391 \| 0.8275 \| 0.1468 \| 0.0835 \| \| 0.0097 \| 69.99 \| 3049 \| 0.8073 \| 0.8448 \| 0.2824 \| 1.3640 \| 0.8448 \| 0.8344 \| 0.1413 \| 0.0833 \| \| 0.0097 \| 70.98 \| 3092 \| 0.8140 \| 0.8477 \| 0.2834 \| 1.3617 \| 0.8477 \| 0.8363 \| 0.1444 \| 0.0837 \| \| 0.0097 \| 71.99 \| 3136 \| 0.8152 \| 0.8420 \| 0.2842 \| 1.4009 \| 0.8420 \| 0.8277 \| 0.1439 \| 0.0840 \| \| 0.0097 \| 73.0 \| 3180 \| 0.8163 \| 0.8391 \| 0.2858 \| 1.4029 \| 0.8391 \| 0.8246 \| 0.1482 \| 0.0836 \| \| 0.0097 \| 73.99 \| 3223 \| 0.8192 \| 0.8391 \| 0.2844 \| 1.3644 \| 0.8391 \| 0.8240 \| 0.1475 \| 0.0843 \| \| 0.0097 \| 75.0 \| 3267 \| 0.8225 \| 0.8448 \| 0.2836 \| 1.3593 \| 0.8448 \| 0.8344 \| 0.1473 \| 0.0847 \| \| 0.0097 \| 75.98 \| 3310 \| 0.8267 \| 0.8362 \| 0.2859 \| 1.3642 \| 0.8362 \| 0.8207 \| 0.1473 \| 0.0840 \| \| 0.0097 \| 76.99 \| 3354 \| 0.8275 \| 0.8391 \| 0.2847 \| 1.3618 \| 0.8391 \| 0.8240 \| 0.1450 \| 0.0849 \| \| 0.0097 \| 77.98 \| 3397 \| 0.8325 \| 0.8362 \| 0.2879 \| 1.3686 \| 0.8362 \| 0.8207 \| 0.1491 \| 0.0843 \| \| 0.0097 \| 78.99 \| 3441 \| 0.8389 \| 0.8448 \| 0.2885 \| 1.3629 \| 0.8448 \| 0.8329 \| 0.1504 \| 0.0833 \| \| 0.0097 \| 80.0 \| 3485 \| 0.8420 \| 0.8420 \| 0.2887 \| 1.3610 \| 0.8420 \| 0.8261 \| 0.1458 \| 0.0837 \| \| 0.0073 \| 80.99 \| 3528 \| 0.8452 \| 0.8362 \| 0.2900 \| 1.4064 \| 0.8362 \| 0.8221 \| 0.1488 \| 0.0833 \| \| 0.0073 \| 82.0 \| 3572 \| 0.8492 \| 0.8362 \| 0.2898 \| 1.4076 \| 0.8362 \| 0.8221 \| 0.1500 \| 0.0837 \| \| 0.0073 \| 82.98 \| 3615 \| 0.8478 \| 0.8362 \| 0.2895 \| 1.3609 \| 0.8362 \| 0.8207 \| 0.1485 \| 0.0847 \| \| 0.0073 \| 83.99 \| 3659 \| 0.8483 \| 0.8391 \| 0.2880 \| 1.3622 \| 0.8391 \| 0.8243 \| 0.1480 \| 0.0842 \| \| 0.0073 \| 84.98 \| 3702 \| 0.8534 \| 0.8420 \| 0.2892 \| 1.3609 \| 0.8420 \| 0.8261 \| 0.1468 \| 0.0843 \| \| 0.0073 \| 85.99 \| 3746 \| 0.8547 \| 0.8333 \| 0.2898 \| 1.4028 \| 0.8333 \| 0.8186 \| 0.1513 \| 0.0846 \| \| 0.0073 \| 86.98 \| 3789 \| 0.8618 \| 0.8391 \| 0.2906 \| 1.3597 \| 0.8391 \| 0.8243 \| 0.1445 \| 0.0846 \| \| 0.0073 \| 87.99 \| 3833 \| 0.8594 \| 0.8420 \| 0.2885 \| 1.3265 \| 0.8420 \| 0.8311 \| 0.1462 \| 0.0848 \| \| 0.0073 \| 89.0 \| 3877 \| 0.8669 \| 0.8391 \| 0.2911 \| 1.3592 \| 0.8391 \| 0.8243 \| 0.1471 \| 0.0843 \| \| 0.0073 \| 89.99 \| 3920 \| 0.8664 \| 0.8391 \| 0.2901 \| 1.3597 \| 0.8391 \| 0.8243 \| 0.1468 \| 0.0852 \| \| 0.0073 \| 91.0 \| 3964 \| 0.8678 \| 0.8420 \| 0.2905 \| 1.3253 \| 0.8420 \| 0.8296 \| 0.1462 \| 0.0854 \| \| 0.0057 \| 91.98 \| 4007 \| 0.8719 \| 0.8391 \| 0.2909 \| 1.3585 \| 0.8391 \| 0.8243 \| 0.1475 \| 0.0853 \| \| 0.0057 \| 92.99 \| 4051 \| 0.8768 \| 0.8391 \| 0.2930 \| 1.3595 \| 0.8391 \| 0.8243 \| 0.1493 \| 0.0852 \| \| 0.0057 \| 93.98 \| 4094 \| 0.8785 \| 0.8333 \| 0.2928 \| 1.4034 \| 0.8333 \| 0.8203 \| 0.1529 \| 0.0849 \| \| 0.0057 \| 94.99 \| 4138 \| 0.8859 \| 0.8333 \| 0.2942 \| 1.3684 \| 0.8333 \| 0.8183 \| 0.1543 \| 0.0844 \| \| 0.0057 \| 96.0 \| 4182 \| 0.8839 \| 0.8362 \| 0.2937 \| 1.3597 \| 0.8362 \| 0.8221 \| 0.1497 \| 0.0852 \| \| 0.0057 \| 96.99 \| 4225 \| 0.8864 \| 0.8333 \| 0.2940 \| 1.4012 \| 0.8333 \| 0.8203 \| 0.1532 \| 0.0850 \| \| 0.0057 \| 98.0 \| 4269 \| 0.8879 \| 0.8362 \| 0.2941 \| 1.3607 \| 0.8362 \| 0.8221 \| 0.1504 \| 0.0849 \| \| 0.0057 \| 98.98 \| 4312 \| 0.8921 \| 0.8333 \| 0.2954 \| 1.3609 \| 0.8333 \| 0.8183 \| 0.1521 \| 0.0851 \| \| 0.0057 \| 99.99 \| 4356 \| 0.8949 \| 0.8391 \| 0.2945 \| 1.3575 \| 0.8391 \| 0.8243 \| 0.1491 \| 0.0854 \| \| 0.0057 \| 100.98 \| 4399 \| 0.8945 \| 0.8362 \| 0.2945 \| 1.3591 \| 0.8362 \| 0.8221 \| 0.1500 \| 0.0856 \| \| 0.0057 \| 101.99 \| 4443 \| 0.8985 \| 0.8333 \| 0.2944 \| 1.3599 \| 0.8333 \| 0.8183 \| 0.1530 \| 0.0854 \| \| 0.0057 \| 102.98 \| 4486 \| 0.8987 \| 0.8391 \| 0.2951 \| 1.3586 \| 0.8391 \| 0.8246 \| 0.1499 \| 0.0850 \| \| 0.0045 \| 103.99 \| 4530 \| 0.9025 \| 0.8362 \| 0.2957 \| 1.3592 \| 0.8362 \| 0.8221 \| 0.1510 \| 0.0857 \| \| 0.0045 \| 105.0 \| 4574 \| 0.9082 \| 0.8305 \| 0.2972 \| 1.3625 \| 0.8305 \| 0.8165 \| 0.1568 \| 0.0852 \| \| 0.0045 \| 105.99 \| 4617 \| 0.9087 \| 0.8362 \| 0.2958 \| 1.3579 \| 0.8362 \| 0.8221 \| 0.1505 \| 0.0858 \| \| 0.0045 \| 107.0 \| 4661 \| 0.9105 \| 0.8305 \| 0.2977 \| 1.3619 \| 0.8305 \| 0.8165 \| 0.1561 \| 0.0844 \| \| 0.0045 \| 107.98 \| 4704 \| 0.9136 \| 0.8305 \| 0.2978 \| 1.3994 \| 0.8305 \| 0.8165 \| 0.1559 \| 0.0851 \| \| 0.0045 \| 108.99 \| 4748 \| 0.9148 \| 0.8391 \| 0.2968 \| 1.3573 \| 0.8391 \| 0.8243 \| 0.1504 \| 0.0856 \| \| 0.0045 \| 109.98 \| 4791 \| 0.9188 \| 0.8333 \| 0.2974 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1532 \| 0.0850 \| \| 0.0045 \| 110.99 \| 4835 \| 0.9164 \| 0.8362 \| 0.2959 \| 1.3595 \| 0.8362 \| 0.8221 \| 0.1507 \| 0.0857 \| \| 0.0045 \| 112.0 \| 4879 \| 0.9221 \| 0.8333 \| 0.2977 \| 1.3573 \| 0.8333 \| 0.8183 \| 0.1550 \| 0.0857 \| \| 0.0045 \| 112.99 \| 4922 \| 0.9256 \| 0.8305 \| 0.2990 \| 1.3599 \| 0.8305 \| 0.8165 \| 0.1574 \| 0.0852 \| \| 0.0045 \| 114.0 \| 4966 \| 0.9284 \| 0.8305 \| 0.2994 \| 1.3610 \| 0.8305 \| 0.8165 \| 0.1572 \| 0.0848 \| \| 0.0037 \| 114.98 \| 5009 \| 0.9312 \| 0.8333 \| 0.2998 \| 1.3565 \| 0.8333 \| 0.8183 \| 0.1537 \| 0.0857 \| \| 0.0037 \| 115.99 \| 5053 \| 0.9322 \| 0.8333 \| 0.2995 \| 1.3583 \| 0.8333 \| 0.8183 \| 0.1543 \| 0.0852 \| \| 0.0037 \| 116.98 \| 5096 \| 0.9385 \| 0.8305 \| 0.3007 \| 1.3593 \| 0.8305 \| 0.8165 \| 0.1577 \| 0.0852 \| \| 0.0037 \| 117.99 \| 5140 \| 0.9386 \| 0.8305 \| 0.3009 \| 1.4329 \| 0.8305 \| 0.8165 \| 0.1582 \| 0.0851 \| \| 0.0037 \| 118.98 \| 5183 \| 0.9386 \| 0.8333 \| 0.2996 \| 1.3570 \| 0.8333 \| 0.8183 \| 0.1542 \| 0.0855 \| \| 0.0037 \| 119.99 \| 5227 \| 0.9406 \| 0.8333 \| 0.2995 \| 1.3554 \| 0.8333 \| 0.8183 \| 0.1540 \| 0.0848 \| \| 0.0037 \| 121.0 \| 5271 \| 0.9442 \| 0.8305 \| 0.3006 \| 1.3589 \| 0.8305 \| 0.8165 \| 0.1570 \| 0.0849 \| \| 0.0037 \| 121.99 \| 5314 \| 0.9435 \| 0.8333 \| 0.3000 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1546 \| 0.0855 \| \| 0.0037 \| 123.0 \| 5358 \| 0.9456 \| 0.8333 \| 0.2996 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1544 \| 0.0848 \| \| 0.0037 \| 123.98 \| 5401 \| 0.9490 \| 0.8333 \| 0.3008 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1547 \| 0.0850 \| \| 0.0037 \| 124.99 \| 5445 \| 0.9500 \| 0.8333 \| 0.3011 \| 1.3592 \| 0.8333 \| 0.8183 \| 0.1551 \| 0.0846 \| \| 0.0037 \| 125.98 \| 5488 \| 0.9513 \| 0.8333 \| 0.3003 \| 1.3549 \| 0.8333 \| 0.8183 \| 0.1544 \| 0.0845 \| \| 0.0031 \| 126.99 \| 5532 \| 0.9575 \| 0.8305 \| 0.3024 \| 1.3580 \| 0.8305 \| 0.8165 \| 0.1581 \| 0.0849 \| \| 0.0031 \| 128.0 \| 5576 \| 0.9593 \| 0.8305 \| 0.3025 \| 1.4028 \| 0.8305 \| 0.8165 \| 0.1591 \| 0.0851 \| \| 0.0031 \| 128.99 \| 5619 \| 0.9594 \| 0.8305 \| 0.3021 \| 1.3619 \| 0.8305 \| 0.8165 \| 0.1579 \| 0.0849 \| \| 0.0031 \| 130.0 \| 5663 \| 0.9628 \| 0.8305 \| 0.3025 \| 1.3589 \| 0.8305 \| 0.8165 \| 0.1587 \| 0.0847 \| \| 0.0031 \| 130.98 \| 5706 \| 0.9652 \| 0.8305 \| 0.3031 \| 1.3599 \| 0.8305 \| 0.8165 \| 0.1593 \| 0.0844 \| \| 0.0031 \| 131.99 \| 5750 \| 0.9646 \| 0.8362 \| 0.3005 \| 1.3353 \| 0.8362 \| 0.8205 \| 0.1520 \| 0.0851 \| \| 0.0031 \| 132.98 \| 5793 \| 0.9658 \| 0.8333 \| 0.3021 \| 1.3562 \| 0.8333 \| 0.8183 \| 0.1555 \| 0.0849 \| \| 0.0031 \| 133.99 \| 5837 \| 0.9698 \| 0.8333 \| 0.3023 \| 1.3545 \| 0.8333 \| 0.8183 \| 0.1554 \| 0.0845 \| \| 0.0031 \| 134.98 \| 5880 \| 0.9716 \| 0.8333 \| 0.3032 \| 1.3559 \| 0.8333 \| 0.8183 \| 0.1555 \| 0.0852 \| \| 0.0031 \| 135.99 \| 5924 \| 0.9736 \| 0.8305 \| 0.3037 \| 1.3624 \| 0.8305 \| 0.8165 \| 0.1584 \| 0.0849 \| \| 0.0031 \| 137.0 \| 5968 \| 0.9760 \| 0.8333 \| 0.3039 \| 1.3575 \| 0.8333 \| 0.8183 \| 0.1551 \| 0.0845 \| \| 0.0026 \| 137.99 \| 6011 \| 0.9789 \| 0.8305 \| 0.3041 \| 1.3569 \| 0.8305 \| 0.8165 \| 0.1592 \| 0.0848 \| \| 0.0026 \| 139.0 \| 6055 \| 0.9801 \| 0.8305 \| 0.3040 \| 1.3574 \| 0.8305 \| 0.8165 \| 0.1598 \| 0.0854 \| \| 0.0026 \| 139.98 \| 6098 \| 0.9806 \| 0.8333 \| 0.3035 \| 1.3552 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0852 \| \| 0.0026 \| 140.99 \| 6142 \| 0.9835 \| 0.8333 \| 0.3041 \| 1.3574 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0846 \| \| 0.0026 \| 141.98 \| 6185 \| 0.9838 \| 0.8333 \| 0.3037 \| 1.3549 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0849 \| \| 0.0026 \| 142.99 \| 6229 \| 0.9872 \| 0.8333 \| 0.3044 \| 1.3544 \| 0.8333 \| 0.8183 \| 0.1557 \| 0.0851 \| \| 0.0026 \| 144.0 \| 6273 \| 0.9900 \| 0.8305 \| 0.3056 \| 1.3654 \| 0.8305 \| 0.8165 \| 0.1597 \| 0.0847 \| \| 0.0026 \| 144.99 \| 6316 \| 0.9907 \| 0.8333 \| 0.3049 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0854 \| \| 0.0026 \| 146.0 \| 6360 \| 0.9896 \| 0.8333 \| 0.3044 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0843 \| \| 0.0026 \| 146.98 \| 6403 \| 0.9938 \| 0.8333 \| 0.3053 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0844 \| \| 0.0026 \| 147.99 \| 6447 \| 0.9962 \| 0.8305 \| 0.3056 \| 1.3615 \| 0.8305 \| 0.8165 \| 0.1594 \| 0.0844 \| \| 0.0026 \| 148.98 \| 6490 \| 0.9954 \| 0.8305 \| 0.3051 \| 1.3601 \| 0.8305 \| 0.8165 \| 0.1590 \| 0.0847 \| \| 0.0022 \| 149.99 \| 6534 \| 0.9961 \| 0.8333 \| 0.3043 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1554 \| 0.0847 \| \| 0.0022 \| 150.98 \| 6577 \| 1.0026 \| 0.8333 \| 0.3059 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0853 \| \| 0.0022 \| 151.99 \| 6621 \| 1.0004 \| 0.8333 \| 0.3049 \| 1.3544 \| 0.8333 \| 0.8183 \| 0.1566 \| 0.0847 \| \| 0.0022 \| 153.0 \| 6665 \| 1.0024 \| 0.8305 \| 0.3058 \| 1.3606 \| 0.8305 \| 0.8165 \| 0.1595 \| 0.0846 \| \| 0.0022 \| 153.99 \| 6708 \| 1.0054 \| 0.8305 \| 0.3064 \| 1.3598 \| 0.8305 \| 0.8165 \| 0.1591 \| 0.0848 \| \| 0.0022 \| 155.0 \| 6752 \| 1.0053 \| 0.8333 \| 0.3054 \| 1.3548 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0845 \| \| 0.0022 \| 155.98 \| 6795 \| 1.0068 \| 0.8333 \| 0.3053 \| 1.3548 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.0022 \| 156.99 \| 6839 \| 1.0076 \| 0.8333 \| 0.3055 \| 1.3551 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0844 \| \| 0.0022 \| 157.98 \| 6882 \| 1.0105 \| 0.8333 \| 0.3059 \| 1.3546 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0845 \| \| 0.0022 \| 158.99 \| 6926 \| 1.0114 \| 0.8333 \| 0.3061 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1559 \| 0.0851 \| \| 0.0022 \| 160.0 \| 6970 \| 1.0108 \| 0.8333 \| 0.3061 \| 1.3586 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0848 \| \| 0.002 \| 160.99 \| 7013 \| 1.0129 \| 0.8333 \| 0.3064 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0845 \| \| 0.002 \| 162.0 \| 7057 \| 1.0141 \| 0.8333 \| 0.3060 \| 1.3542 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0845 \| \| 0.002 \| 162.98 \| 7100 \| 1.0150 \| 0.8333 \| 0.3063 \| 1.3555 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0847 \| \| 0.002 \| 163.99 \| 7144 \| 1.0181 \| 0.8305 \| 0.3071 \| 1.3616 \| 0.8305 \| 0.8165 \| 0.1587 \| 0.0847 \| \| 0.002 \| 164.98 \| 7187 \| 1.0197 \| 0.8305 \| 0.3073 \| 1.3610 \| 0.8305 \| 0.8165 \| 0.1585 \| 0.0847 \| \| 0.002 \| 165.99 \| 7231 \| 1.0203 \| 0.8333 \| 0.3071 \| 1.3566 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0846 \| \| 0.002 \| 166.98 \| 7274 \| 1.0214 \| 0.8333 \| 0.3070 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0845 \| \| 0.002 \| 167.99 \| 7318 \| 1.0211 \| 0.8333 \| 0.3067 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.002 \| 169.0 \| 7362 \| 1.0255 \| 0.8305 \| 0.3077 \| 1.3564 \| 0.8305 \| 0.8165 \| 0.1592 \| 0.0846 \| \| 0.002 \| 169.99 \| 7405 \| 1.0238 \| 0.8333 \| 0.3066 \| 1.3535 \| 0.8333 \| 0.8183 \| 0.1567 \| 0.0844 \| \| 0.002 \| 171.0 \| 7449 \| 1.0258 \| 0.8333 \| 0.3075 \| 1.3580 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.002 \| 171.98 \| 7492 \| 1.0260 \| 0.8333 \| 0.3073 \| 1.3594 \| 0.8333 \| 0.8183 \| 0.1559 \| 0.0846 \| \| 0.0018 \| 172.99 \| 7536 \| 1.0281 \| 0.8305 \| 0.3077 \| 1.3584 \| 0.8305 \| 0.8165 \| 0.1586 \| 0.0847 \| \| 0.0018 \| 173.98 \| 7579 \| 1.0274 \| 0.8333 \| 0.3073 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0851 \| \| 0.0018 \| 174.99 \| 7623 \| 1.0323 \| 0.8305 \| 0.3082 \| 1.3577 \| 0.8305 \| 0.8165 \| 0.1596 \| 0.0848 \| \| 0.0018 \| 176.0 \| 7667 \| 1.0303 \| 0.8333 \| 0.3076 \| 1.3579 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0846 \| \| 0.0018 \| 176.99 \| 7710 \| 1.0325 \| 0.8333 \| 0.3081 \| 1.3567 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0845 \| \| 0.0018 \| 178.0 \| 7754 \| 1.0319 \| 0.8333 \| 0.3077 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1560 \| 0.0847 \| \| 0.0018 \| 178.98 \| 7797 \| 1.0340 \| 0.8333 \| 0.3081 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0018 \| 179.99 \| 7841 \| 1.0331 \| 0.8333 \| 0.3072 \| 1.3550 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0018 \| 180.98 \| 7884 \| 1.0346 \| 0.8333 \| 0.3079 \| 1.3563 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0847 \| \| 0.0018 \| 181.99 \| 7928 \| 1.0344 \| 0.8333 \| 0.3079 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0847 \| \| 0.0018 \| 182.98 \| 7971 \| 1.0363 \| 0.8333 \| 0.3080 \| 1.3556 \| 0.8333 \| 0.8183 \| 0.1566 \| 0.0850 \| \| 0.0016 \| 183.99 \| 8015 \| 1.0368 \| 0.8333 \| 0.3080 \| 1.3569 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0847 \| \| 0.0016 \| 185.0 \| 8059 \| 1.0369 \| 0.8333 \| 0.3080 \| 1.3563 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0016 \| 185.99 \| 8102 \| 1.0373 \| 0.8333 \| 0.3080 \| 1.3565 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0850 \| \| 0.0016 \| 187.0 \| 8146 \| 1.0377 \| 0.8333 \| 0.3080 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1561 \| 0.0846 \| \| 0.0016 \| 187.98 \| 8189 \| 1.0392 \| 0.8333 \| 0.3084 \| 1.3577 \| 0.8333 \| 0.8183 \| 0.1565 \| 0.0846 \| \| 0.0016 \| 188.99 \| 8233 \| 1.0391 \| 0.8333 \| 0.3082 \| 1.3564 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0848 \| \| 0.0016 \| 189.98 \| 8276 \| 1.0393 \| 0.8333 \| 0.3081 \| 1.3561 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0847 \| \| 0.0016 \| 190.99 \| 8320 \| 1.0398 \| 0.8333 \| 0.3084 \| 1.3582 \| 0.8333 \| 0.8183 \| 0.1562 \| 0.0846 \| \| 0.0016 \| 192.0 \| 8364 \| 1.0405 \| 0.8333 \| 0.3083 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 192.99 \| 8407 \| 1.0401 \| 0.8333 \| 0.3082 \| 1.3558 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 194.0 \| 8451 \| 1.0407 \| 0.8333 \| 0.3083 \| 1.3564 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0016 \| 194.98 \| 8494 \| 1.0414 \| 0.8333 \| 0.3086 \| 1.3573 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0847 \| \| 0.0015 \| 195.99 \| 8538 \| 1.0410 \| 0.8333 \| 0.3084 \| 1.3567 \| 0.8333 \| 0.8183 \| 0.1564 \| 0.0848 \| \| 0.0015 \| 196.98 \| 8581 \| 1.0411 \| 0.8333 \| 0.3084 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0846 \| \| 0.0015 \| 197.42 \| 8600 \| 1.0411 \| 0.8333 \| 0.3084 \| 1.3568 \| 0.8333 \| 0.8183 \| 0.1563 \| 0.0847 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hbenitez/AV_classifier1	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hbenitez/AV_classifier1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6840 - Validation Loss: 0.6659 - Train Accuracy: 0.5 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 80, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6840 \| 0.6659 \| 0.5 \| 0 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.13.0-rc2 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "brake", "dont_brake" ]
arunboss/triage_R5_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # triage_R5_model This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0123 - Accuracy: 0.6837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0452 \| 1.0 \| 159 \| 1.9622 \| 0.3814 \| \| 1.7034 \| 2.0 \| 319 \| 1.5695 \| 0.4923 \| \| 1.441 \| 3.0 \| 479 \| 1.4427 \| 0.5433 \| \| 1.2908 \| 4.0 \| 639 \| 1.2970 \| 0.5895 \| \| 1.2294 \| 5.0 \| 798 \| 1.2293 \| 0.6071 \| \| 1.1097 \| 6.0 \| 958 \| 1.1892 \| 0.6300 \| \| 1.0342 \| 7.0 \| 1118 \| 1.1048 \| 0.6546 \| \| 0.9644 \| 8.0 \| 1278 \| 1.0731 \| 0.6678 \| \| 0.8534 \| 9.0 \| 1437 \| 1.0367 \| 0.6766 \| \| 0.8037 \| 10.0 \| 1597 \| 1.0211 \| 0.6802 \| \| 0.7765 \| 11.0 \| 1757 \| 1.0073 \| 0.6885 \| \| 0.7658 \| 11.94 \| 1908 \| 1.0123 \| 0.6837 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "acne and rosacea", "actinic keratosis bcc other malignant lesions", "allergic reactions & similar", "atopic dermatitis eczema", "bullous disease", "hair loss alopecia and other hair diseases", "melanoma (skin cancer) nevi and moles", "nail fungus and other nail disease", "pigmentation disorders", "psoriasis & related diseases", "seborrheic keratoses and other benign tumors", "tinea ringworm candida", "urticaria (hives)", "vascular tumors", "vasculitis", "viral exanthems", "warts (molluscum) and other viral infections" ]
jordyvl/vit-tiny_tobacco3482_simkd__tNone_gNone__logits	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_tobacco3482_simkd__tNone_gNone__logits This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0388 - Accuracy: 0.82 - Brier Loss: 0.7227 - Nll: 2.6079 - F1 Micro: 0.82 - F1 Macro: 0.7960 - Ece: 0.6182 - Aurc: 0.0575 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 100 \| 0.0492 \| 0.03 \| 0.9002 \| 15.7630 \| 0.03 \| 0.0130 \| 0.1184 \| 0.9407 \| \| No log \| 2.0 \| 200 \| 0.0479 \| 0.045 \| 0.8991 \| 12.1839 \| 0.045 \| 0.0140 \| 0.1350 \| 0.9268 \| \| No log \| 3.0 \| 300 \| 0.0472 \| 0.305 \| 0.8968 \| 12.2323 \| 0.305 \| 0.1551 \| 0.2930 \| 0.5596 \| \| No log \| 4.0 \| 400 \| 0.0463 \| 0.305 \| 0.8938 \| 10.0711 \| 0.305 \| 0.1809 \| 0.3077 \| 0.5037 \| \| 0.0541 \| 5.0 \| 500 \| 0.0453 \| 0.325 \| 0.8898 \| 9.3778 \| 0.325 \| 0.2046 \| 0.3193 \| 0.4698 \| \| 0.0541 \| 6.0 \| 600 \| 0.0442 \| 0.4 \| 0.8787 \| 9.6363 \| 0.4000 \| 0.2403 \| 0.3713 \| 0.3759 \| \| 0.0541 \| 7.0 \| 700 \| 0.0433 \| 0.4 \| 0.8639 \| 9.3483 \| 0.4000 \| 0.2510 \| 0.3685 \| 0.3742 \| \| 0.0541 \| 8.0 \| 800 \| 0.0428 \| 0.475 \| 0.8521 \| 9.0007 \| 0.4750 \| 0.2666 \| 0.4177 \| 0.2978 \| \| 0.0541 \| 9.0 \| 900 \| 0.0422 \| 0.515 \| 0.8438 \| 7.6934 \| 0.515 \| 0.3644 \| 0.4480 \| 0.2431 \| \| 0.0451 \| 10.0 \| 1000 \| 0.0417 \| 0.515 \| 0.8210 \| 7.6243 \| 0.515 \| 0.3743 \| 0.4387 \| 0.2146 \| \| 0.0451 \| 11.0 \| 1100 \| 0.0411 \| 0.68 \| 0.8190 \| 3.6435 \| 0.68 \| 0.5226 \| 0.5677 \| 0.1272 \| \| 0.0451 \| 12.0 \| 1200 \| 0.0403 \| 0.655 \| 0.7803 \| 5.4537 \| 0.655 \| 0.5379 \| 0.5202 \| 0.1352 \| \| 0.0451 \| 13.0 \| 1300 \| 0.0398 \| 0.75 \| 0.7745 \| 4.1150 \| 0.75 \| 0.6543 \| 0.5945 \| 0.0749 \| \| 0.0451 \| 14.0 \| 1400 \| 0.0390 \| 0.77 \| 0.7561 \| 2.8538 \| 0.7700 \| 0.6703 \| 0.6085 \| 0.0753 \| \| 0.0406 \| 15.0 \| 1500 \| 0.0392 \| 0.745 \| 0.7637 \| 3.7047 \| 0.745 \| 0.6673 \| 0.5796 \| 0.1060 \| \| 0.0406 \| 16.0 \| 1600 \| 0.0398 \| 0.73 \| 0.7603 \| 4.3010 \| 0.7300 \| 0.6681 \| 0.5693 \| 0.0949 \| \| 0.0406 \| 17.0 \| 1700 \| 0.0399 \| 0.705 \| 0.7610 \| 3.6375 \| 0.705 \| 0.6564 \| 0.5590 \| 0.0923 \| \| 0.0406 \| 18.0 \| 1800 \| 0.0397 \| 0.705 \| 0.7536 \| 4.2628 \| 0.705 \| 0.6170 \| 0.5472 \| 0.1332 \| \| 0.0406 \| 19.0 \| 1900 \| 0.0390 \| 0.745 \| 0.7400 \| 2.8861 \| 0.745 \| 0.6686 \| 0.5644 \| 0.1070 \| \| 0.0379 \| 20.0 \| 2000 \| 0.0394 \| 0.785 \| 0.7238 \| 3.3111 \| 0.785 \| 0.7255 \| 0.5995 \| 0.0695 \| \| 0.0379 \| 21.0 \| 2100 \| 0.0396 \| 0.76 \| 0.7419 \| 3.1935 \| 0.76 \| 0.7463 \| 0.5885 \| 0.0793 \| \| 0.0379 \| 22.0 \| 2200 \| 0.0396 \| 0.785 \| 0.7423 \| 3.7954 \| 0.785 \| 0.7737 \| 0.6043 \| 0.0873 \| \| 0.0379 \| 23.0 \| 2300 \| 0.0395 \| 0.78 \| 0.7321 \| 3.8067 \| 0.78 \| 0.7491 \| 0.5885 \| 0.0908 \| \| 0.0379 \| 24.0 \| 2400 \| 0.0387 \| 0.8 \| 0.7228 \| 2.9339 \| 0.8000 \| 0.7758 \| 0.6038 \| 0.0609 \| \| 0.037 \| 25.0 \| 2500 \| 0.0387 \| 0.795 \| 0.7222 \| 2.6252 \| 0.795 \| 0.7601 \| 0.6094 \| 0.0606 \| \| 0.037 \| 26.0 \| 2600 \| 0.0387 \| 0.8 \| 0.7241 \| 2.6253 \| 0.8000 \| 0.7628 \| 0.6110 \| 0.0607 \| \| 0.037 \| 27.0 \| 2700 \| 0.0387 \| 0.795 \| 0.7235 \| 2.4818 \| 0.795 \| 0.7616 \| 0.6093 \| 0.0629 \| \| 0.037 \| 28.0 \| 2800 \| 0.0387 \| 0.795 \| 0.7245 \| 2.6226 \| 0.795 \| 0.7586 \| 0.6032 \| 0.0604 \| \| 0.037 \| 29.0 \| 2900 \| 0.0387 \| 0.805 \| 0.7253 \| 2.7588 \| 0.805 \| 0.7725 \| 0.6144 \| 0.0609 \| \| 0.0364 \| 30.0 \| 3000 \| 0.0387 \| 0.805 \| 0.7233 \| 2.4956 \| 0.805 \| 0.7701 \| 0.6204 \| 0.0594 \| \| 0.0364 \| 31.0 \| 3100 \| 0.0387 \| 0.81 \| 0.7241 \| 2.7695 \| 0.81 \| 0.7797 \| 0.6188 \| 0.0602 \| \| 0.0364 \| 32.0 \| 3200 \| 0.0386 \| 0.81 \| 0.7239 \| 2.6185 \| 0.81 \| 0.7797 \| 0.6190 \| 0.0580 \| \| 0.0364 \| 33.0 \| 3300 \| 0.0387 \| 0.805 \| 0.7238 \| 2.9106 \| 0.805 \| 0.7717 \| 0.6182 \| 0.0586 \| \| 0.0364 \| 34.0 \| 3400 \| 0.0386 \| 0.805 \| 0.7231 \| 2.9062 \| 0.805 \| 0.7725 \| 0.6133 \| 0.0590 \| \| 0.0364 \| 35.0 \| 3500 \| 0.0387 \| 0.805 \| 0.7247 \| 2.7645 \| 0.805 \| 0.7717 \| 0.6141 \| 0.0590 \| \| 0.0364 \| 36.0 \| 3600 \| 0.0386 \| 0.805 \| 0.7238 \| 2.9152 \| 0.805 \| 0.7717 \| 0.6104 \| 0.0578 \| \| 0.0364 \| 37.0 \| 3700 \| 0.0387 \| 0.805 \| 0.7229 \| 2.9094 \| 0.805 \| 0.7717 \| 0.6142 \| 0.0588 \| \| 0.0364 \| 38.0 \| 3800 \| 0.0386 \| 0.805 \| 0.7237 \| 2.9185 \| 0.805 \| 0.7717 \| 0.6173 \| 0.0565 \| \| 0.0364 \| 39.0 \| 3900 \| 0.0386 \| 0.805 \| 0.7230 \| 2.9178 \| 0.805 \| 0.7717 \| 0.6131 \| 0.0578 \| \| 0.0364 \| 40.0 \| 4000 \| 0.0386 \| 0.805 \| 0.7233 \| 2.9155 \| 0.805 \| 0.7717 \| 0.6131 \| 0.0561 \| \| 0.0364 \| 41.0 \| 4100 \| 0.0386 \| 0.805 \| 0.7235 \| 2.9142 \| 0.805 \| 0.7717 \| 0.6173 \| 0.0574 \| \| 0.0364 \| 42.0 \| 4200 \| 0.0387 \| 0.805 \| 0.7225 \| 2.9162 \| 0.805 \| 0.7717 \| 0.6196 \| 0.0572 \| \| 0.0364 \| 43.0 \| 4300 \| 0.0387 \| 0.805 \| 0.7231 \| 3.0596 \| 0.805 \| 0.7717 \| 0.6139 \| 0.0560 \| \| 0.0364 \| 44.0 \| 4400 \| 0.0387 \| 0.805 \| 0.7229 \| 3.0584 \| 0.805 \| 0.7717 \| 0.6140 \| 0.0558 \| \| 0.0364 \| 45.0 \| 4500 \| 0.0386 \| 0.815 \| 0.7231 \| 2.9107 \| 0.815 \| 0.7856 \| 0.6224 \| 0.0551 \| \| 0.0364 \| 46.0 \| 4600 \| 0.0386 \| 0.8 \| 0.7228 \| 3.0609 \| 0.8000 \| 0.7683 \| 0.6154 \| 0.0570 \| \| 0.0364 \| 47.0 \| 4700 \| 0.0386 \| 0.8 \| 0.7229 \| 3.0539 \| 0.8000 \| 0.7683 \| 0.6141 \| 0.0564 \| \| 0.0364 \| 48.0 \| 4800 \| 0.0387 \| 0.805 \| 0.7228 \| 2.9149 \| 0.805 \| 0.7753 \| 0.6164 \| 0.0559 \| \| 0.0364 \| 49.0 \| 4900 \| 0.0387 \| 0.805 \| 0.7239 \| 3.0631 \| 0.805 \| 0.7729 \| 0.6144 \| 0.0569 \| \| 0.0364 \| 50.0 \| 5000 \| 0.0387 \| 0.8 \| 0.7231 \| 3.0551 \| 0.8000 \| 0.7683 \| 0.6094 \| 0.0562 \| \| 0.0364 \| 51.0 \| 5100 \| 0.0387 \| 0.815 \| 0.7232 \| 3.0662 \| 0.815 \| 0.7868 \| 0.6212 \| 0.0569 \| \| 0.0364 \| 52.0 \| 5200 \| 0.0386 \| 0.805 \| 0.7226 \| 2.9067 \| 0.805 \| 0.7753 \| 0.6241 \| 0.0550 \| \| 0.0364 \| 53.0 \| 5300 \| 0.0387 \| 0.81 \| 0.7236 \| 2.9086 \| 0.81 \| 0.7761 \| 0.6209 \| 0.0558 \| \| 0.0364 \| 54.0 \| 5400 \| 0.0387 \| 0.805 \| 0.7240 \| 2.9108 \| 0.805 \| 0.7729 \| 0.6072 \| 0.0577 \| \| 0.0364 \| 55.0 \| 5500 \| 0.0387 \| 0.815 \| 0.7228 \| 2.9235 \| 0.815 \| 0.7832 \| 0.6202 \| 0.0556 \| \| 0.0364 \| 56.0 \| 5600 \| 0.0387 \| 0.82 \| 0.7229 \| 2.9335 \| 0.82 \| 0.7898 \| 0.6273 \| 0.0544 \| \| 0.0364 \| 57.0 \| 5700 \| 0.0387 \| 0.82 \| 0.7230 \| 2.9210 \| 0.82 \| 0.7900 \| 0.6292 \| 0.0561 \| \| 0.0364 \| 58.0 \| 5800 \| 0.0387 \| 0.82 \| 0.7227 \| 2.9211 \| 0.82 \| 0.7898 \| 0.6325 \| 0.0560 \| \| 0.0364 \| 59.0 \| 5900 \| 0.0386 \| 0.82 \| 0.7238 \| 3.0664 \| 0.82 \| 0.7898 \| 0.6249 \| 0.0548 \| \| 0.0364 \| 60.0 \| 6000 \| 0.0387 \| 0.82 \| 0.7218 \| 2.9137 \| 0.82 \| 0.7900 \| 0.6397 \| 0.0545 \| \| 0.0364 \| 61.0 \| 6100 \| 0.0387 \| 0.82 \| 0.7233 \| 3.0756 \| 0.82 \| 0.7900 \| 0.6225 \| 0.0563 \| \| 0.0364 \| 62.0 \| 6200 \| 0.0387 \| 0.815 \| 0.7231 \| 3.0725 \| 0.815 \| 0.7878 \| 0.6190 \| 0.0558 \| \| 0.0364 \| 63.0 \| 6300 \| 0.0387 \| 0.82 \| 0.7218 \| 3.0589 \| 0.82 \| 0.7900 \| 0.6223 \| 0.0554 \| \| 0.0364 \| 64.0 \| 6400 \| 0.0387 \| 0.82 \| 0.7231 \| 3.0632 \| 0.82 \| 0.7900 \| 0.6289 \| 0.0551 \| \| 0.0363 \| 65.0 \| 6500 \| 0.0387 \| 0.82 \| 0.7227 \| 3.0595 \| 0.82 \| 0.7900 \| 0.6339 \| 0.0560 \| \| 0.0363 \| 66.0 \| 6600 \| 0.0387 \| 0.82 \| 0.7229 \| 2.9000 \| 0.82 \| 0.7900 \| 0.6320 \| 0.0551 \| \| 0.0363 \| 67.0 \| 6700 \| 0.0387 \| 0.82 \| 0.7222 \| 2.9092 \| 0.82 \| 0.7900 \| 0.6292 \| 0.0548 \| \| 0.0363 \| 68.0 \| 6800 \| 0.0387 \| 0.82 \| 0.7233 \| 2.7662 \| 0.82 \| 0.7900 \| 0.6299 \| 0.0564 \| \| 0.0363 \| 69.0 \| 6900 \| 0.0387 \| 0.82 \| 0.7230 \| 2.9095 \| 0.82 \| 0.7900 \| 0.6239 \| 0.0555 \| \| 0.0363 \| 70.0 \| 7000 \| 0.0387 \| 0.82 \| 0.7226 \| 2.9175 \| 0.82 \| 0.7908 \| 0.6274 \| 0.0549 \| \| 0.0363 \| 71.0 \| 7100 \| 0.0387 \| 0.82 \| 0.7237 \| 3.0548 \| 0.82 \| 0.7900 \| 0.6337 \| 0.0550 \| \| 0.0363 \| 72.0 \| 7200 \| 0.0387 \| 0.815 \| 0.7229 \| 2.9144 \| 0.815 \| 0.7841 \| 0.6207 \| 0.0570 \| \| 0.0363 \| 73.0 \| 7300 \| 0.0387 \| 0.82 \| 0.7235 \| 2.9041 \| 0.82 \| 0.7900 \| 0.6310 \| 0.0564 \| \| 0.0363 \| 74.0 \| 7400 \| 0.0387 \| 0.82 \| 0.7227 \| 2.9094 \| 0.82 \| 0.7908 \| 0.6291 \| 0.0558 \| \| 0.0363 \| 75.0 \| 7500 \| 0.0387 \| 0.825 \| 0.7236 \| 2.9105 \| 0.825 \| 0.7983 \| 0.6319 \| 0.0543 \| \| 0.0363 \| 76.0 \| 7600 \| 0.0387 \| 0.82 \| 0.7225 \| 2.9172 \| 0.82 \| 0.7908 \| 0.6260 \| 0.0550 \| \| 0.0363 \| 77.0 \| 7700 \| 0.0387 \| 0.815 \| 0.7227 \| 2.9050 \| 0.815 \| 0.7841 \| 0.6325 \| 0.0557 \| \| 0.0363 \| 78.0 \| 7800 \| 0.0387 \| 0.825 \| 0.7236 \| 2.9242 \| 0.825 \| 0.7983 \| 0.6264 \| 0.0575 \| \| 0.0363 \| 79.0 \| 7900 \| 0.0387 \| 0.82 \| 0.7231 \| 2.9167 \| 0.82 \| 0.7900 \| 0.6263 \| 0.0572 \| \| 0.0363 \| 80.0 \| 8000 \| 0.0387 \| 0.825 \| 0.7229 \| 2.7707 \| 0.825 \| 0.8004 \| 0.6311 \| 0.0569 \| \| 0.0363 \| 81.0 \| 8100 \| 0.0387 \| 0.81 \| 0.7230 \| 2.9083 \| 0.81 \| 0.7812 \| 0.6295 \| 0.0573 \| \| 0.0363 \| 82.0 \| 8200 \| 0.0387 \| 0.82 \| 0.7227 \| 2.7600 \| 0.82 \| 0.7927 \| 0.6263 \| 0.0576 \| \| 0.0363 \| 83.0 \| 8300 \| 0.0387 \| 0.815 \| 0.7226 \| 2.7902 \| 0.815 \| 0.7930 \| 0.6169 \| 0.0576 \| \| 0.0363 \| 84.0 \| 8400 \| 0.0387 \| 0.815 \| 0.7226 \| 2.7666 \| 0.815 \| 0.7841 \| 0.6254 \| 0.0571 \| \| 0.0363 \| 85.0 \| 8500 \| 0.0387 \| 0.82 \| 0.7228 \| 2.7730 \| 0.82 \| 0.7960 \| 0.6226 \| 0.0566 \| \| 0.0363 \| 86.0 \| 8600 \| 0.0387 \| 0.815 \| 0.7228 \| 2.6311 \| 0.815 \| 0.7878 \| 0.6186 \| 0.0572 \| \| 0.0363 \| 87.0 \| 8700 \| 0.0388 \| 0.82 \| 0.7230 \| 2.6272 \| 0.82 \| 0.7924 \| 0.6321 \| 0.0575 \| \| 0.0363 \| 88.0 \| 8800 \| 0.0387 \| 0.82 \| 0.7227 \| 2.7579 \| 0.82 \| 0.7924 \| 0.6318 \| 0.0568 \| \| 0.0363 \| 89.0 \| 8900 \| 0.0388 \| 0.82 \| 0.7227 \| 2.7688 \| 0.82 \| 0.7960 \| 0.6238 \| 0.0575 \| \| 0.0363 \| 90.0 \| 9000 \| 0.0387 \| 0.82 \| 0.7232 \| 2.6100 \| 0.82 \| 0.7986 \| 0.6290 \| 0.0569 \| \| 0.0363 \| 91.0 \| 9100 \| 0.0387 \| 0.82 \| 0.7228 \| 2.6088 \| 0.82 \| 0.7960 \| 0.6258 \| 0.0574 \| \| 0.0363 \| 92.0 \| 9200 \| 0.0387 \| 0.825 \| 0.7228 \| 2.6134 \| 0.825 \| 0.8038 \| 0.6239 \| 0.0575 \| \| 0.0363 \| 93.0 \| 9300 \| 0.0387 \| 0.825 \| 0.7229 \| 2.6136 \| 0.825 \| 0.7990 \| 0.6283 \| 0.0576 \| \| 0.0363 \| 94.0 \| 9400 \| 0.0387 \| 0.82 \| 0.7228 \| 2.6112 \| 0.82 \| 0.7924 \| 0.6235 \| 0.0580 \| \| 0.0363 \| 95.0 \| 9500 \| 0.0388 \| 0.82 \| 0.7228 \| 2.6077 \| 0.82 \| 0.7960 \| 0.6185 \| 0.0575 \| \| 0.0363 \| 96.0 \| 9600 \| 0.0388 \| 0.825 \| 0.7229 \| 2.6073 \| 0.825 \| 0.8038 \| 0.6206 \| 0.0572 \| \| 0.0363 \| 97.0 \| 9700 \| 0.0388 \| 0.82 \| 0.7227 \| 2.6077 \| 0.82 \| 0.7960 \| 0.6213 \| 0.0572 \| \| 0.0363 \| 98.0 \| 9800 \| 0.0388 \| 0.815 \| 0.7228 \| 2.6094 \| 0.815 \| 0.7893 \| 0.6161 \| 0.0579 \| \| 0.0363 \| 99.0 \| 9900 \| 0.0388 \| 0.815 \| 0.7228 \| 2.6086 \| 0.815 \| 0.7893 \| 0.6160 \| 0.0579 \| \| 0.0363 \| 100.0 \| 10000 \| 0.0388 \| 0.82 \| 0.7227 \| 2.6079 \| 0.82 \| 0.7960 \| 0.6182 \| 0.0575 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
daxiboy/vit-base-patch16-224-finetuned-flower	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
ayanban011/vit-base_tobacco_bs_16_lr_5e-6_e_300_wr_0.1_wd_0.2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_bs_16_lr_5e-6_e_300_wr_0.1_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8461 - Accuracy: 0.775 - Brier Loss: 0.3632 - Nll: 1.4570 - F1 Micro: 0.775 - F1 Macro: 0.7418 - Ece: 0.2043 - Aurc: 0.1066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7447 \| 0.815 \| 0.3078 \| 1.1882 \| 0.815 \| 0.7942 \| 0.2385 \| 0.0731 \| \| No log \| 2.0 \| 25 \| 0.7442 \| 0.815 \| 0.3075 \| 1.1872 \| 0.815 \| 0.7922 \| 0.2401 \| 0.0736 \| \| No log \| 2.96 \| 37 \| 0.7439 \| 0.815 \| 0.3075 \| 1.1883 \| 0.815 \| 0.7942 \| 0.2292 \| 0.0722 \| \| No log \| 4.0 \| 50 \| 0.7463 \| 0.815 \| 0.3083 \| 1.1904 \| 0.815 \| 0.7942 \| 0.2454 \| 0.0762 \| \| No log \| 4.96 \| 62 \| 0.7441 \| 0.805 \| 0.3077 \| 1.1886 \| 0.805 \| 0.7819 \| 0.2322 \| 0.0731 \| \| No log \| 6.0 \| 75 \| 0.7408 \| 0.81 \| 0.3064 \| 1.1842 \| 0.81 \| 0.7914 \| 0.2217 \| 0.0704 \| \| No log \| 6.96 \| 87 \| 0.7448 \| 0.81 \| 0.3082 \| 1.1852 \| 0.81 \| 0.7847 \| 0.2341 \| 0.0748 \| \| No log \| 8.0 \| 100 \| 0.7454 \| 0.815 \| 0.3084 \| 1.1882 \| 0.815 \| 0.7942 \| 0.2129 \| 0.0767 \| \| No log \| 8.96 \| 112 \| 0.7462 \| 0.815 \| 0.3080 \| 1.1954 \| 0.815 \| 0.7922 \| 0.2535 \| 0.0775 \| \| No log \| 10.0 \| 125 \| 0.7427 \| 0.81 \| 0.3067 \| 1.1924 \| 0.81 \| 0.7876 \| 0.2280 \| 0.0767 \| \| No log \| 10.96 \| 137 \| 0.7420 \| 0.815 \| 0.3067 \| 1.2033 \| 0.815 \| 0.7942 \| 0.2611 \| 0.0755 \| \| No log \| 12.0 \| 150 \| 0.7417 \| 0.805 \| 0.3063 \| 1.1881 \| 0.805 \| 0.7820 \| 0.2456 \| 0.0774 \| \| No log \| 12.96 \| 162 \| 0.7442 \| 0.815 \| 0.3089 \| 1.1895 \| 0.815 \| 0.8059 \| 0.2230 \| 0.0768 \| \| No log \| 14.0 \| 175 \| 0.7398 \| 0.805 \| 0.3061 \| 1.2547 \| 0.805 \| 0.7843 \| 0.2310 \| 0.0766 \| \| No log \| 14.96 \| 187 \| 0.7355 \| 0.81 \| 0.3046 \| 1.1887 \| 0.81 \| 0.7914 \| 0.2328 \| 0.0746 \| \| No log \| 16.0 \| 200 \| 0.7368 \| 0.81 \| 0.3053 \| 1.1894 \| 0.81 \| 0.7922 \| 0.2256 \| 0.0774 \| \| No log \| 16.96 \| 212 \| 0.7355 \| 0.81 \| 0.3037 \| 1.2537 \| 0.81 \| 0.7947 \| 0.2077 \| 0.0788 \| \| No log \| 18.0 \| 225 \| 0.7407 \| 0.81 \| 0.3065 \| 1.1882 \| 0.81 \| 0.7871 \| 0.2421 \| 0.0767 \| \| No log \| 18.96 \| 237 \| 0.7279 \| 0.8 \| 0.2999 \| 1.2540 \| 0.8000 \| 0.7796 \| 0.2159 \| 0.0742 \| \| No log \| 20.0 \| 250 \| 0.7324 \| 0.805 \| 0.3042 \| 1.1811 \| 0.805 \| 0.7841 \| 0.2269 \| 0.0763 \| \| No log \| 20.96 \| 262 \| 0.7421 \| 0.805 \| 0.3079 \| 1.1827 \| 0.805 \| 0.7850 \| 0.2339 \| 0.0797 \| \| No log \| 22.0 \| 275 \| 0.7343 \| 0.81 \| 0.3050 \| 1.1689 \| 0.81 \| 0.7877 \| 0.2223 \| 0.0784 \| \| No log \| 22.96 \| 287 \| 0.7308 \| 0.81 \| 0.3032 \| 1.1901 \| 0.81 \| 0.7922 \| 0.2190 \| 0.0774 \| \| No log \| 24.0 \| 300 \| 0.7381 \| 0.805 \| 0.3057 \| 1.3200 \| 0.805 \| 0.7853 \| 0.2500 \| 0.0819 \| \| No log \| 24.96 \| 312 \| 0.7336 \| 0.81 \| 0.3042 \| 1.3123 \| 0.81 \| 0.7903 \| 0.2082 \| 0.0795 \| \| No log \| 26.0 \| 325 \| 0.7282 \| 0.805 \| 0.3020 \| 1.2465 \| 0.805 \| 0.7847 \| 0.2248 \| 0.0792 \| \| No log \| 26.96 \| 337 \| 0.7346 \| 0.81 \| 0.3050 \| 1.2538 \| 0.81 \| 0.7956 \| 0.2095 \| 0.0818 \| \| No log \| 28.0 \| 350 \| 0.7305 \| 0.805 \| 0.3031 \| 1.2443 \| 0.805 \| 0.7850 \| 0.2488 \| 0.0823 \| \| No log \| 28.96 \| 362 \| 0.7395 \| 0.8 \| 0.3071 \| 1.3235 \| 0.8000 \| 0.7818 \| 0.2223 \| 0.0843 \| \| No log \| 30.0 \| 375 \| 0.7349 \| 0.8 \| 0.3058 \| 1.2511 \| 0.8000 \| 0.7733 \| 0.2004 \| 0.0817 \| \| No log \| 30.96 \| 387 \| 0.7344 \| 0.8 \| 0.3048 \| 1.2516 \| 0.8000 \| 0.7818 \| 0.2183 \| 0.0837 \| \| No log \| 32.0 \| 400 \| 0.7332 \| 0.795 \| 0.3037 \| 1.3836 \| 0.795 \| 0.7686 \| 0.2185 \| 0.0844 \| \| No log \| 32.96 \| 412 \| 0.7306 \| 0.81 \| 0.3042 \| 1.1767 \| 0.81 \| 0.7905 \| 0.2117 \| 0.0837 \| \| No log \| 34.0 \| 425 \| 0.7326 \| 0.8 \| 0.3040 \| 1.2058 \| 0.8000 \| 0.7783 \| 0.2106 \| 0.0857 \| \| No log \| 34.96 \| 437 \| 0.7317 \| 0.8 \| 0.3045 \| 1.3068 \| 0.8000 \| 0.7733 \| 0.2337 \| 0.0843 \| \| No log \| 36.0 \| 450 \| 0.7345 \| 0.805 \| 0.3073 \| 1.3065 \| 0.805 \| 0.7782 \| 0.1928 \| 0.0823 \| \| No log \| 36.96 \| 462 \| 0.7367 \| 0.8 \| 0.3074 \| 1.3259 \| 0.8000 \| 0.7733 \| 0.1941 \| 0.0860 \| \| No log \| 38.0 \| 475 \| 0.7349 \| 0.8 \| 0.3073 \| 1.3074 \| 0.8000 \| 0.7731 \| 0.2138 \| 0.0853 \| \| No log \| 38.96 \| 487 \| 0.7331 \| 0.81 \| 0.3057 \| 1.3149 \| 0.81 \| 0.7909 \| 0.1981 \| 0.0865 \| \| 0.1577 \| 40.0 \| 500 \| 0.7269 \| 0.8 \| 0.3018 \| 1.3700 \| 0.8000 \| 0.7746 \| 0.2033 \| 0.0865 \| \| 0.1577 \| 40.96 \| 512 \| 0.7270 \| 0.8 \| 0.3020 \| 1.3687 \| 0.8000 \| 0.7737 \| 0.2108 \| 0.0860 \| \| 0.1577 \| 42.0 \| 525 \| 0.7356 \| 0.805 \| 0.3078 \| 1.3105 \| 0.805 \| 0.7784 \| 0.2053 \| 0.0892 \| \| 0.1577 \| 42.96 \| 537 \| 0.7291 \| 0.8 \| 0.3031 \| 1.3687 \| 0.8000 \| 0.7746 \| 0.2066 \| 0.0876 \| \| 0.1577 \| 44.0 \| 550 \| 0.7276 \| 0.81 \| 0.3034 \| 1.3655 \| 0.81 \| 0.7844 \| 0.2189 \| 0.0872 \| \| 0.1577 \| 44.96 \| 562 \| 0.7318 \| 0.805 \| 0.3050 \| 1.3684 \| 0.805 \| 0.7793 \| 0.2209 \| 0.0893 \| \| 0.1577 \| 46.0 \| 575 \| 0.7300 \| 0.805 \| 0.3041 \| 1.3679 \| 0.805 \| 0.7793 \| 0.2040 \| 0.0885 \| \| 0.1577 \| 46.96 \| 587 \| 0.7342 \| 0.805 \| 0.3060 \| 1.3679 \| 0.805 \| 0.7797 \| 0.2059 \| 0.0893 \| \| 0.1577 \| 48.0 \| 600 \| 0.7303 \| 0.805 \| 0.3045 \| 1.3672 \| 0.805 \| 0.7797 \| 0.1862 \| 0.0889 \| \| 0.1577 \| 48.96 \| 612 \| 0.7401 \| 0.8 \| 0.3090 \| 1.3710 \| 0.8000 \| 0.7746 \| 0.1930 \| 0.0915 \| \| 0.1577 \| 50.0 \| 625 \| 0.7329 \| 0.795 \| 0.3054 \| 1.3696 \| 0.795 \| 0.7654 \| 0.1984 \| 0.0891 \| \| 0.1577 \| 50.96 \| 637 \| 0.7363 \| 0.795 \| 0.3072 \| 1.3689 \| 0.795 \| 0.7654 \| 0.2196 \| 0.0907 \| \| 0.1577 \| 52.0 \| 650 \| 0.7402 \| 0.805 \| 0.3101 \| 1.3646 \| 0.805 \| 0.7784 \| 0.2028 \| 0.0911 \| \| 0.1577 \| 52.96 \| 662 \| 0.7347 \| 0.8 \| 0.3065 \| 1.3687 \| 0.8000 \| 0.7746 \| 0.2062 \| 0.0894 \| \| 0.1577 \| 54.0 \| 675 \| 0.7388 \| 0.805 \| 0.3097 \| 1.3649 \| 0.805 \| 0.7784 \| 0.2027 \| 0.0907 \| \| 0.1577 \| 54.96 \| 687 \| 0.7381 \| 0.8 \| 0.3087 \| 1.3681 \| 0.8000 \| 0.7704 \| 0.2120 \| 0.0908 \| \| 0.1577 \| 56.0 \| 700 \| 0.7372 \| 0.805 \| 0.3088 \| 1.3646 \| 0.805 \| 0.7749 \| 0.1866 \| 0.0903 \| \| 0.1577 \| 56.96 \| 712 \| 0.7403 \| 0.805 \| 0.3102 \| 1.3682 \| 0.805 \| 0.7749 \| 0.2287 \| 0.0922 \| \| 0.1577 \| 58.0 \| 725 \| 0.7352 \| 0.8 \| 0.3069 \| 1.3680 \| 0.8000 \| 0.7704 \| 0.2117 \| 0.0900 \| \| 0.1577 \| 58.96 \| 737 \| 0.7373 \| 0.8 \| 0.3079 \| 1.3699 \| 0.8000 \| 0.7704 \| 0.1990 \| 0.0923 \| \| 0.1577 \| 60.0 \| 750 \| 0.7353 \| 0.795 \| 0.3065 \| 1.3690 \| 0.795 \| 0.7656 \| 0.2078 \| 0.0900 \| \| 0.1577 \| 60.96 \| 762 \| 0.7357 \| 0.805 \| 0.3071 \| 1.3657 \| 0.805 \| 0.7732 \| 0.2076 \| 0.0899 \| \| 0.1577 \| 62.0 \| 775 \| 0.7409 \| 0.79 \| 0.3103 \| 1.3737 \| 0.79 \| 0.7623 \| 0.2066 \| 0.0920 \| \| 0.1577 \| 62.96 \| 787 \| 0.7393 \| 0.795 \| 0.3082 \| 1.4518 \| 0.795 \| 0.7670 \| 0.2047 \| 0.0912 \| \| 0.1577 \| 64.0 \| 800 \| 0.7417 \| 0.8 \| 0.3093 \| 1.3304 \| 0.8000 \| 0.7684 \| 0.1955 \| 0.0917 \| \| 0.1577 \| 64.96 \| 812 \| 0.7438 \| 0.8 \| 0.3121 \| 1.3714 \| 0.8000 \| 0.7707 \| 0.1782 \| 0.0920 \| \| 0.1577 \| 66.0 \| 825 \| 0.7408 \| 0.8 \| 0.3100 \| 1.3758 \| 0.8000 \| 0.7709 \| 0.1965 \| 0.0931 \| \| 0.1577 \| 66.96 \| 837 \| 0.7434 \| 0.8 \| 0.3112 \| 1.3767 \| 0.8000 \| 0.7707 \| 0.2124 \| 0.0935 \| \| 0.1577 \| 68.0 \| 850 \| 0.7393 \| 0.8 \| 0.3107 \| 1.3038 \| 0.8000 \| 0.7704 \| 0.1786 \| 0.0901 \| \| 0.1577 \| 68.96 \| 862 \| 0.7383 \| 0.8 \| 0.3090 \| 1.3689 \| 0.8000 \| 0.7704 \| 0.2041 \| 0.0913 \| \| 0.1577 \| 70.0 \| 875 \| 0.7436 \| 0.8 \| 0.3119 \| 1.3658 \| 0.8000 \| 0.7704 \| 0.1983 \| 0.0932 \| \| 0.1577 \| 70.96 \| 887 \| 0.7463 \| 0.8 \| 0.3130 \| 1.3700 \| 0.8000 \| 0.7707 \| 0.1932 \| 0.0947 \| \| 0.1577 \| 72.0 \| 900 \| 0.7464 \| 0.795 \| 0.3135 \| 1.3720 \| 0.795 \| 0.7656 \| 0.2089 \| 0.0932 \| \| 0.1577 \| 72.96 \| 912 \| 0.7469 \| 0.8 \| 0.3137 \| 1.3703 \| 0.8000 \| 0.7707 \| 0.2004 \| 0.0943 \| \| 0.1577 \| 74.0 \| 925 \| 0.7435 \| 0.8 \| 0.3124 \| 1.3674 \| 0.8000 \| 0.7704 \| 0.1958 \| 0.0930 \| \| 0.1577 \| 74.96 \| 937 \| 0.7427 \| 0.8 \| 0.3117 \| 1.3708 \| 0.8000 \| 0.7707 \| 0.2224 \| 0.0921 \| \| 0.1577 \| 76.0 \| 950 \| 0.7420 \| 0.8 \| 0.3111 \| 1.3664 \| 0.8000 \| 0.7704 \| 0.2145 \| 0.0928 \| \| 0.1577 \| 76.96 \| 962 \| 0.7457 \| 0.8 \| 0.3135 \| 1.3690 \| 0.8000 \| 0.7707 \| 0.2178 \| 0.0934 \| \| 0.1577 \| 78.0 \| 975 \| 0.7513 \| 0.8 \| 0.3163 \| 1.3707 \| 0.8000 \| 0.7707 \| 0.1964 \| 0.0947 \| \| 0.1577 \| 78.96 \| 987 \| 0.7466 \| 0.8 \| 0.3139 \| 1.3722 \| 0.8000 \| 0.7704 \| 0.2001 \| 0.0936 \| \| 0.1081 \| 80.0 \| 1000 \| 0.7491 \| 0.8 \| 0.3154 \| 1.3712 \| 0.8000 \| 0.7707 \| 0.2100 \| 0.0943 \| \| 0.1081 \| 80.96 \| 1012 \| 0.7483 \| 0.8 \| 0.3150 \| 1.3675 \| 0.8000 \| 0.7704 \| 0.2083 \| 0.0939 \| \| 0.1081 \| 82.0 \| 1025 \| 0.7523 \| 0.8 \| 0.3163 \| 1.3742 \| 0.8000 \| 0.7707 \| 0.2095 \| 0.0958 \| \| 0.1081 \| 82.96 \| 1037 \| 0.7511 \| 0.8 \| 0.3166 \| 1.3703 \| 0.8000 \| 0.7707 \| 0.2034 \| 0.0944 \| \| 0.1081 \| 84.0 \| 1050 \| 0.7481 \| 0.8 \| 0.3150 \| 1.3687 \| 0.8000 \| 0.7704 \| 0.2113 \| 0.0941 \| \| 0.1081 \| 84.96 \| 1062 \| 0.7501 \| 0.8 \| 0.3164 \| 1.3668 \| 0.8000 \| 0.7693 \| 0.2053 \| 0.0932 \| \| 0.1081 \| 86.0 \| 1075 \| 0.7539 \| 0.8 \| 0.3177 \| 1.3725 \| 0.8000 \| 0.7707 \| 0.2025 \| 0.0951 \| \| 0.1081 \| 86.96 \| 1087 \| 0.7550 \| 0.8 \| 0.3182 \| 1.3731 \| 0.8000 \| 0.7707 \| 0.1969 \| 0.0953 \| \| 0.1081 \| 88.0 \| 1100 \| 0.7553 \| 0.8 \| 0.3183 \| 1.3697 \| 0.8000 \| 0.7707 \| 0.1972 \| 0.0952 \| \| 0.1081 \| 88.96 \| 1112 \| 0.7535 \| 0.8 \| 0.3176 \| 1.3719 \| 0.8000 \| 0.7707 \| 0.2073 \| 0.0945 \| \| 0.1081 \| 90.0 \| 1125 \| 0.7558 \| 0.795 \| 0.3186 \| 1.3742 \| 0.795 \| 0.7681 \| 0.2018 \| 0.0959 \| \| 0.1081 \| 90.96 \| 1137 \| 0.7573 \| 0.8 \| 0.3193 \| 1.3739 \| 0.8000 \| 0.7704 \| 0.1919 \| 0.0965 \| \| 0.1081 \| 92.0 \| 1150 \| 0.7565 \| 0.8 \| 0.3193 \| 1.3743 \| 0.8000 \| 0.7698 \| 0.1967 \| 0.0959 \| \| 0.1081 \| 92.96 \| 1162 \| 0.7619 \| 0.795 \| 0.3218 \| 1.3758 \| 0.795 \| 0.7681 \| 0.1989 \| 0.0974 \| \| 0.1081 \| 94.0 \| 1175 \| 0.7577 \| 0.8 \| 0.3198 \| 1.3793 \| 0.8000 \| 0.7696 \| 0.1996 \| 0.0957 \| \| 0.1081 \| 94.96 \| 1187 \| 0.7575 \| 0.795 \| 0.3201 \| 1.3781 \| 0.795 \| 0.7666 \| 0.1954 \| 0.0964 \| \| 0.1081 \| 96.0 \| 1200 \| 0.7573 \| 0.8 \| 0.3199 \| 1.3752 \| 0.8000 \| 0.7693 \| 0.1863 \| 0.0955 \| \| 0.1081 \| 96.96 \| 1212 \| 0.7615 \| 0.795 \| 0.3216 \| 1.3753 \| 0.795 \| 0.7681 \| 0.1997 \| 0.0975 \| \| 0.1081 \| 98.0 \| 1225 \| 0.7603 \| 0.795 \| 0.3215 \| 1.3731 \| 0.795 \| 0.7681 \| 0.2051 \| 0.0963 \| \| 0.1081 \| 98.96 \| 1237 \| 0.7596 \| 0.795 \| 0.3209 \| 1.3744 \| 0.795 \| 0.7673 \| 0.2081 \| 0.0959 \| \| 0.1081 \| 100.0 \| 1250 \| 0.7582 \| 0.795 \| 0.3203 \| 1.3743 \| 0.795 \| 0.7673 \| 0.2024 \| 0.0955 \| \| 0.1081 \| 100.96 \| 1262 \| 0.7609 \| 0.795 \| 0.3223 \| 1.3761 \| 0.795 \| 0.7681 \| 0.1823 \| 0.0968 \| \| 0.1081 \| 102.0 \| 1275 \| 0.7632 \| 0.785 \| 0.3233 \| 1.3758 \| 0.785 \| 0.7528 \| 0.1833 \| 0.0970 \| \| 0.1081 \| 102.96 \| 1287 \| 0.7618 \| 0.785 \| 0.3219 \| 1.3785 \| 0.785 \| 0.7516 \| 0.2141 \| 0.0970 \| \| 0.1081 \| 104.0 \| 1300 \| 0.7633 \| 0.795 \| 0.3230 \| 1.4970 \| 0.795 \| 0.7664 \| 0.1956 \| 0.0952 \| \| 0.1081 \| 104.96 \| 1312 \| 0.7657 \| 0.79 \| 0.3243 \| 1.4406 \| 0.79 \| 0.7639 \| 0.1960 \| 0.0961 \| \| 0.1081 \| 106.0 \| 1325 \| 0.7673 \| 0.785 \| 0.3251 \| 1.4424 \| 0.785 \| 0.7516 \| 0.2083 \| 0.0978 \| \| 0.1081 \| 106.96 \| 1337 \| 0.7667 \| 0.79 \| 0.3250 \| 1.4392 \| 0.79 \| 0.7639 \| 0.1875 \| 0.0976 \| \| 0.1081 \| 108.0 \| 1350 \| 0.7690 \| 0.785 \| 0.3250 \| 1.3876 \| 0.785 \| 0.7526 \| 0.2078 \| 0.0990 \| \| 0.1081 \| 108.96 \| 1362 \| 0.7676 \| 0.785 \| 0.3252 \| 1.3872 \| 0.785 \| 0.7554 \| 0.2073 \| 0.0985 \| \| 0.1081 \| 110.0 \| 1375 \| 0.7662 \| 0.79 \| 0.3249 \| 1.4335 \| 0.79 \| 0.7639 \| 0.1939 \| 0.0980 \| \| 0.1081 \| 110.96 \| 1387 \| 0.7723 \| 0.785 \| 0.3273 \| 1.4567 \| 0.785 \| 0.7554 \| 0.2066 \| 0.0995 \| \| 0.1081 \| 112.0 \| 1400 \| 0.7665 \| 0.78 \| 0.3250 \| 1.3960 \| 0.78 \| 0.7488 \| 0.2066 \| 0.0976 \| \| 0.1081 \| 112.96 \| 1412 \| 0.7722 \| 0.785 \| 0.3275 \| 1.4410 \| 0.785 \| 0.7573 \| 0.2063 \| 0.0991 \| \| 0.1081 \| 114.0 \| 1425 \| 0.7722 \| 0.79 \| 0.3271 \| 1.4039 \| 0.79 \| 0.7639 \| 0.1902 \| 0.0990 \| \| 0.1081 \| 114.96 \| 1437 \| 0.7699 \| 0.79 \| 0.3264 \| 1.3849 \| 0.79 \| 0.7644 \| 0.1914 \| 0.0982 \| \| 0.1081 \| 116.0 \| 1450 \| 0.7749 \| 0.785 \| 0.3285 \| 1.3854 \| 0.785 \| 0.7573 \| 0.1942 \| 0.0999 \| \| 0.1081 \| 116.96 \| 1462 \| 0.7722 \| 0.78 \| 0.3279 \| 1.4365 \| 0.78 \| 0.7488 \| 0.1973 \| 0.0991 \| \| 0.1081 \| 118.0 \| 1475 \| 0.7763 \| 0.78 \| 0.3293 \| 1.3823 \| 0.78 \| 0.7488 \| 0.2050 \| 0.1006 \| \| 0.1081 \| 118.96 \| 1487 \| 0.7740 \| 0.78 \| 0.3287 \| 1.3822 \| 0.78 \| 0.7488 \| 0.2105 \| 0.0991 \| \| 0.0821 \| 120.0 \| 1500 \| 0.7761 \| 0.785 \| 0.3294 \| 1.4414 \| 0.785 \| 0.7573 \| 0.1996 \| 0.0995 \| \| 0.0821 \| 120.96 \| 1512 \| 0.7749 \| 0.78 \| 0.3289 \| 1.4387 \| 0.78 \| 0.7488 \| 0.1981 \| 0.0991 \| \| 0.0821 \| 122.0 \| 1525 \| 0.7763 \| 0.78 \| 0.3297 \| 1.4395 \| 0.78 \| 0.7488 \| 0.2175 \| 0.0993 \| \| 0.0821 \| 122.96 \| 1537 \| 0.7775 \| 0.78 \| 0.3305 \| 1.4407 \| 0.78 \| 0.7488 \| 0.2073 \| 0.0993 \| \| 0.0821 \| 124.0 \| 1550 \| 0.7770 \| 0.78 \| 0.3299 \| 1.4411 \| 0.78 \| 0.7488 \| 0.2096 \| 0.0996 \| \| 0.0821 \| 124.96 \| 1562 \| 0.7785 \| 0.78 \| 0.3309 \| 1.4415 \| 0.78 \| 0.7488 \| 0.2174 \| 0.1004 \| \| 0.0821 \| 126.0 \| 1575 \| 0.7808 \| 0.78 \| 0.3321 \| 1.4431 \| 0.78 \| 0.7488 \| 0.2082 \| 0.1005 \| \| 0.0821 \| 126.96 \| 1587 \| 0.7791 \| 0.78 \| 0.3312 \| 1.4405 \| 0.78 \| 0.7488 \| 0.2087 \| 0.0998 \| \| 0.0821 \| 128.0 \| 1600 \| 0.7789 \| 0.78 \| 0.3312 \| 1.4386 \| 0.78 \| 0.7488 \| 0.2047 \| 0.0995 \| \| 0.0821 \| 128.96 \| 1612 \| 0.7829 \| 0.78 \| 0.3330 \| 1.4423 \| 0.78 \| 0.7488 \| 0.1920 \| 0.1005 \| \| 0.0821 \| 130.0 \| 1625 \| 0.7797 \| 0.78 \| 0.3317 \| 1.4400 \| 0.78 \| 0.7488 \| 0.2013 \| 0.1006 \| \| 0.0821 \| 130.96 \| 1637 \| 0.7849 \| 0.78 \| 0.3336 \| 1.4446 \| 0.78 \| 0.7491 \| 0.2064 \| 0.1006 \| \| 0.0821 \| 132.0 \| 1650 \| 0.7817 \| 0.78 \| 0.3322 \| 1.4396 \| 0.78 \| 0.7488 \| 0.2060 \| 0.1003 \| \| 0.0821 \| 132.96 \| 1662 \| 0.7823 \| 0.78 \| 0.3329 \| 1.4407 \| 0.78 \| 0.7488 \| 0.1990 \| 0.0999 \| \| 0.0821 \| 134.0 \| 1675 \| 0.7869 \| 0.78 \| 0.3354 \| 1.4482 \| 0.78 \| 0.7488 \| 0.1999 \| 0.1009 \| \| 0.0821 \| 134.96 \| 1687 \| 0.7859 \| 0.78 \| 0.3349 \| 1.4429 \| 0.78 \| 0.7488 \| 0.1934 \| 0.1013 \| \| 0.0821 \| 136.0 \| 1700 \| 0.7867 \| 0.78 \| 0.3352 \| 1.4437 \| 0.78 \| 0.7488 \| 0.2114 \| 0.1006 \| \| 0.0821 \| 136.96 \| 1712 \| 0.7867 \| 0.78 \| 0.3350 \| 1.4403 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1011 \| \| 0.0821 \| 138.0 \| 1725 \| 0.7851 \| 0.78 \| 0.3341 \| 1.4439 \| 0.78 \| 0.7488 \| 0.1906 \| 0.1009 \| \| 0.0821 \| 138.96 \| 1737 \| 0.7892 \| 0.78 \| 0.3360 \| 1.4495 \| 0.78 \| 0.7488 \| 0.2009 \| 0.1020 \| \| 0.0821 \| 140.0 \| 1750 \| 0.7893 \| 0.78 \| 0.3366 \| 1.4434 \| 0.78 \| 0.7488 \| 0.1976 \| 0.1013 \| \| 0.0821 \| 140.96 \| 1762 \| 0.7848 \| 0.78 \| 0.3344 \| 1.4383 \| 0.78 \| 0.7488 \| 0.1995 \| 0.1001 \| \| 0.0821 \| 142.0 \| 1775 \| 0.7911 \| 0.78 \| 0.3372 \| 1.4487 \| 0.78 \| 0.7488 \| 0.1995 \| 0.1020 \| \| 0.0821 \| 142.96 \| 1787 \| 0.7890 \| 0.78 \| 0.3362 \| 1.4416 \| 0.78 \| 0.7488 \| 0.2075 \| 0.1010 \| \| 0.0821 \| 144.0 \| 1800 \| 0.7915 \| 0.78 \| 0.3372 \| 1.4476 \| 0.78 \| 0.7488 \| 0.1842 \| 0.1019 \| \| 0.0821 \| 144.96 \| 1812 \| 0.7876 \| 0.78 \| 0.3351 \| 1.4999 \| 0.78 \| 0.7488 \| 0.1904 \| 0.0995 \| \| 0.0821 \| 146.0 \| 1825 \| 0.7933 \| 0.78 \| 0.3378 \| 1.4469 \| 0.78 \| 0.7488 \| 0.1973 \| 0.1023 \| \| 0.0821 \| 146.96 \| 1837 \| 0.7932 \| 0.78 \| 0.3383 \| 1.4441 \| 0.78 \| 0.7488 \| 0.2070 \| 0.1016 \| \| 0.0821 \| 148.0 \| 1850 \| 0.7907 \| 0.78 \| 0.3369 \| 1.4439 \| 0.78 \| 0.7488 \| 0.1932 \| 0.1014 \| \| 0.0821 \| 148.96 \| 1862 \| 0.7939 \| 0.78 \| 0.3386 \| 1.4462 \| 0.78 \| 0.7488 \| 0.1906 \| 0.1015 \| \| 0.0821 \| 150.0 \| 1875 \| 0.7943 \| 0.78 \| 0.3386 \| 1.4449 \| 0.78 \| 0.7488 \| 0.1965 \| 0.1016 \| \| 0.0821 \| 150.96 \| 1887 \| 0.7955 \| 0.78 \| 0.3393 \| 1.5025 \| 0.78 \| 0.7488 \| 0.2112 \| 0.1015 \| \| 0.0821 \| 152.0 \| 1900 \| 0.7936 \| 0.78 \| 0.3386 \| 1.4407 \| 0.78 \| 0.7488 \| 0.2112 \| 0.1012 \| \| 0.0821 \| 152.96 \| 1912 \| 0.7966 \| 0.78 \| 0.3400 \| 1.5033 \| 0.78 \| 0.7488 \| 0.1963 \| 0.1012 \| \| 0.0821 \| 154.0 \| 1925 \| 0.7981 \| 0.78 \| 0.3405 \| 1.4495 \| 0.78 \| 0.7488 \| 0.1895 \| 0.1020 \| \| 0.0821 \| 154.96 \| 1937 \| 0.7972 \| 0.78 \| 0.3401 \| 1.4417 \| 0.78 \| 0.7488 \| 0.1953 \| 0.1018 \| \| 0.0821 \| 156.0 \| 1950 \| 0.7922 \| 0.78 \| 0.3381 \| 1.4395 \| 0.78 \| 0.7488 \| 0.2056 \| 0.0999 \| \| 0.0821 \| 156.96 \| 1962 \| 0.8013 \| 0.775 \| 0.3425 \| 1.4473 \| 0.775 \| 0.7451 \| 0.1869 \| 0.1028 \| \| 0.0821 \| 158.0 \| 1975 \| 0.7977 \| 0.78 \| 0.3403 \| 1.4446 \| 0.78 \| 0.7488 \| 0.1872 \| 0.1014 \| \| 0.0821 \| 158.96 \| 1987 \| 0.7990 \| 0.78 \| 0.3412 \| 1.4413 \| 0.78 \| 0.7488 \| 0.1939 \| 0.1017 \| \| 0.0668 \| 160.0 \| 2000 \| 0.8048 \| 0.775 \| 0.3435 \| 1.4532 \| 0.775 \| 0.7451 \| 0.1966 \| 0.1049 \| \| 0.0668 \| 160.96 \| 2012 \| 0.8064 \| 0.77 \| 0.3448 \| 1.4529 \| 0.7700 \| 0.7358 \| 0.1953 \| 0.1044 \| \| 0.0668 \| 162.0 \| 2025 \| 0.7989 \| 0.78 \| 0.3412 \| 1.4423 \| 0.78 \| 0.7488 \| 0.2038 \| 0.1022 \| \| 0.0668 \| 162.96 \| 2037 \| 0.8001 \| 0.78 \| 0.3414 \| 1.4440 \| 0.78 \| 0.7488 \| 0.1972 \| 0.1015 \| \| 0.0668 \| 164.0 \| 2050 \| 0.8068 \| 0.775 \| 0.3448 \| 1.4523 \| 0.775 \| 0.7396 \| 0.2031 \| 0.1036 \| \| 0.0668 \| 164.96 \| 2062 \| 0.8046 \| 0.785 \| 0.3438 \| 1.4475 \| 0.785 \| 0.7536 \| 0.2070 \| 0.1037 \| \| 0.0668 \| 166.0 \| 2075 \| 0.8016 \| 0.78 \| 0.3426 \| 1.4451 \| 0.78 \| 0.7488 \| 0.1975 \| 0.1012 \| \| 0.0668 \| 166.96 \| 2087 \| 0.8053 \| 0.78 \| 0.3442 \| 1.4485 \| 0.78 \| 0.7477 \| 0.2112 \| 0.1022 \| \| 0.0668 \| 168.0 \| 2100 \| 0.8040 \| 0.78 \| 0.3433 \| 1.4459 \| 0.78 \| 0.7422 \| 0.2014 \| 0.1031 \| \| 0.0668 \| 168.96 \| 2112 \| 0.8048 \| 0.785 \| 0.3437 \| 1.4479 \| 0.785 \| 0.7515 \| 0.2046 \| 0.1033 \| \| 0.0668 \| 170.0 \| 2125 \| 0.8054 \| 0.775 \| 0.3447 \| 1.5060 \| 0.775 \| 0.7450 \| 0.1896 \| 0.1017 \| \| 0.0668 \| 170.96 \| 2137 \| 0.8067 \| 0.775 \| 0.3451 \| 1.5079 \| 0.775 \| 0.7450 \| 0.1898 \| 0.1018 \| \| 0.0668 \| 172.0 \| 2150 \| 0.8060 \| 0.78 \| 0.3447 \| 1.4508 \| 0.78 \| 0.7488 \| 0.1842 \| 0.1022 \| \| 0.0668 \| 172.96 \| 2162 \| 0.8127 \| 0.77 \| 0.3484 \| 1.4513 \| 0.7700 \| 0.7358 \| 0.2006 \| 0.1042 \| \| 0.0668 \| 174.0 \| 2175 \| 0.8080 \| 0.77 \| 0.3457 \| 1.4453 \| 0.7700 \| 0.7349 \| 0.2198 \| 0.1034 \| \| 0.0668 \| 174.96 \| 2187 \| 0.8095 \| 0.775 \| 0.3460 \| 1.4471 \| 0.775 \| 0.7384 \| 0.2029 \| 0.1027 \| \| 0.0668 \| 176.0 \| 2200 \| 0.8112 \| 0.775 \| 0.3467 \| 1.4559 \| 0.775 \| 0.7395 \| 0.1995 \| 0.1036 \| \| 0.0668 \| 176.96 \| 2212 \| 0.8089 \| 0.77 \| 0.3460 \| 1.4485 \| 0.7700 \| 0.7357 \| 0.2050 \| 0.1019 \| \| 0.0668 \| 178.0 \| 2225 \| 0.8093 \| 0.77 \| 0.3461 \| 1.4459 \| 0.7700 \| 0.7357 \| 0.1989 \| 0.1021 \| \| 0.0668 \| 178.96 \| 2237 \| 0.8118 \| 0.775 \| 0.3473 \| 1.4499 \| 0.775 \| 0.7384 \| 0.2085 \| 0.1029 \| \| 0.0668 \| 180.0 \| 2250 \| 0.8112 \| 0.775 \| 0.3472 \| 1.4471 \| 0.775 \| 0.7384 \| 0.2070 \| 0.1027 \| \| 0.0668 \| 180.96 \| 2262 \| 0.8124 \| 0.77 \| 0.3478 \| 1.4484 \| 0.7700 \| 0.7357 \| 0.1983 \| 0.1029 \| \| 0.0668 \| 182.0 \| 2275 \| 0.8140 \| 0.77 \| 0.3484 \| 1.4489 \| 0.7700 \| 0.7357 \| 0.1987 \| 0.1038 \| \| 0.0668 \| 182.96 \| 2287 \| 0.8137 \| 0.77 \| 0.3483 \| 1.4491 \| 0.7700 \| 0.7357 \| 0.2036 \| 0.1030 \| \| 0.0668 \| 184.0 \| 2300 \| 0.8133 \| 0.77 \| 0.3481 \| 1.4468 \| 0.7700 \| 0.7357 \| 0.2012 \| 0.1024 \| \| 0.0668 \| 184.96 \| 2312 \| 0.8152 \| 0.77 \| 0.3489 \| 1.4525 \| 0.7700 \| 0.7357 \| 0.1996 \| 0.1029 \| \| 0.0668 \| 186.0 \| 2325 \| 0.8149 \| 0.77 \| 0.3490 \| 1.4511 \| 0.7700 \| 0.7357 \| 0.1917 \| 0.1027 \| \| 0.0668 \| 186.96 \| 2337 \| 0.8151 \| 0.77 \| 0.3490 \| 1.4489 \| 0.7700 \| 0.7357 \| 0.1956 \| 0.1028 \| \| 0.0668 \| 188.0 \| 2350 \| 0.8175 \| 0.77 \| 0.3500 \| 1.5084 \| 0.7700 \| 0.7357 \| 0.2011 \| 0.1038 \| \| 0.0668 \| 188.96 \| 2362 \| 0.8181 \| 0.765 \| 0.3499 \| 1.4506 \| 0.765 \| 0.7323 \| 0.1975 \| 0.1056 \| \| 0.0668 \| 190.0 \| 2375 \| 0.8180 \| 0.765 \| 0.3504 \| 1.4499 \| 0.765 \| 0.7323 \| 0.2162 \| 0.1050 \| \| 0.0668 \| 190.96 \| 2387 \| 0.8168 \| 0.77 \| 0.3498 \| 1.4510 \| 0.7700 \| 0.7357 \| 0.2014 \| 0.1039 \| \| 0.0668 \| 192.0 \| 2400 \| 0.8183 \| 0.77 \| 0.3505 \| 1.4483 \| 0.7700 \| 0.7379 \| 0.2114 \| 0.1032 \| \| 0.0668 \| 192.96 \| 2412 \| 0.8193 \| 0.775 \| 0.3507 \| 1.4508 \| 0.775 \| 0.7384 \| 0.2025 \| 0.1042 \| \| 0.0668 \| 194.0 \| 2425 \| 0.8181 \| 0.77 \| 0.3503 \| 1.4565 \| 0.7700 \| 0.7357 \| 0.2090 \| 0.1027 \| \| 0.0668 \| 194.96 \| 2437 \| 0.8192 \| 0.77 \| 0.3507 \| 1.4513 \| 0.7700 \| 0.7357 \| 0.1953 \| 0.1032 \| \| 0.0668 \| 196.0 \| 2450 \| 0.8214 \| 0.77 \| 0.3520 \| 1.4519 \| 0.7700 \| 0.7349 \| 0.2112 \| 0.1045 \| \| 0.0668 \| 196.96 \| 2462 \| 0.8231 \| 0.765 \| 0.3531 \| 1.4517 \| 0.765 \| 0.7323 \| 0.2042 \| 0.1049 \| \| 0.0668 \| 198.0 \| 2475 \| 0.8219 \| 0.77 \| 0.3521 \| 1.4512 \| 0.7700 \| 0.7349 \| 0.2152 \| 0.1044 \| \| 0.0668 \| 198.96 \| 2487 \| 0.8223 \| 0.77 \| 0.3523 \| 1.4507 \| 0.7700 \| 0.7349 \| 0.1888 \| 0.1050 \| \| 0.0571 \| 200.0 \| 2500 \| 0.8235 \| 0.77 \| 0.3529 \| 1.4533 \| 0.7700 \| 0.7349 \| 0.2029 \| 0.1050 \| \| 0.0571 \| 200.96 \| 2512 \| 0.8227 \| 0.77 \| 0.3525 \| 1.4718 \| 0.7700 \| 0.7357 \| 0.2170 \| 0.1033 \| \| 0.0571 \| 202.0 \| 2525 \| 0.8226 \| 0.77 \| 0.3525 \| 1.4505 \| 0.7700 \| 0.7349 \| 0.1954 \| 0.1041 \| \| 0.0571 \| 202.96 \| 2537 \| 0.8231 \| 0.765 \| 0.3530 \| 1.4506 \| 0.765 \| 0.7321 \| 0.1962 \| 0.1046 \| \| 0.0571 \| 204.0 \| 2550 \| 0.8255 \| 0.77 \| 0.3535 \| 1.4520 \| 0.7700 \| 0.7380 \| 0.2078 \| 0.1060 \| \| 0.0571 \| 204.96 \| 2562 \| 0.8276 \| 0.77 \| 0.3550 \| 1.4594 \| 0.7700 \| 0.7349 \| 0.2013 \| 0.1046 \| \| 0.0571 \| 206.0 \| 2575 \| 0.8257 \| 0.77 \| 0.3542 \| 1.4532 \| 0.7700 \| 0.7349 \| 0.1987 \| 0.1040 \| \| 0.0571 \| 206.96 \| 2587 \| 0.8248 \| 0.775 \| 0.3536 \| 1.4499 \| 0.775 \| 0.7406 \| 0.1903 \| 0.1043 \| \| 0.0571 \| 208.0 \| 2600 \| 0.8250 \| 0.77 \| 0.3534 \| 1.4537 \| 0.7700 \| 0.7349 \| 0.2070 \| 0.1040 \| \| 0.0571 \| 208.96 \| 2612 \| 0.8277 \| 0.77 \| 0.3548 \| 1.4521 \| 0.7700 \| 0.7380 \| 0.1867 \| 0.1058 \| \| 0.0571 \| 210.0 \| 2625 \| 0.8271 \| 0.77 \| 0.3545 \| 1.4543 \| 0.7700 \| 0.7349 \| 0.2213 \| 0.1036 \| \| 0.0571 \| 210.96 \| 2637 \| 0.8284 \| 0.775 \| 0.3552 \| 1.4516 \| 0.775 \| 0.7406 \| 0.1992 \| 0.1053 \| \| 0.0571 \| 212.0 \| 2650 \| 0.8278 \| 0.77 \| 0.3545 \| 1.4533 \| 0.7700 \| 0.7360 \| 0.1938 \| 0.1056 \| \| 0.0571 \| 212.96 \| 2662 \| 0.8289 \| 0.77 \| 0.3552 \| 1.4533 \| 0.7700 \| 0.7380 \| 0.2017 \| 0.1057 \| \| 0.0571 \| 214.0 \| 2675 \| 0.8290 \| 0.775 \| 0.3556 \| 1.4530 \| 0.775 \| 0.7406 \| 0.2005 \| 0.1052 \| \| 0.0571 \| 214.96 \| 2687 \| 0.8282 \| 0.77 \| 0.3551 \| 1.4517 \| 0.7700 \| 0.7379 \| 0.1985 \| 0.1037 \| \| 0.0571 \| 216.0 \| 2700 \| 0.8294 \| 0.77 \| 0.3555 \| 1.4588 \| 0.7700 \| 0.7349 \| 0.1941 \| 0.1045 \| \| 0.0571 \| 216.96 \| 2712 \| 0.8305 \| 0.775 \| 0.3562 \| 1.4516 \| 0.775 \| 0.7406 \| 0.1977 \| 0.1057 \| \| 0.0571 \| 218.0 \| 2725 \| 0.8310 \| 0.77 \| 0.3565 \| 1.4539 \| 0.7700 \| 0.7380 \| 0.1926 \| 0.1054 \| \| 0.0571 \| 218.96 \| 2737 \| 0.8304 \| 0.775 \| 0.3560 \| 1.4516 \| 0.775 \| 0.7406 \| 0.1986 \| 0.1054 \| \| 0.0571 \| 220.0 \| 2750 \| 0.8320 \| 0.775 \| 0.3568 \| 1.4545 \| 0.775 \| 0.7406 \| 0.1953 \| 0.1054 \| \| 0.0571 \| 220.96 \| 2762 \| 0.8316 \| 0.775 \| 0.3569 \| 1.4523 \| 0.775 \| 0.7406 \| 0.1945 \| 0.1045 \| \| 0.0571 \| 222.0 \| 2775 \| 0.8330 \| 0.77 \| 0.3573 \| 1.4547 \| 0.7700 \| 0.7380 \| 0.1892 \| 0.1067 \| \| 0.0571 \| 222.96 \| 2787 \| 0.8309 \| 0.77 \| 0.3563 \| 1.4548 \| 0.7700 \| 0.7379 \| 0.2060 \| 0.1033 \| \| 0.0571 \| 224.0 \| 2800 \| 0.8323 \| 0.775 \| 0.3572 \| 1.4515 \| 0.775 \| 0.7406 \| 0.1910 \| 0.1050 \| \| 0.0571 \| 224.96 \| 2812 \| 0.8329 \| 0.775 \| 0.3569 \| 1.4530 \| 0.775 \| 0.7406 \| 0.1931 \| 0.1055 \| \| 0.0571 \| 226.0 \| 2825 \| 0.8319 \| 0.78 \| 0.3567 \| 1.4513 \| 0.78 \| 0.7444 \| 0.2038 \| 0.1043 \| \| 0.0571 \| 226.96 \| 2837 \| 0.8354 \| 0.77 \| 0.3586 \| 1.4556 \| 0.7700 \| 0.7380 \| 0.1969 \| 0.1068 \| \| 0.0571 \| 228.0 \| 2850 \| 0.8340 \| 0.78 \| 0.3575 \| 1.4550 \| 0.78 \| 0.7444 \| 0.2043 \| 0.1062 \| \| 0.0571 \| 228.96 \| 2862 \| 0.8355 \| 0.775 \| 0.3584 \| 1.4546 \| 0.775 \| 0.7406 \| 0.2048 \| 0.1055 \| \| 0.0571 \| 230.0 \| 2875 \| 0.8350 \| 0.78 \| 0.3579 \| 1.4538 \| 0.78 \| 0.7444 \| 0.2069 \| 0.1064 \| \| 0.0571 \| 230.96 \| 2887 \| 0.8358 \| 0.77 \| 0.3584 \| 1.4550 \| 0.7700 \| 0.7380 \| 0.1899 \| 0.1061 \| \| 0.0571 \| 232.0 \| 2900 \| 0.8366 \| 0.77 \| 0.3587 \| 1.4564 \| 0.7700 \| 0.7380 \| 0.1921 \| 0.1070 \| \| 0.0571 \| 232.96 \| 2912 \| 0.8364 \| 0.775 \| 0.3587 \| 1.4557 \| 0.775 \| 0.7418 \| 0.1970 \| 0.1065 \| \| 0.0571 \| 234.0 \| 2925 \| 0.8359 \| 0.775 \| 0.3585 \| 1.4543 \| 0.775 \| 0.7406 \| 0.1912 \| 0.1061 \| \| 0.0571 \| 234.96 \| 2937 \| 0.8360 \| 0.775 \| 0.3587 \| 1.4540 \| 0.775 \| 0.7406 \| 0.2017 \| 0.1049 \| \| 0.0571 \| 236.0 \| 2950 \| 0.8362 \| 0.78 \| 0.3587 \| 1.4527 \| 0.78 \| 0.7444 \| 0.1985 \| 0.1060 \| \| 0.0571 \| 236.96 \| 2962 \| 0.8375 \| 0.78 \| 0.3593 \| 1.4554 \| 0.78 \| 0.7444 \| 0.2035 \| 0.1061 \| \| 0.0571 \| 238.0 \| 2975 \| 0.8378 \| 0.775 \| 0.3593 \| 1.4544 \| 0.775 \| 0.7418 \| 0.1971 \| 0.1068 \| \| 0.0571 \| 238.96 \| 2987 \| 0.8369 \| 0.78 \| 0.3588 \| 1.4557 \| 0.78 \| 0.7444 \| 0.2178 \| 0.1057 \| \| 0.0512 \| 240.0 \| 3000 \| 0.8388 \| 0.77 \| 0.3600 \| 1.4558 \| 0.7700 \| 0.7380 \| 0.1939 \| 0.1067 \| \| 0.0512 \| 240.96 \| 3012 \| 0.8375 \| 0.78 \| 0.3593 \| 1.4540 \| 0.78 \| 0.7444 \| 0.2071 \| 0.1058 \| \| 0.0512 \| 242.0 \| 3025 \| 0.8393 \| 0.775 \| 0.3602 \| 1.4546 \| 0.775 \| 0.7406 \| 0.1990 \| 0.1066 \| \| 0.0512 \| 242.96 \| 3037 \| 0.8391 \| 0.775 \| 0.3601 \| 1.4551 \| 0.775 \| 0.7406 \| 0.2025 \| 0.1063 \| \| 0.0512 \| 244.0 \| 3050 \| 0.8414 \| 0.77 \| 0.3610 \| 1.4575 \| 0.7700 \| 0.7380 \| 0.1924 \| 0.1072 \| \| 0.0512 \| 244.96 \| 3062 \| 0.8385 \| 0.78 \| 0.3597 \| 1.4531 \| 0.78 \| 0.7444 \| 0.2062 \| 0.1059 \| \| 0.0512 \| 246.0 \| 3075 \| 0.8394 \| 0.78 \| 0.3603 \| 1.4583 \| 0.78 \| 0.7444 \| 0.1962 \| 0.1057 \| \| 0.0512 \| 246.96 \| 3087 \| 0.8401 \| 0.775 \| 0.3604 \| 1.4535 \| 0.775 \| 0.7406 \| 0.1880 \| 0.1060 \| \| 0.0512 \| 248.0 \| 3100 \| 0.8400 \| 0.78 \| 0.3605 \| 1.4550 \| 0.78 \| 0.7444 \| 0.2156 \| 0.1058 \| \| 0.0512 \| 248.96 \| 3112 \| 0.8404 \| 0.78 \| 0.3606 \| 1.4554 \| 0.78 \| 0.7444 \| 0.1977 \| 0.1061 \| \| 0.0512 \| 250.0 \| 3125 \| 0.8406 \| 0.78 \| 0.3607 \| 1.4542 \| 0.78 \| 0.7444 \| 0.2055 \| 0.1062 \| \| 0.0512 \| 250.96 \| 3137 \| 0.8408 \| 0.78 \| 0.3608 \| 1.4545 \| 0.78 \| 0.7444 \| 0.2036 \| 0.1062 \| \| 0.0512 \| 252.0 \| 3150 \| 0.8414 \| 0.78 \| 0.3611 \| 1.4560 \| 0.78 \| 0.7444 \| 0.2054 \| 0.1063 \| \| 0.0512 \| 252.96 \| 3162 \| 0.8424 \| 0.775 \| 0.3614 \| 1.4580 \| 0.775 \| 0.7418 \| 0.2037 \| 0.1072 \| \| 0.0512 \| 254.0 \| 3175 \| 0.8423 \| 0.775 \| 0.3616 \| 1.4558 \| 0.775 \| 0.7406 \| 0.2057 \| 0.1064 \| \| 0.0512 \| 254.96 \| 3187 \| 0.8422 \| 0.775 \| 0.3613 \| 1.4562 \| 0.775 \| 0.7418 \| 0.2070 \| 0.1066 \| \| 0.0512 \| 256.0 \| 3200 \| 0.8419 \| 0.78 \| 0.3612 \| 1.4562 \| 0.78 \| 0.7444 \| 0.2196 \| 0.1063 \| \| 0.0512 \| 256.96 \| 3212 \| 0.8434 \| 0.775 \| 0.3620 \| 1.4565 \| 0.775 \| 0.7406 \| 0.2033 \| 0.1065 \| \| 0.0512 \| 258.0 \| 3225 \| 0.8431 \| 0.775 \| 0.3619 \| 1.4557 \| 0.775 \| 0.7418 \| 0.2072 \| 0.1064 \| \| 0.0512 \| 258.96 \| 3237 \| 0.8435 \| 0.77 \| 0.3620 \| 1.4567 \| 0.7700 \| 0.7380 \| 0.1985 \| 0.1066 \| \| 0.0512 \| 260.0 \| 3250 \| 0.8433 \| 0.78 \| 0.3619 \| 1.4567 \| 0.78 \| 0.7444 \| 0.2179 \| 0.1065 \| \| 0.0512 \| 260.96 \| 3262 \| 0.8430 \| 0.78 \| 0.3619 \| 1.4558 \| 0.78 \| 0.7444 \| 0.2120 \| 0.1060 \| \| 0.0512 \| 262.0 \| 3275 \| 0.8432 \| 0.78 \| 0.3619 \| 1.4552 \| 0.78 \| 0.7444 \| 0.2058 \| 0.1060 \| \| 0.0512 \| 262.96 \| 3287 \| 0.8444 \| 0.775 \| 0.3623 \| 1.4572 \| 0.775 \| 0.7418 \| 0.2035 \| 0.1068 \| \| 0.0512 \| 264.0 \| 3300 \| 0.8442 \| 0.775 \| 0.3622 \| 1.4574 \| 0.775 \| 0.7418 \| 0.2054 \| 0.1067 \| \| 0.0512 \| 264.96 \| 3312 \| 0.8441 \| 0.78 \| 0.3623 \| 1.4554 \| 0.78 \| 0.7444 \| 0.2051 \| 0.1062 \| \| 0.0512 \| 266.0 \| 3325 \| 0.8446 \| 0.775 \| 0.3624 \| 1.4561 \| 0.775 \| 0.7418 \| 0.1975 \| 0.1066 \| \| 0.0512 \| 266.96 \| 3337 \| 0.8447 \| 0.775 \| 0.3624 \| 1.4570 \| 0.775 \| 0.7418 \| 0.2053 \| 0.1065 \| \| 0.0512 \| 268.0 \| 3350 \| 0.8448 \| 0.78 \| 0.3624 \| 1.4573 \| 0.78 \| 0.7444 \| 0.2085 \| 0.1065 \| \| 0.0512 \| 268.96 \| 3362 \| 0.8443 \| 0.78 \| 0.3624 \| 1.4558 \| 0.78 \| 0.7444 \| 0.2119 \| 0.1065 \| \| 0.0512 \| 270.0 \| 3375 \| 0.8453 \| 0.775 \| 0.3628 \| 1.4571 \| 0.775 \| 0.7418 \| 0.2035 \| 0.1067 \| \| 0.0512 \| 270.96 \| 3387 \| 0.8444 \| 0.78 \| 0.3623 \| 1.4561 \| 0.78 \| 0.7444 \| 0.2076 \| 0.1063 \| \| 0.0512 \| 272.0 \| 3400 \| 0.8455 \| 0.775 \| 0.3629 \| 1.4569 \| 0.775 \| 0.7418 \| 0.2034 \| 0.1066 \| \| 0.0512 \| 272.96 \| 3412 \| 0.8453 \| 0.78 \| 0.3628 \| 1.4574 \| 0.78 \| 0.7444 \| 0.2021 \| 0.1065 \| \| 0.0512 \| 274.0 \| 3425 \| 0.8450 \| 0.78 \| 0.3626 \| 1.4560 \| 0.78 \| 0.7444 \| 0.2058 \| 0.1064 \| \| 0.0512 \| 274.96 \| 3437 \| 0.8456 \| 0.775 \| 0.3629 \| 1.4569 \| 0.775 \| 0.7418 \| 0.2035 \| 0.1066 \| \| 0.0512 \| 276.0 \| 3450 \| 0.8454 \| 0.775 \| 0.3628 \| 1.4565 \| 0.775 \| 0.7418 \| 0.2033 \| 0.1065 \| \| 0.0512 \| 276.96 \| 3462 \| 0.8454 \| 0.78 \| 0.3628 \| 1.4575 \| 0.78 \| 0.7444 \| 0.2137 \| 0.1063 \| \| 0.0512 \| 278.0 \| 3475 \| 0.8457 \| 0.78 \| 0.3630 \| 1.4567 \| 0.78 \| 0.7444 \| 0.2092 \| 0.1065 \| \| 0.0512 \| 278.96 \| 3487 \| 0.8462 \| 0.775 \| 0.3632 \| 1.4567 \| 0.775 \| 0.7418 \| 0.1994 \| 0.1067 \| \| 0.0481 \| 280.0 \| 3500 \| 0.8456 \| 0.78 \| 0.3630 \| 1.4572 \| 0.78 \| 0.7444 \| 0.2192 \| 0.1064 \| \| 0.0481 \| 280.96 \| 3512 \| 0.8462 \| 0.775 \| 0.3632 \| 1.4571 \| 0.775 \| 0.7418 \| 0.2034 \| 0.1066 \| \| 0.0481 \| 282.0 \| 3525 \| 0.8457 \| 0.775 \| 0.3630 \| 1.4563 \| 0.775 \| 0.7418 \| 0.2042 \| 0.1065 \| \| 0.0481 \| 282.96 \| 3537 \| 0.8460 \| 0.775 \| 0.3631 \| 1.4570 \| 0.775 \| 0.7418 \| 0.2106 \| 0.1066 \| \| 0.0481 \| 284.0 \| 3550 \| 0.8462 \| 0.775 \| 0.3632 \| 1.4570 \| 0.775 \| 0.7418 \| 0.2106 \| 0.1067 \| \| 0.0481 \| 284.96 \| 3562 \| 0.8460 \| 0.775 \| 0.3631 \| 1.4567 \| 0.775 \| 0.7418 \| 0.2042 \| 0.1065 \| \| 0.0481 \| 286.0 \| 3575 \| 0.8461 \| 0.775 \| 0.3632 \| 1.4568 \| 0.775 \| 0.7418 \| 0.2043 \| 0.1066 \| \| 0.0481 \| 286.96 \| 3587 \| 0.8461 \| 0.775 \| 0.3632 \| 1.4570 \| 0.775 \| 0.7418 \| 0.2043 \| 0.1066 \| \| 0.0481 \| 288.0 \| 3600 \| 0.8461 \| 0.775 \| 0.3632 \| 1.4570 \| 0.775 \| 0.7418 \| 0.2043 \| 0.1066 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/vit-base_tobacco_lr1e-5_wr_0.05_wd_0.1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_tobacco_lr1e-5_wr_0.05_wd_0.1 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9592 - Accuracy: 0.775 - Brier Loss: 0.3981 - Nll: 1.5416 - F1 Micro: 0.775 - F1 Macro: 0.7418 - Ece: 0.2227 - Aurc: 0.1082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 12 \| 0.7440 \| 0.815 \| 0.3076 \| 1.1842 \| 0.815 \| 0.7942 \| 0.2216 \| 0.0733 \| \| No log \| 2.0 \| 25 \| 0.7436 \| 0.82 \| 0.3075 \| 1.1869 \| 0.82 \| 0.8049 \| 0.2132 \| 0.0741 \| \| No log \| 2.96 \| 37 \| 0.7454 \| 0.81 \| 0.3085 \| 1.1880 \| 0.81 \| 0.7914 \| 0.2312 \| 0.0755 \| \| No log \| 4.0 \| 50 \| 0.7439 \| 0.815 \| 0.3077 \| 1.1846 \| 0.815 \| 0.7926 \| 0.2369 \| 0.0760 \| \| No log \| 4.96 \| 62 \| 0.7370 \| 0.82 \| 0.3040 \| 1.1982 \| 0.82 \| 0.8028 \| 0.2374 \| 0.0745 \| \| No log \| 6.0 \| 75 \| 0.7507 \| 0.82 \| 0.3112 \| 1.1980 \| 0.82 \| 0.8005 \| 0.2513 \| 0.0809 \| \| No log \| 6.96 \| 87 \| 0.7370 \| 0.805 \| 0.3060 \| 1.1778 \| 0.805 \| 0.7841 \| 0.2522 \| 0.0746 \| \| No log \| 8.0 \| 100 \| 0.7437 \| 0.81 \| 0.3076 \| 1.1846 \| 0.81 \| 0.7877 \| 0.2301 \| 0.0804 \| \| No log \| 8.96 \| 112 \| 0.7311 \| 0.81 \| 0.3031 \| 1.1975 \| 0.81 \| 0.7920 \| 0.2084 \| 0.0753 \| \| No log \| 10.0 \| 125 \| 0.7305 \| 0.8 \| 0.3020 \| 1.1785 \| 0.8000 \| 0.7792 \| 0.2131 \| 0.0777 \| \| No log \| 10.96 \| 137 \| 0.7478 \| 0.805 \| 0.3119 \| 1.3979 \| 0.805 \| 0.7860 \| 0.2133 \| 0.0827 \| \| No log \| 12.0 \| 150 \| 0.7469 \| 0.805 \| 0.3082 \| 1.3337 \| 0.805 \| 0.7844 \| 0.2213 \| 0.0843 \| \| No log \| 12.96 \| 162 \| 0.7545 \| 0.805 \| 0.3114 \| 1.4280 \| 0.805 \| 0.7893 \| 0.2092 \| 0.0935 \| \| No log \| 14.0 \| 175 \| 0.7283 \| 0.795 \| 0.3012 \| 1.1856 \| 0.795 \| 0.7739 \| 0.2182 \| 0.0806 \| \| No log \| 14.96 \| 187 \| 0.7219 \| 0.815 \| 0.2972 \| 1.2792 \| 0.815 \| 0.8043 \| 0.2024 \| 0.0734 \| \| No log \| 16.0 \| 200 \| 0.7284 \| 0.805 \| 0.3001 \| 1.2528 \| 0.805 \| 0.7899 \| 0.2068 \| 0.0858 \| \| No log \| 16.96 \| 212 \| 0.7191 \| 0.805 \| 0.2981 \| 1.3067 \| 0.805 \| 0.7919 \| 0.2062 \| 0.0809 \| \| No log \| 18.0 \| 225 \| 0.7221 \| 0.8 \| 0.3011 \| 1.1747 \| 0.8000 \| 0.7792 \| 0.2091 \| 0.0803 \| \| No log \| 18.96 \| 237 \| 0.7253 \| 0.81 \| 0.2995 \| 1.3143 \| 0.81 \| 0.7955 \| 0.2136 \| 0.0889 \| \| No log \| 20.0 \| 250 \| 0.7186 \| 0.8 \| 0.2981 \| 1.1839 \| 0.8000 \| 0.7819 \| 0.1899 \| 0.0812 \| \| No log \| 20.96 \| 262 \| 0.7247 \| 0.805 \| 0.3012 \| 1.2501 \| 0.805 \| 0.7925 \| 0.2214 \| 0.0891 \| \| No log \| 22.0 \| 275 \| 0.7317 \| 0.805 \| 0.3058 \| 1.3767 \| 0.805 \| 0.7853 \| 0.2141 \| 0.0893 \| \| No log \| 22.96 \| 287 \| 0.7250 \| 0.81 \| 0.3031 \| 1.3683 \| 0.81 \| 0.7907 \| 0.1886 \| 0.0838 \| \| No log \| 24.0 \| 300 \| 0.7137 \| 0.805 \| 0.2983 \| 1.3088 \| 0.805 \| 0.7851 \| 0.1799 \| 0.0782 \| \| No log \| 24.96 \| 312 \| 0.7334 \| 0.81 \| 0.3070 \| 1.4296 \| 0.81 \| 0.7909 \| 0.1903 \| 0.0898 \| \| No log \| 26.0 \| 325 \| 0.7284 \| 0.81 \| 0.3035 \| 1.2467 \| 0.81 \| 0.7984 \| 0.2152 \| 0.0916 \| \| No log \| 26.96 \| 337 \| 0.7242 \| 0.805 \| 0.3020 \| 1.3077 \| 0.805 \| 0.7862 \| 0.2071 \| 0.0891 \| \| No log \| 28.0 \| 350 \| 0.7285 \| 0.81 \| 0.3028 \| 1.3756 \| 0.81 \| 0.7910 \| 0.2158 \| 0.0915 \| \| No log \| 28.96 \| 362 \| 0.7253 \| 0.8 \| 0.3016 \| 1.3714 \| 0.8000 \| 0.7716 \| 0.2057 \| 0.0894 \| \| No log \| 30.0 \| 375 \| 0.7321 \| 0.8 \| 0.3068 \| 1.3688 \| 0.8000 \| 0.7736 \| 0.1943 \| 0.0885 \| \| No log \| 30.96 \| 387 \| 0.7294 \| 0.8 \| 0.3047 \| 1.3713 \| 0.8000 \| 0.7746 \| 0.2138 \| 0.0900 \| \| No log \| 32.0 \| 400 \| 0.7296 \| 0.81 \| 0.3054 \| 1.3749 \| 0.81 \| 0.7921 \| 0.2074 \| 0.0910 \| \| No log \| 32.96 \| 412 \| 0.7311 \| 0.805 \| 0.3061 \| 1.3704 \| 0.805 \| 0.7811 \| 0.1984 \| 0.0920 \| \| No log \| 34.0 \| 425 \| 0.7291 \| 0.805 \| 0.3049 \| 1.3686 \| 0.805 \| 0.7811 \| 0.2126 \| 0.0916 \| \| No log \| 34.96 \| 437 \| 0.7301 \| 0.795 \| 0.3048 \| 1.3712 \| 0.795 \| 0.7654 \| 0.1917 \| 0.0904 \| \| No log \| 36.0 \| 450 \| 0.7318 \| 0.81 \| 0.3072 \| 1.3695 \| 0.81 \| 0.7844 \| 0.1976 \| 0.0900 \| \| No log \| 36.96 \| 462 \| 0.7403 \| 0.795 \| 0.3102 \| 1.3712 \| 0.795 \| 0.7656 \| 0.2039 \| 0.0934 \| \| No log \| 38.0 \| 475 \| 0.7376 \| 0.795 \| 0.3095 \| 1.3653 \| 0.795 \| 0.7654 \| 0.1982 \| 0.0920 \| \| No log \| 38.96 \| 487 \| 0.7326 \| 0.805 \| 0.3049 \| 1.3815 \| 0.805 \| 0.7744 \| 0.1820 \| 0.0948 \| \| 0.1331 \| 40.0 \| 500 \| 0.7268 \| 0.8 \| 0.3038 \| 1.3702 \| 0.8000 \| 0.7704 \| 0.2051 \| 0.0899 \| \| 0.1331 \| 40.96 \| 512 \| 0.7371 \| 0.8 \| 0.3074 \| 1.3824 \| 0.8000 \| 0.7684 \| 0.1946 \| 0.0939 \| \| 0.1331 \| 42.0 \| 525 \| 0.7374 \| 0.81 \| 0.3107 \| 1.3600 \| 0.81 \| 0.7844 \| 0.2109 \| 0.0910 \| \| 0.1331 \| 42.96 \| 537 \| 0.7366 \| 0.8 \| 0.3071 \| 1.4434 \| 0.8000 \| 0.7776 \| 0.2042 \| 0.0935 \| \| 0.1331 \| 44.0 \| 550 \| 0.7362 \| 0.805 \| 0.3083 \| 1.3721 \| 0.805 \| 0.7829 \| 0.1782 \| 0.0929 \| \| 0.1331 \| 44.96 \| 562 \| 0.7389 \| 0.8 \| 0.3110 \| 1.3695 \| 0.8000 \| 0.7704 \| 0.1966 \| 0.0917 \| \| 0.1331 \| 46.0 \| 575 \| 0.7426 \| 0.79 \| 0.3108 \| 1.5068 \| 0.79 \| 0.7644 \| 0.1938 \| 0.0968 \| \| 0.1331 \| 46.96 \| 587 \| 0.7395 \| 0.8 \| 0.3096 \| 1.3760 \| 0.8000 \| 0.7704 \| 0.1951 \| 0.0927 \| \| 0.1331 \| 48.0 \| 600 \| 0.7540 \| 0.805 \| 0.3185 \| 1.4936 \| 0.805 \| 0.7821 \| 0.1958 \| 0.0979 \| \| 0.1331 \| 48.96 \| 612 \| 0.7413 \| 0.805 \| 0.3116 \| 1.4368 \| 0.805 \| 0.7829 \| 0.1835 \| 0.0955 \| \| 0.1331 \| 50.0 \| 625 \| 0.7543 \| 0.805 \| 0.3167 \| 1.4402 \| 0.805 \| 0.7831 \| 0.2143 \| 0.0974 \| \| 0.1331 \| 50.96 \| 637 \| 0.7378 \| 0.805 \| 0.3087 \| 1.3850 \| 0.805 \| 0.7829 \| 0.1886 \| 0.0935 \| \| 0.1331 \| 52.0 \| 650 \| 0.7545 \| 0.795 \| 0.3175 \| 1.3873 \| 0.795 \| 0.7656 \| 0.2007 \| 0.0957 \| \| 0.1331 \| 52.96 \| 662 \| 0.7464 \| 0.8 \| 0.3140 \| 1.3734 \| 0.8000 \| 0.7707 \| 0.1872 \| 0.0938 \| \| 0.1331 \| 54.0 \| 675 \| 0.7439 \| 0.8 \| 0.3120 \| 1.3765 \| 0.8000 \| 0.7704 \| 0.2036 \| 0.0942 \| \| 0.1331 \| 54.96 \| 687 \| 0.7506 \| 0.8 \| 0.3150 \| 1.3788 \| 0.8000 \| 0.7707 \| 0.1788 \| 0.0959 \| \| 0.1331 \| 56.0 \| 700 \| 0.7511 \| 0.805 \| 0.3158 \| 1.4378 \| 0.805 \| 0.7829 \| 0.2054 \| 0.0955 \| \| 0.1331 \| 56.96 \| 712 \| 0.7587 \| 0.805 \| 0.3196 \| 1.4494 \| 0.805 \| 0.7831 \| 0.1844 \| 0.0972 \| \| 0.1331 \| 58.0 \| 725 \| 0.7505 \| 0.8 \| 0.3154 \| 1.3759 \| 0.8000 \| 0.7704 \| 0.1913 \| 0.0953 \| \| 0.1331 \| 58.96 \| 737 \| 0.7553 \| 0.79 \| 0.3167 \| 1.4457 \| 0.79 \| 0.7549 \| 0.1977 \| 0.0959 \| \| 0.1331 \| 60.0 \| 750 \| 0.7543 \| 0.8 \| 0.3175 \| 1.3807 \| 0.8000 \| 0.7707 \| 0.1963 \| 0.0953 \| \| 0.1331 \| 60.96 \| 762 \| 0.7592 \| 0.795 \| 0.3200 \| 1.3759 \| 0.795 \| 0.7681 \| 0.1986 \| 0.0961 \| \| 0.1331 \| 62.0 \| 775 \| 0.7557 \| 0.795 \| 0.3185 \| 1.3785 \| 0.795 \| 0.7634 \| 0.1971 \| 0.0948 \| \| 0.1331 \| 62.96 \| 787 \| 0.7591 \| 0.79 \| 0.3200 \| 1.4466 \| 0.79 \| 0.7613 \| 0.2033 \| 0.0963 \| \| 0.1331 \| 64.0 \| 800 \| 0.7624 \| 0.795 \| 0.3210 \| 1.4423 \| 0.795 \| 0.7621 \| 0.2030 \| 0.0962 \| \| 0.1331 \| 64.96 \| 812 \| 0.7674 \| 0.79 \| 0.3240 \| 1.4454 \| 0.79 \| 0.7596 \| 0.1973 \| 0.0969 \| \| 0.1331 \| 66.0 \| 825 \| 0.7645 \| 0.79 \| 0.3224 \| 1.4497 \| 0.79 \| 0.7611 \| 0.1999 \| 0.0964 \| \| 0.1331 \| 66.96 \| 837 \| 0.7652 \| 0.795 \| 0.3234 \| 1.4418 \| 0.795 \| 0.7668 \| 0.1819 \| 0.0968 \| \| 0.1331 \| 68.0 \| 850 \| 0.7695 \| 0.795 \| 0.3250 \| 1.4969 \| 0.795 \| 0.7606 \| 0.1914 \| 0.0979 \| \| 0.1331 \| 68.96 \| 862 \| 0.7708 \| 0.785 \| 0.3258 \| 1.4482 \| 0.785 \| 0.7516 \| 0.1954 \| 0.0976 \| \| 0.1331 \| 70.0 \| 875 \| 0.7691 \| 0.795 \| 0.3249 \| 1.4960 \| 0.795 \| 0.7673 \| 0.1895 \| 0.0976 \| \| 0.1331 \| 70.96 \| 887 \| 0.7741 \| 0.785 \| 0.3272 \| 1.5043 \| 0.785 \| 0.7519 \| 0.1898 \| 0.0982 \| \| 0.1331 \| 72.0 \| 900 \| 0.7788 \| 0.79 \| 0.3293 \| 1.5094 \| 0.79 \| 0.7611 \| 0.1738 \| 0.0995 \| \| 0.1331 \| 72.96 \| 912 \| 0.7837 \| 0.785 \| 0.3329 \| 1.5306 \| 0.785 \| 0.7577 \| 0.2002 \| 0.1004 \| \| 0.1331 \| 74.0 \| 925 \| 0.7755 \| 0.785 \| 0.3280 \| 1.4985 \| 0.785 \| 0.7514 \| 0.1906 \| 0.0981 \| \| 0.1331 \| 74.96 \| 937 \| 0.7797 \| 0.785 \| 0.3308 \| 1.4611 \| 0.785 \| 0.7580 \| 0.1925 \| 0.0994 \| \| 0.1331 \| 76.0 \| 950 \| 0.7744 \| 0.785 \| 0.3273 \| 1.4441 \| 0.785 \| 0.7519 \| 0.1929 \| 0.0976 \| \| 0.1331 \| 76.96 \| 962 \| 0.7766 \| 0.785 \| 0.3295 \| 1.4420 \| 0.785 \| 0.7516 \| 0.1899 \| 0.0972 \| \| 0.1331 \| 78.0 \| 975 \| 0.7888 \| 0.785 \| 0.3339 \| 1.4991 \| 0.785 \| 0.7573 \| 0.1879 \| 0.0998 \| \| 0.1331 \| 78.96 \| 987 \| 0.7765 \| 0.795 \| 0.3292 \| 1.4915 \| 0.795 \| 0.7663 \| 0.1750 \| 0.0948 \| \| 0.071 \| 80.0 \| 1000 \| 0.7821 \| 0.785 \| 0.3303 \| 1.4990 \| 0.785 \| 0.7519 \| 0.1940 \| 0.0986 \| \| 0.071 \| 80.96 \| 1012 \| 0.7860 \| 0.79 \| 0.3330 \| 1.4977 \| 0.79 \| 0.7644 \| 0.1698 \| 0.0976 \| \| 0.071 \| 82.0 \| 1025 \| 0.7882 \| 0.78 \| 0.3342 \| 1.5243 \| 0.78 \| 0.7482 \| 0.1930 \| 0.1006 \| \| 0.071 \| 82.96 \| 1037 \| 0.7879 \| 0.78 \| 0.3333 \| 1.5037 \| 0.78 \| 0.7491 \| 0.2055 \| 0.0995 \| \| 0.071 \| 84.0 \| 1050 \| 0.7842 \| 0.78 \| 0.3326 \| 1.4959 \| 0.78 \| 0.7488 \| 0.1945 \| 0.0985 \| \| 0.071 \| 84.96 \| 1062 \| 0.7866 \| 0.78 \| 0.3338 \| 1.4961 \| 0.78 \| 0.7488 \| 0.1877 \| 0.0982 \| \| 0.071 \| 86.0 \| 1075 \| 0.7931 \| 0.785 \| 0.3369 \| 1.5006 \| 0.785 \| 0.7573 \| 0.1898 \| 0.1003 \| \| 0.071 \| 86.96 \| 1087 \| 0.7937 \| 0.78 \| 0.3360 \| 1.5043 \| 0.78 \| 0.7488 \| 0.1828 \| 0.0999 \| \| 0.071 \| 88.0 \| 1100 \| 0.7948 \| 0.78 \| 0.3374 \| 1.5034 \| 0.78 \| 0.7488 \| 0.1893 \| 0.0999 \| \| 0.071 \| 88.96 \| 1112 \| 0.7962 \| 0.78 \| 0.3372 \| 1.5078 \| 0.78 \| 0.7494 \| 0.1943 \| 0.1011 \| \| 0.071 \| 90.0 \| 1125 \| 0.7956 \| 0.785 \| 0.3377 \| 1.5039 \| 0.785 \| 0.7516 \| 0.1918 \| 0.0999 \| \| 0.071 \| 90.96 \| 1137 \| 0.7996 \| 0.78 \| 0.3382 \| 1.5060 \| 0.78 \| 0.7491 \| 0.1982 \| 0.1013 \| \| 0.071 \| 92.0 \| 1150 \| 0.7980 \| 0.78 \| 0.3381 \| 1.5023 \| 0.78 \| 0.7488 \| 0.1902 \| 0.1004 \| \| 0.071 \| 92.96 \| 1162 \| 0.8015 \| 0.78 \| 0.3396 \| 1.5029 \| 0.78 \| 0.7488 \| 0.1978 \| 0.1007 \| \| 0.071 \| 94.0 \| 1175 \| 0.8044 \| 0.78 \| 0.3411 \| 1.5047 \| 0.78 \| 0.7488 \| 0.1929 \| 0.1012 \| \| 0.071 \| 94.96 \| 1187 \| 0.7977 \| 0.78 \| 0.3392 \| 1.4989 \| 0.78 \| 0.7488 \| 0.1866 \| 0.0989 \| \| 0.071 \| 96.0 \| 1200 \| 0.8071 \| 0.78 \| 0.3425 \| 1.5021 \| 0.78 \| 0.7488 \| 0.1941 \| 0.1018 \| \| 0.071 \| 96.96 \| 1212 \| 0.8033 \| 0.78 \| 0.3406 \| 1.4967 \| 0.78 \| 0.7488 \| 0.1913 \| 0.1000 \| \| 0.071 \| 98.0 \| 1225 \| 0.8148 \| 0.775 \| 0.3466 \| 1.4555 \| 0.775 \| 0.7462 \| 0.1828 \| 0.1036 \| \| 0.071 \| 98.96 \| 1237 \| 0.8062 \| 0.78 \| 0.3417 \| 1.5007 \| 0.78 \| 0.7488 \| 0.1949 \| 0.1004 \| \| 0.071 \| 100.0 \| 1250 \| 0.8123 \| 0.77 \| 0.3456 \| 1.5069 \| 0.7700 \| 0.7424 \| 0.1964 \| 0.1020 \| \| 0.071 \| 100.96 \| 1262 \| 0.8117 \| 0.78 \| 0.3452 \| 1.5048 \| 0.78 \| 0.7488 \| 0.2081 \| 0.1020 \| \| 0.071 \| 102.0 \| 1275 \| 0.8125 \| 0.77 \| 0.3454 \| 1.5066 \| 0.7700 \| 0.7424 \| 0.2040 \| 0.1022 \| \| 0.071 \| 102.96 \| 1287 \| 0.8134 \| 0.775 \| 0.3458 \| 1.5048 \| 0.775 \| 0.7450 \| 0.1977 \| 0.1013 \| \| 0.071 \| 104.0 \| 1300 \| 0.8152 \| 0.78 \| 0.3461 \| 1.5027 \| 0.78 \| 0.7488 \| 0.2044 \| 0.1014 \| \| 0.071 \| 104.96 \| 1312 \| 0.8185 \| 0.78 \| 0.3478 \| 1.5057 \| 0.78 \| 0.7488 \| 0.1900 \| 0.1022 \| \| 0.071 \| 106.0 \| 1325 \| 0.8191 \| 0.78 \| 0.3480 \| 1.5053 \| 0.78 \| 0.7488 \| 0.2084 \| 0.1026 \| \| 0.071 \| 106.96 \| 1337 \| 0.8207 \| 0.77 \| 0.3497 \| 1.5095 \| 0.7700 \| 0.7424 \| 0.1984 \| 0.1025 \| \| 0.071 \| 108.0 \| 1350 \| 0.8221 \| 0.77 \| 0.3487 \| 1.5095 \| 0.7700 \| 0.7424 \| 0.1871 \| 0.1031 \| \| 0.071 \| 108.96 \| 1362 \| 0.8229 \| 0.765 \| 0.3501 \| 1.4607 \| 0.765 \| 0.7331 \| 0.1920 \| 0.1028 \| \| 0.071 \| 110.0 \| 1375 \| 0.8232 \| 0.78 \| 0.3498 \| 1.5044 \| 0.78 \| 0.7488 \| 0.1995 \| 0.1023 \| \| 0.071 \| 110.96 \| 1387 \| 0.8279 \| 0.785 \| 0.3513 \| 1.5060 \| 0.785 \| 0.7526 \| 0.2073 \| 0.1033 \| \| 0.071 \| 112.0 \| 1400 \| 0.8246 \| 0.775 \| 0.3505 \| 1.5038 \| 0.775 \| 0.7450 \| 0.1927 \| 0.1018 \| \| 0.071 \| 112.96 \| 1412 \| 0.8308 \| 0.765 \| 0.3537 \| 1.5095 \| 0.765 \| 0.7331 \| 0.1931 \| 0.1035 \| \| 0.071 \| 114.0 \| 1425 \| 0.8277 \| 0.775 \| 0.3513 \| 1.5058 \| 0.775 \| 0.7395 \| 0.1977 \| 0.1022 \| \| 0.071 \| 114.96 \| 1437 \| 0.8302 \| 0.76 \| 0.3531 \| 1.4583 \| 0.76 \| 0.7296 \| 0.2112 \| 0.1028 \| \| 0.071 \| 116.0 \| 1450 \| 0.8328 \| 0.765 \| 0.3535 \| 1.5125 \| 0.765 \| 0.7331 \| 0.2008 \| 0.1037 \| \| 0.071 \| 116.96 \| 1462 \| 0.8309 \| 0.76 \| 0.3533 \| 1.4542 \| 0.76 \| 0.7296 \| 0.2037 \| 0.1029 \| \| 0.071 \| 118.0 \| 1475 \| 0.8378 \| 0.765 \| 0.3558 \| 1.5162 \| 0.765 \| 0.7323 \| 0.2040 \| 0.1055 \| \| 0.071 \| 118.96 \| 1487 \| 0.8341 \| 0.76 \| 0.3547 \| 1.5076 \| 0.76 \| 0.7296 \| 0.1942 \| 0.1032 \| \| 0.0462 \| 120.0 \| 1500 \| 0.8367 \| 0.76 \| 0.3557 \| 1.5134 \| 0.76 \| 0.7296 \| 0.1987 \| 0.1034 \| \| 0.0462 \| 120.96 \| 1512 \| 0.8369 \| 0.76 \| 0.3553 \| 1.5081 \| 0.76 \| 0.7296 \| 0.2121 \| 0.1036 \| \| 0.0462 \| 122.0 \| 1525 \| 0.8385 \| 0.77 \| 0.3560 \| 1.5076 \| 0.7700 \| 0.7357 \| 0.1944 \| 0.1034 \| \| 0.0462 \| 122.96 \| 1537 \| 0.8415 \| 0.76 \| 0.3577 \| 1.5127 \| 0.76 \| 0.7296 \| 0.2080 \| 0.1040 \| \| 0.0462 \| 124.0 \| 1550 \| 0.8418 \| 0.765 \| 0.3571 \| 1.5123 \| 0.765 \| 0.7333 \| 0.1905 \| 0.1043 \| \| 0.0462 \| 124.96 \| 1562 \| 0.8431 \| 0.76 \| 0.3581 \| 1.5124 \| 0.76 \| 0.7296 \| 0.2029 \| 0.1043 \| \| 0.0462 \| 126.0 \| 1575 \| 0.8461 \| 0.765 \| 0.3595 \| 1.5115 \| 0.765 \| 0.7331 \| 0.1861 \| 0.1044 \| \| 0.0462 \| 126.96 \| 1587 \| 0.8446 \| 0.76 \| 0.3586 \| 1.5117 \| 0.76 \| 0.7296 \| 0.1962 \| 0.1043 \| \| 0.0462 \| 128.0 \| 1600 \| 0.8448 \| 0.765 \| 0.3585 \| 1.5106 \| 0.765 \| 0.7333 \| 0.1899 \| 0.1048 \| \| 0.0462 \| 128.96 \| 1612 \| 0.8503 \| 0.765 \| 0.3611 \| 1.5156 \| 0.765 \| 0.7323 \| 0.1865 \| 0.1050 \| \| 0.0462 \| 130.0 \| 1625 \| 0.8473 \| 0.765 \| 0.3597 \| 1.5082 \| 0.765 \| 0.7333 \| 0.1992 \| 0.1040 \| \| 0.0462 \| 130.96 \| 1637 \| 0.8530 \| 0.76 \| 0.3617 \| 1.5178 \| 0.76 \| 0.7296 \| 0.2008 \| 0.1053 \| \| 0.0462 \| 132.0 \| 1650 \| 0.8499 \| 0.765 \| 0.3608 \| 1.5105 \| 0.765 \| 0.7321 \| 0.1910 \| 0.1035 \| \| 0.0462 \| 132.96 \| 1662 \| 0.8529 \| 0.765 \| 0.3612 \| 1.5095 \| 0.765 \| 0.7333 \| 0.1943 \| 0.1043 \| \| 0.0462 \| 134.0 \| 1675 \| 0.8547 \| 0.765 \| 0.3635 \| 1.5095 \| 0.765 \| 0.7321 \| 0.2002 \| 0.1032 \| \| 0.0462 \| 134.96 \| 1687 \| 0.8572 \| 0.765 \| 0.3638 \| 1.5159 \| 0.765 \| 0.7333 \| 0.1979 \| 0.1056 \| \| 0.0462 \| 136.0 \| 1700 \| 0.8582 \| 0.765 \| 0.3642 \| 1.5165 \| 0.765 \| 0.7333 \| 0.2026 \| 0.1057 \| \| 0.0462 \| 136.96 \| 1712 \| 0.8581 \| 0.76 \| 0.3639 \| 1.5118 \| 0.76 \| 0.7296 \| 0.1965 \| 0.1052 \| \| 0.0462 \| 138.0 \| 1725 \| 0.8570 \| 0.77 \| 0.3629 \| 1.5094 \| 0.7700 \| 0.7358 \| 0.1870 \| 0.1029 \| \| 0.0462 \| 138.96 \| 1737 \| 0.8611 \| 0.76 \| 0.3650 \| 1.5129 \| 0.76 \| 0.7296 \| 0.1919 \| 0.1040 \| \| 0.0462 \| 140.0 \| 1750 \| 0.8618 \| 0.76 \| 0.3659 \| 1.5131 \| 0.76 \| 0.7296 \| 0.1981 \| 0.1042 \| \| 0.0462 \| 140.96 \| 1762 \| 0.8605 \| 0.765 \| 0.3652 \| 1.5115 \| 0.765 \| 0.7333 \| 0.1875 \| 0.1048 \| \| 0.0462 \| 142.0 \| 1775 \| 0.8647 \| 0.76 \| 0.3666 \| 1.5157 \| 0.76 \| 0.7296 \| 0.2002 \| 0.1052 \| \| 0.0462 \| 142.96 \| 1787 \| 0.8618 \| 0.76 \| 0.3654 \| 1.5116 \| 0.76 \| 0.7296 \| 0.2006 \| 0.1045 \| \| 0.0462 \| 144.0 \| 1800 \| 0.8672 \| 0.765 \| 0.3672 \| 1.5160 \| 0.765 \| 0.7333 \| 0.1979 \| 0.1053 \| \| 0.0462 \| 144.96 \| 1812 \| 0.8625 \| 0.77 \| 0.3648 \| 1.5080 \| 0.7700 \| 0.7358 \| 0.1975 \| 0.1026 \| \| 0.0462 \| 146.0 \| 1825 \| 0.8695 \| 0.765 \| 0.3679 \| 1.5169 \| 0.765 \| 0.7323 \| 0.1973 \| 0.1051 \| \| 0.0462 \| 146.96 \| 1837 \| 0.8696 \| 0.76 \| 0.3685 \| 1.5132 \| 0.76 \| 0.7296 \| 0.1936 \| 0.1037 \| \| 0.0462 \| 148.0 \| 1850 \| 0.8678 \| 0.765 \| 0.3671 \| 1.5110 \| 0.765 \| 0.7333 \| 0.2008 \| 0.1040 \| \| 0.0462 \| 148.96 \| 1862 \| 0.8713 \| 0.765 \| 0.3690 \| 1.5152 \| 0.765 \| 0.7333 \| 0.1983 \| 0.1050 \| \| 0.0462 \| 150.0 \| 1875 \| 0.8716 \| 0.765 \| 0.3687 \| 1.5163 \| 0.765 \| 0.7323 \| 0.2029 \| 0.1051 \| \| 0.0462 \| 150.96 \| 1887 \| 0.8724 \| 0.77 \| 0.3691 \| 1.5113 \| 0.7700 \| 0.7358 \| 0.1997 \| 0.1037 \| \| 0.0462 \| 152.0 \| 1900 \| 0.8729 \| 0.765 \| 0.3695 \| 1.5134 \| 0.765 \| 0.7333 \| 0.1966 \| 0.1050 \| \| 0.0462 \| 152.96 \| 1912 \| 0.8760 \| 0.765 \| 0.3706 \| 1.5131 \| 0.765 \| 0.7333 \| 0.2046 \| 0.1040 \| \| 0.0462 \| 154.0 \| 1925 \| 0.8761 \| 0.765 \| 0.3707 \| 1.5138 \| 0.765 \| 0.7333 \| 0.1896 \| 0.1037 \| \| 0.0462 \| 154.96 \| 1937 \| 0.8778 \| 0.765 \| 0.3711 \| 1.5138 \| 0.765 \| 0.7333 \| 0.2012 \| 0.1046 \| \| 0.0462 \| 156.0 \| 1950 \| 0.8768 \| 0.765 \| 0.3712 \| 1.5125 \| 0.765 \| 0.7333 \| 0.1891 \| 0.1041 \| \| 0.0462 \| 156.96 \| 1962 \| 0.8816 \| 0.77 \| 0.3732 \| 1.5205 \| 0.7700 \| 0.7360 \| 0.1993 \| 0.1067 \| \| 0.0462 \| 158.0 \| 1975 \| 0.8793 \| 0.765 \| 0.3718 \| 1.5157 \| 0.765 \| 0.7333 \| 0.2025 \| 0.1049 \| \| 0.0462 \| 158.96 \| 1987 \| 0.8788 \| 0.77 \| 0.3713 \| 1.5126 \| 0.7700 \| 0.7358 \| 0.2044 \| 0.1039 \| \| 0.0335 \| 160.0 \| 2000 \| 0.8851 \| 0.77 \| 0.3739 \| 1.5193 \| 0.7700 \| 0.7360 \| 0.2042 \| 0.1069 \| \| 0.0335 \| 160.96 \| 2012 \| 0.8872 \| 0.77 \| 0.3748 \| 1.5200 \| 0.7700 \| 0.7360 \| 0.2009 \| 0.1057 \| \| 0.0335 \| 162.0 \| 2025 \| 0.8827 \| 0.765 \| 0.3731 \| 1.5144 \| 0.765 \| 0.7333 \| 0.1897 \| 0.1050 \| \| 0.0335 \| 162.96 \| 2037 \| 0.8821 \| 0.765 \| 0.3724 \| 1.5129 \| 0.765 \| 0.7333 \| 0.1971 \| 0.1042 \| \| 0.0335 \| 164.0 \| 2050 \| 0.8919 \| 0.77 \| 0.3770 \| 1.5229 \| 0.7700 \| 0.7360 \| 0.2119 \| 0.1061 \| \| 0.0335 \| 164.96 \| 2062 \| 0.8907 \| 0.765 \| 0.3764 \| 1.5240 \| 0.765 \| 0.7323 \| 0.2125 \| 0.1069 \| \| 0.0335 \| 166.0 \| 2075 \| 0.8857 \| 0.765 \| 0.3743 \| 1.5127 \| 0.765 \| 0.7333 \| 0.1906 \| 0.1044 \| \| 0.0335 \| 166.96 \| 2087 \| 0.8928 \| 0.77 \| 0.3771 \| 1.5253 \| 0.7700 \| 0.7360 \| 0.2062 \| 0.1062 \| \| 0.0335 \| 168.0 \| 2100 \| 0.8895 \| 0.77 \| 0.3750 \| 1.5179 \| 0.7700 \| 0.7360 \| 0.2062 \| 0.1054 \| \| 0.0335 \| 168.96 \| 2112 \| 0.8904 \| 0.77 \| 0.3754 \| 1.5178 \| 0.7700 \| 0.7360 \| 0.2048 \| 0.1055 \| \| 0.0335 \| 170.0 \| 2125 \| 0.8919 \| 0.765 \| 0.3766 \| 1.5137 \| 0.765 \| 0.7333 \| 0.2170 \| 0.1044 \| \| 0.0335 \| 170.96 \| 2137 \| 0.8949 \| 0.77 \| 0.3779 \| 1.5203 \| 0.7700 \| 0.7360 \| 0.2042 \| 0.1069 \| \| 0.0335 \| 172.0 \| 2150 \| 0.8949 \| 0.77 \| 0.3779 \| 1.5204 \| 0.7700 \| 0.7360 \| 0.2078 \| 0.1069 \| \| 0.0335 \| 172.96 \| 2162 \| 0.8986 \| 0.765 \| 0.3794 \| 1.5241 \| 0.765 \| 0.7310 \| 0.2079 \| 0.1072 \| \| 0.0335 \| 174.0 \| 2175 \| 0.8978 \| 0.76 \| 0.3787 \| 1.5201 \| 0.76 \| 0.7272 \| 0.2108 \| 0.1056 \| \| 0.0335 \| 174.96 \| 2187 \| 0.8990 \| 0.77 \| 0.3786 \| 1.5198 \| 0.7700 \| 0.7360 \| 0.2032 \| 0.1053 \| \| 0.0335 \| 176.0 \| 2200 \| 0.9003 \| 0.77 \| 0.3794 \| 1.5206 \| 0.7700 \| 0.7360 \| 0.1996 \| 0.1060 \| \| 0.0335 \| 176.96 \| 2212 \| 0.9000 \| 0.77 \| 0.3797 \| 1.5196 \| 0.7700 \| 0.7360 \| 0.2116 \| 0.1063 \| \| 0.0335 \| 178.0 \| 2225 \| 0.9000 \| 0.765 \| 0.3794 \| 1.5178 \| 0.765 \| 0.7333 \| 0.1875 \| 0.1055 \| \| 0.0335 \| 178.96 \| 2237 \| 0.9034 \| 0.77 \| 0.3804 \| 1.5218 \| 0.7700 \| 0.7360 \| 0.1964 \| 0.1068 \| \| 0.0335 \| 180.0 \| 2250 \| 0.9020 \| 0.77 \| 0.3802 \| 1.5198 \| 0.7700 \| 0.7360 \| 0.2058 \| 0.1063 \| \| 0.0335 \| 180.96 \| 2262 \| 0.9037 \| 0.77 \| 0.3808 \| 1.5192 \| 0.7700 \| 0.7360 \| 0.1976 \| 0.1063 \| \| 0.0335 \| 182.0 \| 2275 \| 0.9059 \| 0.77 \| 0.3812 \| 1.5227 \| 0.7700 \| 0.7360 \| 0.1962 \| 0.1067 \| \| 0.0335 \| 182.96 \| 2287 \| 0.9063 \| 0.77 \| 0.3818 \| 1.5206 \| 0.7700 \| 0.7360 \| 0.2000 \| 0.1065 \| \| 0.0335 \| 184.0 \| 2300 \| 0.9058 \| 0.77 \| 0.3814 \| 1.5196 \| 0.7700 \| 0.7360 \| 0.1926 \| 0.1061 \| \| 0.0335 \| 184.96 \| 2312 \| 0.9082 \| 0.77 \| 0.3821 \| 1.5211 \| 0.7700 \| 0.7360 \| 0.2001 \| 0.1067 \| \| 0.0335 \| 186.0 \| 2325 \| 0.9083 \| 0.77 \| 0.3824 \| 1.5204 \| 0.7700 \| 0.7360 \| 0.2062 \| 0.1057 \| \| 0.0335 \| 186.96 \| 2337 \| 0.9090 \| 0.77 \| 0.3824 \| 1.5220 \| 0.7700 \| 0.7360 \| 0.2027 \| 0.1063 \| \| 0.0335 \| 188.0 \| 2350 \| 0.9106 \| 0.77 \| 0.3828 \| 1.5213 \| 0.7700 \| 0.7360 \| 0.1968 \| 0.1068 \| \| 0.0335 \| 188.96 \| 2362 \| 0.9116 \| 0.77 \| 0.3829 \| 1.5238 \| 0.7700 \| 0.7360 \| 0.2029 \| 0.1071 \| \| 0.0335 \| 190.0 \| 2375 \| 0.9120 \| 0.77 \| 0.3835 \| 1.5225 \| 0.7700 \| 0.7360 \| 0.1953 \| 0.1064 \| \| 0.0335 \| 190.96 \| 2387 \| 0.9123 \| 0.77 \| 0.3835 \| 1.5227 \| 0.7700 \| 0.7360 \| 0.2080 \| 0.1069 \| \| 0.0335 \| 192.0 \| 2400 \| 0.9131 \| 0.775 \| 0.3838 \| 1.5222 \| 0.775 \| 0.7418 \| 0.2039 \| 0.1061 \| \| 0.0335 \| 192.96 \| 2412 \| 0.9144 \| 0.765 \| 0.3841 \| 1.5200 \| 0.765 \| 0.7333 \| 0.2163 \| 0.1060 \| \| 0.0335 \| 194.0 \| 2425 \| 0.9138 \| 0.77 \| 0.3839 \| 1.5200 \| 0.7700 \| 0.7360 \| 0.2092 \| 0.1057 \| \| 0.0335 \| 194.96 \| 2437 \| 0.9164 \| 0.775 \| 0.3850 \| 1.5249 \| 0.775 \| 0.7418 \| 0.2188 \| 0.1065 \| \| 0.0335 \| 196.0 \| 2450 \| 0.9185 \| 0.77 \| 0.3861 \| 1.5257 \| 0.7700 \| 0.7360 \| 0.2087 \| 0.1067 \| \| 0.0335 \| 196.96 \| 2462 \| 0.9207 \| 0.77 \| 0.3868 \| 1.5286 \| 0.7700 \| 0.7360 \| 0.2063 \| 0.1074 \| \| 0.0335 \| 198.0 \| 2475 \| 0.9191 \| 0.77 \| 0.3858 \| 1.5254 \| 0.7700 \| 0.7360 \| 0.2129 \| 0.1068 \| \| 0.0335 \| 198.96 \| 2487 \| 0.9195 \| 0.77 \| 0.3861 \| 1.5240 \| 0.7700 \| 0.7360 \| 0.2059 \| 0.1066 \| \| 0.0264 \| 200.0 \| 2500 \| 0.9205 \| 0.77 \| 0.3864 \| 1.5246 \| 0.7700 \| 0.7360 \| 0.2081 \| 0.1069 \| \| 0.0264 \| 200.96 \| 2512 \| 0.9214 \| 0.77 \| 0.3865 \| 1.5235 \| 0.7700 \| 0.7360 \| 0.2018 \| 0.1066 \| \| 0.0264 \| 202.0 \| 2525 \| 0.9216 \| 0.775 \| 0.3867 \| 1.5253 \| 0.775 \| 0.7418 \| 0.2156 \| 0.1068 \| \| 0.0264 \| 202.96 \| 2537 \| 0.9218 \| 0.775 \| 0.3870 \| 1.5225 \| 0.775 \| 0.7418 \| 0.2108 \| 0.1064 \| \| 0.0264 \| 204.0 \| 2550 \| 0.9241 \| 0.775 \| 0.3871 \| 1.4893 \| 0.775 \| 0.7418 \| 0.2087 \| 0.1071 \| \| 0.0264 \| 204.96 \| 2562 \| 0.9270 \| 0.77 \| 0.3889 \| 1.5244 \| 0.7700 \| 0.7360 \| 0.2024 \| 0.1071 \| \| 0.0264 \| 206.0 \| 2575 \| 0.9260 \| 0.775 \| 0.3885 \| 1.5262 \| 0.775 \| 0.7418 \| 0.2116 \| 0.1069 \| \| 0.0264 \| 206.96 \| 2587 \| 0.9259 \| 0.775 \| 0.3883 \| 1.5269 \| 0.775 \| 0.7418 \| 0.2089 \| 0.1065 \| \| 0.0264 \| 208.0 \| 2600 \| 0.9254 \| 0.77 \| 0.3875 \| 1.5247 \| 0.7700 \| 0.7360 \| 0.2060 \| 0.1069 \| \| 0.0264 \| 208.96 \| 2612 \| 0.9285 \| 0.775 \| 0.3889 \| 1.5281 \| 0.775 \| 0.7418 \| 0.2115 \| 0.1074 \| \| 0.0264 \| 210.0 \| 2625 \| 0.9277 \| 0.775 \| 0.3886 \| 1.5254 \| 0.775 \| 0.7418 \| 0.2114 \| 0.1069 \| \| 0.0264 \| 210.96 \| 2637 \| 0.9304 \| 0.775 \| 0.3897 \| 1.5278 \| 0.775 \| 0.7418 \| 0.2095 \| 0.1071 \| \| 0.0264 \| 212.0 \| 2650 \| 0.9288 \| 0.77 \| 0.3886 \| 1.5270 \| 0.7700 \| 0.7360 \| 0.2068 \| 0.1070 \| \| 0.0264 \| 212.96 \| 2662 \| 0.9310 \| 0.775 \| 0.3896 \| 1.5316 \| 0.775 \| 0.7418 \| 0.2135 \| 0.1071 \| \| 0.0264 \| 214.0 \| 2675 \| 0.9311 \| 0.775 \| 0.3899 \| 1.5263 \| 0.775 \| 0.7418 \| 0.2187 \| 0.1070 \| \| 0.0264 \| 214.96 \| 2687 \| 0.9315 \| 0.775 \| 0.3899 \| 1.5256 \| 0.775 \| 0.7418 \| 0.2123 \| 0.1068 \| \| 0.0264 \| 216.0 \| 2700 \| 0.9315 \| 0.77 \| 0.3896 \| 1.5258 \| 0.7700 \| 0.7360 \| 0.2070 \| 0.1071 \| \| 0.0264 \| 216.96 \| 2712 \| 0.9334 \| 0.775 \| 0.3905 \| 1.5291 \| 0.775 \| 0.7418 \| 0.2088 \| 0.1071 \| \| 0.0264 \| 218.0 \| 2725 \| 0.9342 \| 0.775 \| 0.3908 \| 1.5283 \| 0.775 \| 0.7418 \| 0.2146 \| 0.1072 \| \| 0.0264 \| 218.96 \| 2737 \| 0.9337 \| 0.775 \| 0.3903 \| 1.5282 \| 0.775 \| 0.7418 \| 0.2110 \| 0.1070 \| \| 0.0264 \| 220.0 \| 2750 \| 0.9357 \| 0.775 \| 0.3913 \| 1.5284 \| 0.775 \| 0.7418 \| 0.2149 \| 0.1073 \| \| 0.0264 \| 220.96 \| 2762 \| 0.9367 \| 0.775 \| 0.3918 \| 1.5299 \| 0.775 \| 0.7418 \| 0.2088 \| 0.1072 \| \| 0.0264 \| 222.0 \| 2775 \| 0.9371 \| 0.775 \| 0.3916 \| 1.5294 \| 0.775 \| 0.7418 \| 0.2141 \| 0.1075 \| \| 0.0264 \| 222.96 \| 2787 \| 0.9359 \| 0.775 \| 0.3910 \| 1.5271 \| 0.775 \| 0.7418 \| 0.2126 \| 0.1067 \| \| 0.0264 \| 224.0 \| 2800 \| 0.9374 \| 0.775 \| 0.3918 \| 1.5298 \| 0.775 \| 0.7418 \| 0.2084 \| 0.1072 \| \| 0.0264 \| 224.96 \| 2812 \| 0.9378 \| 0.775 \| 0.3914 \| 1.5296 \| 0.775 \| 0.7418 \| 0.2073 \| 0.1072 \| \| 0.0264 \| 226.0 \| 2825 \| 0.9377 \| 0.775 \| 0.3916 \| 1.5274 \| 0.775 \| 0.7418 \| 0.2075 \| 0.1066 \| \| 0.0264 \| 226.96 \| 2837 \| 0.9412 \| 0.775 \| 0.3932 \| 1.5310 \| 0.775 \| 0.7418 \| 0.2096 \| 0.1077 \| \| 0.0264 \| 228.0 \| 2850 \| 0.9402 \| 0.775 \| 0.3923 \| 1.5329 \| 0.775 \| 0.7418 \| 0.2161 \| 0.1076 \| \| 0.0264 \| 228.96 \| 2862 \| 0.9420 \| 0.775 \| 0.3932 \| 1.5301 \| 0.775 \| 0.7418 \| 0.2078 \| 0.1074 \| \| 0.0264 \| 230.0 \| 2875 \| 0.9412 \| 0.775 \| 0.3925 \| 1.5315 \| 0.775 \| 0.7418 \| 0.2078 \| 0.1076 \| \| 0.0264 \| 230.96 \| 2887 \| 0.9422 \| 0.775 \| 0.3930 \| 1.5340 \| 0.775 \| 0.7418 \| 0.2179 \| 0.1077 \| \| 0.0264 \| 232.0 \| 2900 \| 0.9431 \| 0.775 \| 0.3933 \| 1.5336 \| 0.775 \| 0.7418 \| 0.2158 \| 0.1081 \| \| 0.0264 \| 232.96 \| 2912 \| 0.9428 \| 0.775 \| 0.3931 \| 1.5304 \| 0.775 \| 0.7418 \| 0.2086 \| 0.1075 \| \| 0.0264 \| 234.0 \| 2925 \| 0.9434 \| 0.775 \| 0.3935 \| 1.5325 \| 0.775 \| 0.7418 \| 0.2152 \| 0.1074 \| \| 0.0264 \| 234.96 \| 2937 \| 0.9431 \| 0.775 \| 0.3933 \| 1.5286 \| 0.775 \| 0.7418 \| 0.2081 \| 0.1070 \| \| 0.0264 \| 236.0 \| 2950 \| 0.9438 \| 0.775 \| 0.3935 \| 1.5307 \| 0.775 \| 0.7418 \| 0.2077 \| 0.1073 \| \| 0.0264 \| 236.96 \| 2962 \| 0.9452 \| 0.775 \| 0.3940 \| 1.5329 \| 0.775 \| 0.7418 \| 0.2217 \| 0.1074 \| \| 0.0264 \| 238.0 \| 2975 \| 0.9453 \| 0.775 \| 0.3939 \| 1.5328 \| 0.775 \| 0.7418 \| 0.2129 \| 0.1076 \| \| 0.0264 \| 238.96 \| 2987 \| 0.9451 \| 0.775 \| 0.3937 \| 1.5308 \| 0.775 \| 0.7418 \| 0.2133 \| 0.1073 \| \| 0.0223 \| 240.0 \| 3000 \| 0.9470 \| 0.775 \| 0.3947 \| 1.5333 \| 0.775 \| 0.7418 \| 0.2220 \| 0.1077 \| \| 0.0223 \| 240.96 \| 3012 \| 0.9461 \| 0.775 \| 0.3942 \| 1.5329 \| 0.775 \| 0.7418 \| 0.2127 \| 0.1072 \| \| 0.0223 \| 242.0 \| 3025 \| 0.9477 \| 0.775 \| 0.3949 \| 1.5310 \| 0.775 \| 0.7418 \| 0.2133 \| 0.1074 \| \| 0.0223 \| 242.96 \| 3037 \| 0.9480 \| 0.775 \| 0.3949 \| 1.5331 \| 0.775 \| 0.7418 \| 0.2165 \| 0.1073 \| \| 0.0223 \| 244.0 \| 3050 \| 0.9499 \| 0.775 \| 0.3955 \| 1.5384 \| 0.775 \| 0.7418 \| 0.2226 \| 0.1080 \| \| 0.0223 \| 244.96 \| 3062 \| 0.9476 \| 0.775 \| 0.3946 \| 1.5322 \| 0.775 \| 0.7418 \| 0.2128 \| 0.1069 \| \| 0.0223 \| 246.0 \| 3075 \| 0.9490 \| 0.775 \| 0.3953 \| 1.5298 \| 0.775 \| 0.7418 \| 0.2137 \| 0.1071 \| \| 0.0223 \| 246.96 \| 3087 \| 0.9496 \| 0.775 \| 0.3953 \| 1.5315 \| 0.775 \| 0.7418 \| 0.2133 \| 0.1071 \| \| 0.0223 \| 248.0 \| 3100 \| 0.9500 \| 0.775 \| 0.3955 \| 1.5335 \| 0.775 \| 0.7418 \| 0.2131 \| 0.1072 \| \| 0.0223 \| 248.96 \| 3112 \| 0.9503 \| 0.775 \| 0.3956 \| 1.5323 \| 0.775 \| 0.7418 \| 0.2164 \| 0.1072 \| \| 0.0223 \| 250.0 \| 3125 \| 0.9505 \| 0.775 \| 0.3955 \| 1.5338 \| 0.775 \| 0.7418 \| 0.2128 \| 0.1071 \| \| 0.0223 \| 250.96 \| 3137 \| 0.9510 \| 0.775 \| 0.3957 \| 1.5372 \| 0.775 \| 0.7418 \| 0.2266 \| 0.1072 \| \| 0.0223 \| 252.0 \| 3150 \| 0.9517 \| 0.775 \| 0.3960 \| 1.5363 \| 0.775 \| 0.7418 \| 0.2222 \| 0.1073 \| \| 0.0223 \| 252.96 \| 3162 \| 0.9526 \| 0.775 \| 0.3961 \| 1.5372 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1080 \| \| 0.0223 \| 254.0 \| 3175 \| 0.9527 \| 0.77 \| 0.3963 \| 1.5340 \| 0.7700 \| 0.7368 \| 0.2174 \| 0.1081 \| \| 0.0223 \| 254.96 \| 3187 \| 0.9527 \| 0.775 \| 0.3962 \| 1.5389 \| 0.775 \| 0.7418 \| 0.2222 \| 0.1074 \| \| 0.0223 \| 256.0 \| 3200 \| 0.9528 \| 0.775 \| 0.3962 \| 1.5347 \| 0.775 \| 0.7418 \| 0.2258 \| 0.1073 \| \| 0.0223 \| 256.96 \| 3212 \| 0.9545 \| 0.775 \| 0.3969 \| 1.5401 \| 0.775 \| 0.7418 \| 0.2226 \| 0.1083 \| \| 0.0223 \| 258.0 \| 3225 \| 0.9540 \| 0.775 \| 0.3966 \| 1.5369 \| 0.775 \| 0.7418 \| 0.2224 \| 0.1074 \| \| 0.0223 \| 258.96 \| 3237 \| 0.9547 \| 0.775 \| 0.3969 \| 1.5370 \| 0.775 \| 0.7418 \| 0.2228 \| 0.1082 \| \| 0.0223 \| 260.0 \| 3250 \| 0.9549 \| 0.775 \| 0.3969 \| 1.5381 \| 0.775 \| 0.7418 \| 0.2226 \| 0.1075 \| \| 0.0223 \| 260.96 \| 3262 \| 0.9545 \| 0.775 \| 0.3968 \| 1.5345 \| 0.775 \| 0.7418 \| 0.2134 \| 0.1072 \| \| 0.0223 \| 262.0 \| 3275 \| 0.9550 \| 0.775 \| 0.3970 \| 1.5362 \| 0.775 \| 0.7418 \| 0.2145 \| 0.1079 \| \| 0.0223 \| 262.96 \| 3287 \| 0.9558 \| 0.775 \| 0.3971 \| 1.5392 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1076 \| \| 0.0223 \| 264.0 \| 3300 \| 0.9557 \| 0.775 \| 0.3970 \| 1.5383 \| 0.775 \| 0.7418 \| 0.2226 \| 0.1074 \| \| 0.0223 \| 264.96 \| 3312 \| 0.9561 \| 0.775 \| 0.3973 \| 1.5393 \| 0.775 \| 0.7418 \| 0.2224 \| 0.1080 \| \| 0.0223 \| 266.0 \| 3325 \| 0.9563 \| 0.775 \| 0.3972 \| 1.5387 \| 0.775 \| 0.7418 \| 0.2224 \| 0.1073 \| \| 0.0223 \| 266.96 \| 3337 \| 0.9568 \| 0.775 \| 0.3974 \| 1.5407 \| 0.775 \| 0.7418 \| 0.2225 \| 0.1082 \| \| 0.0223 \| 268.0 \| 3350 \| 0.9567 \| 0.775 \| 0.3973 \| 1.5373 \| 0.775 \| 0.7418 \| 0.2259 \| 0.1080 \| \| 0.0223 \| 268.96 \| 3362 \| 0.9566 \| 0.775 \| 0.3973 \| 1.5371 \| 0.775 \| 0.7418 \| 0.2225 \| 0.1080 \| \| 0.0223 \| 270.0 \| 3375 \| 0.9574 \| 0.775 \| 0.3976 \| 1.5403 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1075 \| \| 0.0223 \| 270.96 \| 3387 \| 0.9568 \| 0.775 \| 0.3974 \| 1.5363 \| 0.775 \| 0.7418 \| 0.2225 \| 0.1072 \| \| 0.0223 \| 272.0 \| 3400 \| 0.9580 \| 0.775 \| 0.3978 \| 1.5465 \| 0.775 \| 0.7418 \| 0.2241 \| 0.1081 \| \| 0.0223 \| 272.96 \| 3412 \| 0.9577 \| 0.775 \| 0.3977 \| 1.5383 \| 0.775 \| 0.7418 \| 0.2228 \| 0.1074 \| \| 0.0223 \| 274.0 \| 3425 \| 0.9577 \| 0.775 \| 0.3976 \| 1.5409 \| 0.775 \| 0.7418 \| 0.2225 \| 0.1080 \| \| 0.0223 \| 274.96 \| 3437 \| 0.9582 \| 0.775 \| 0.3978 \| 1.5409 \| 0.775 \| 0.7418 \| 0.2226 \| 0.1075 \| \| 0.0223 \| 276.0 \| 3450 \| 0.9581 \| 0.775 \| 0.3978 \| 1.5412 \| 0.775 \| 0.7418 \| 0.2225 \| 0.1082 \| \| 0.0223 \| 276.96 \| 3462 \| 0.9582 \| 0.775 \| 0.3978 \| 1.5367 \| 0.775 \| 0.7418 \| 0.2220 \| 0.1073 \| \| 0.0223 \| 278.0 \| 3475 \| 0.9587 \| 0.775 \| 0.3980 \| 1.5422 \| 0.775 \| 0.7418 \| 0.2244 \| 0.1082 \| \| 0.0223 \| 278.96 \| 3487 \| 0.9588 \| 0.775 \| 0.3980 \| 1.5478 \| 0.775 \| 0.7418 \| 0.2242 \| 0.1082 \| \| 0.0202 \| 280.0 \| 3500 \| 0.9586 \| 0.775 \| 0.3980 \| 1.5381 \| 0.775 \| 0.7418 \| 0.2219 \| 0.1081 \| \| 0.0202 \| 280.96 \| 3512 \| 0.9592 \| 0.775 \| 0.3981 \| 1.5474 \| 0.775 \| 0.7418 \| 0.2243 \| 0.1082 \| \| 0.0202 \| 282.0 \| 3525 \| 0.9588 \| 0.775 \| 0.3980 \| 1.5396 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1080 \| \| 0.0202 \| 282.96 \| 3537 \| 0.9589 \| 0.775 \| 0.3980 \| 1.5401 \| 0.775 \| 0.7418 \| 0.2218 \| 0.1074 \| \| 0.0202 \| 284.0 \| 3550 \| 0.9593 \| 0.775 \| 0.3982 \| 1.5441 \| 0.775 \| 0.7418 \| 0.2243 \| 0.1083 \| \| 0.0202 \| 284.96 \| 3562 \| 0.9591 \| 0.775 \| 0.3981 \| 1.5412 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1082 \| \| 0.0202 \| 286.0 \| 3575 \| 0.9592 \| 0.775 \| 0.3981 \| 1.5417 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1082 \| \| 0.0202 \| 286.96 \| 3587 \| 0.9592 \| 0.775 \| 0.3981 \| 1.5416 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1082 \| \| 0.0202 \| 288.0 \| 3600 \| 0.9592 \| 0.775 \| 0.3981 \| 1.5416 \| 0.775 \| 0.7418 \| 0.2227 \| 0.1082 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/vit-tiny_tobacco3482_dualsimkd_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_tobacco3482_dualsimkd_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1401 - Accuracy: 0.385 - Brier Loss: 0.8709 - Nll: 8.8462 - F1 Micro: 0.3850 - F1 Macro: 0.1979 - Ece: 0.3606 - Aurc: 0.3874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 100 \| 0.5117 \| 0.04 \| 0.9009 \| 19.1664 \| 0.04 \| 0.0077 \| 0.1344 \| 0.9445 \| \| No log \| 2.0 \| 200 \| 0.3168 \| 0.05 \| 0.8997 \| 15.0313 \| 0.0500 \| 0.0095 \| 0.1344 \| 0.8364 \| \| No log \| 3.0 \| 300 \| 0.2703 \| 0.18 \| 0.8978 \| 9.6860 \| 0.18 \| 0.0305 \| 0.2180 \| 0.7731 \| \| No log \| 4.0 \| 400 \| 0.2266 \| 0.18 \| 0.8952 \| 12.0957 \| 0.18 \| 0.0305 \| 0.2223 \| 0.7993 \| \| 1.1219 \| 5.0 \| 500 \| 0.1687 \| 0.18 \| 0.8951 \| 12.7136 \| 0.18 \| 0.0305 \| 0.2215 \| 0.7713 \| \| 1.1219 \| 6.0 \| 600 \| 0.1331 \| 0.165 \| 0.8956 \| 12.6737 \| 0.165 \| 0.0284 \| 0.2044 \| 0.7829 \| \| 1.1219 \| 7.0 \| 700 \| 0.1139 \| 0.18 \| 0.8960 \| 12.6380 \| 0.18 \| 0.0305 \| 0.2283 \| 0.7875 \| \| 1.1219 \| 8.0 \| 800 \| 0.1143 \| 0.18 \| 0.8963 \| 12.6385 \| 0.18 \| 0.0306 \| 0.2183 \| 0.7703 \| \| 1.1219 \| 9.0 \| 900 \| 0.1246 \| 0.18 \| 0.8966 \| 12.5389 \| 0.18 \| 0.0305 \| 0.2223 \| 0.7726 \| \| 0.0694 \| 10.0 \| 1000 \| 0.1262 \| 0.18 \| 0.8961 \| 12.6316 \| 0.18 \| 0.0305 \| 0.2271 \| 0.7894 \| \| 0.0694 \| 11.0 \| 1100 \| 0.1186 \| 0.155 \| 0.8961 \| 12.6309 \| 0.155 \| 0.0268 \| 0.2169 \| 0.6418 \| \| 0.0694 \| 12.0 \| 1200 \| 0.1290 \| 0.18 \| 0.8960 \| 12.6360 \| 0.18 \| 0.0305 \| 0.2272 \| 0.8014 \| \| 0.0694 \| 13.0 \| 1300 \| 0.1202 \| 0.18 \| 0.8959 \| 12.6644 \| 0.18 \| 0.0305 \| 0.2274 \| 0.7910 \| \| 0.0694 \| 14.0 \| 1400 \| 0.1341 \| 0.18 \| 0.8960 \| 12.6667 \| 0.18 \| 0.0305 \| 0.2273 \| 0.7916 \| \| 0.0505 \| 15.0 \| 1500 \| 0.1234 \| 0.18 \| 0.8961 \| 12.6653 \| 0.18 \| 0.0305 \| 0.2261 \| 0.7819 \| \| 0.0505 \| 16.0 \| 1600 \| 0.1375 \| 0.18 \| 0.8960 \| 12.6951 \| 0.18 \| 0.0305 \| 0.2283 \| 0.7929 \| \| 0.0505 \| 17.0 \| 1700 \| 0.1249 \| 0.18 \| 0.8959 \| 12.7041 \| 0.18 \| 0.0305 \| 0.2262 \| 0.7820 \| \| 0.0505 \| 18.0 \| 1800 \| 0.1263 \| 0.18 \| 0.8964 \| 12.6096 \| 0.18 \| 0.0305 \| 0.2228 \| 0.7900 \| \| 0.0505 \| 19.0 \| 1900 \| 0.1243 \| 0.18 \| 0.8961 \| 12.6667 \| 0.18 \| 0.0305 \| 0.2229 \| 0.7896 \| \| 0.0483 \| 20.0 \| 2000 \| 0.1246 \| 0.18 \| 0.8960 \| 12.6285 \| 0.18 \| 0.0305 \| 0.2172 \| 0.7913 \| \| 0.0483 \| 21.0 \| 2100 \| 0.1218 \| 0.18 \| 0.8961 \| 12.6375 \| 0.18 \| 0.0305 \| 0.2250 \| 0.8003 \| \| 0.0483 \| 22.0 \| 2200 \| 0.1228 \| 0.18 \| 0.8964 \| 12.5765 \| 0.18 \| 0.0305 \| 0.2258 \| 0.7938 \| \| 0.0483 \| 23.0 \| 2300 \| 0.1270 \| 0.18 \| 0.8963 \| 12.6332 \| 0.18 \| 0.0305 \| 0.2239 \| 0.8055 \| \| 0.0483 \| 24.0 \| 2400 \| 0.1303 \| 0.18 \| 0.8963 \| 12.5914 \| 0.18 \| 0.0305 \| 0.2270 \| 0.8006 \| \| 0.0484 \| 25.0 \| 2500 \| 0.1234 \| 0.18 \| 0.8960 \| 12.6429 \| 0.18 \| 0.0305 \| 0.2208 \| 0.7990 \| \| 0.0484 \| 26.0 \| 2600 \| 0.1313 \| 0.18 \| 0.8965 \| 12.5721 \| 0.18 \| 0.0305 \| 0.2205 \| 0.8069 \| \| 0.0484 \| 27.0 \| 2700 \| 0.1314 \| 0.18 \| 0.8963 \| 12.5982 \| 0.18 \| 0.0305 \| 0.2247 \| 0.8110 \| \| 0.0484 \| 28.0 \| 2800 \| 0.1326 \| 0.18 \| 0.8962 \| 12.6539 \| 0.18 \| 0.0305 \| 0.2143 \| 0.8083 \| \| 0.0484 \| 29.0 \| 2900 \| 0.1337 \| 0.18 \| 0.8964 \| 12.5814 \| 0.18 \| 0.0305 \| 0.2225 \| 0.8106 \| \| 0.0473 \| 30.0 \| 3000 \| 0.1369 \| 0.18 \| 0.8962 \| 12.6021 \| 0.18 \| 0.0305 \| 0.2258 \| 0.8095 \| \| 0.0473 \| 31.0 \| 3100 \| 0.1295 \| 0.18 \| 0.8958 \| 12.6587 \| 0.18 \| 0.0305 \| 0.2273 \| 0.8104 \| \| 0.0473 \| 32.0 \| 3200 \| 0.1343 \| 0.18 \| 0.8959 \| 12.6740 \| 0.18 \| 0.0305 \| 0.2220 \| 0.8119 \| \| 0.0473 \| 33.0 \| 3300 \| 0.1359 \| 0.18 \| 0.8960 \| 12.6790 \| 0.18 \| 0.0305 \| 0.2273 \| 0.8134 \| \| 0.0473 \| 34.0 \| 3400 \| 0.1367 \| 0.18 \| 0.8961 \| 12.6336 \| 0.18 \| 0.0305 \| 0.2228 \| 0.8159 \| \| 0.0476 \| 35.0 \| 3500 \| 0.1378 \| 0.18 \| 0.8963 \| 12.6119 \| 0.18 \| 0.0305 \| 0.2270 \| 0.8172 \| \| 0.0476 \| 36.0 \| 3600 \| 0.1286 \| 0.18 \| 0.8961 \| 12.6340 \| 0.18 \| 0.0305 \| 0.2218 \| 0.8148 \| \| 0.0476 \| 37.0 \| 3700 \| 0.1333 \| 0.18 \| 0.8960 \| 12.6328 \| 0.18 \| 0.0305 \| 0.2207 \| 0.8164 \| \| 0.0476 \| 38.0 \| 3800 \| 0.1328 \| 0.18 \| 0.8963 \| 12.6294 \| 0.18 \| 0.0305 \| 0.2196 \| 0.8180 \| \| 0.0476 \| 39.0 \| 3900 \| 0.1344 \| 0.18 \| 0.8961 \| 12.6417 \| 0.18 \| 0.0305 \| 0.2207 \| 0.8209 \| \| 0.0474 \| 40.0 \| 4000 \| 0.1362 \| 0.18 \| 0.8959 \| 12.6775 \| 0.18 \| 0.0305 \| 0.2187 \| 0.8198 \| \| 0.0474 \| 41.0 \| 4100 \| 0.1340 \| 0.18 \| 0.8961 \| 12.6746 \| 0.18 \| 0.0305 \| 0.2249 \| 0.8215 \| \| 0.0474 \| 42.0 \| 4200 \| 0.1308 \| 0.18 \| 0.8958 \| 12.6621 \| 0.18 \| 0.0305 \| 0.2208 \| 0.8215 \| \| 0.0474 \| 43.0 \| 4300 \| 0.1372 \| 0.18 \| 0.8960 \| 12.6133 \| 0.18 \| 0.0305 \| 0.2249 \| 0.8204 \| \| 0.0474 \| 44.0 \| 4400 \| 0.1436 \| 0.18 \| 0.8963 \| 12.6014 \| 0.18 \| 0.0305 \| 0.2280 \| 0.8201 \| \| 0.0472 \| 45.0 \| 4500 \| 0.1374 \| 0.18 \| 0.8960 \| 12.6316 \| 0.18 \| 0.0305 \| 0.2228 \| 0.8193 \| \| 0.0472 \| 46.0 \| 4600 \| 0.1261 \| 0.18 \| 0.8957 \| 12.6840 \| 0.18 \| 0.0305 \| 0.2251 \| 0.8220 \| \| 0.0472 \| 47.0 \| 4700 \| 0.1340 \| 0.18 \| 0.8956 \| 12.6704 \| 0.18 \| 0.0305 \| 0.2251 \| 0.8221 \| \| 0.0472 \| 48.0 \| 4800 \| 0.1320 \| 0.18 \| 0.8959 \| 12.6111 \| 0.18 \| 0.0305 \| 0.2227 \| 0.8203 \| \| 0.0472 \| 49.0 \| 4900 \| 0.1336 \| 0.18 \| 0.8956 \| 12.6838 \| 0.18 \| 0.0305 \| 0.2294 \| 0.8209 \| \| 0.0474 \| 50.0 \| 5000 \| 0.1342 \| 0.18 \| 0.8959 \| 12.3426 \| 0.18 \| 0.0305 \| 0.2292 \| 0.8218 \| \| 0.0474 \| 51.0 \| 5100 \| 0.1362 \| 0.18 \| 0.8957 \| 12.3611 \| 0.18 \| 0.0305 \| 0.2261 \| 0.8224 \| \| 0.0474 \| 52.0 \| 5200 \| 0.1368 \| 0.18 \| 0.8958 \| 11.5617 \| 0.18 \| 0.0305 \| 0.2205 \| 0.8222 \| \| 0.0474 \| 53.0 \| 5300 \| 0.1391 \| 0.18 \| 0.8955 \| 11.5519 \| 0.18 \| 0.0305 \| 0.2312 \| 0.8225 \| \| 0.0474 \| 54.0 \| 5400 \| 0.1366 \| 0.18 \| 0.8947 \| 12.2068 \| 0.18 \| 0.0305 \| 0.2231 \| 0.8231 \| \| 0.047 \| 55.0 \| 5500 \| 0.1355 \| 0.19 \| 0.8943 \| 11.5922 \| 0.19 \| 0.0641 \| 0.2299 \| 0.8248 \| \| 0.047 \| 56.0 \| 5600 \| 0.1386 \| 0.17 \| 0.8930 \| 11.8204 \| 0.17 \| 0.0705 \| 0.2240 \| 0.5968 \| \| 0.047 \| 57.0 \| 5700 \| 0.1364 \| 0.33 \| 0.8936 \| 11.0092 \| 0.33 \| 0.1878 \| 0.3195 \| 0.4381 \| \| 0.047 \| 58.0 \| 5800 \| 0.1368 \| 0.27 \| 0.8923 \| 11.0463 \| 0.27 \| 0.1541 \| 0.2874 \| 0.5187 \| \| 0.047 \| 59.0 \| 5900 \| 0.1328 \| 0.325 \| 0.8915 \| 10.5269 \| 0.325 \| 0.1702 \| 0.3247 \| 0.4469 \| \| 0.0469 \| 60.0 \| 6000 \| 0.1402 \| 0.235 \| 0.8945 \| 9.2940 \| 0.235 \| 0.1141 \| 0.2558 \| 0.6612 \| \| 0.0469 \| 61.0 \| 6100 \| 0.1387 \| 0.345 \| 0.8913 \| 9.2678 \| 0.345 \| 0.1657 \| 0.3422 \| 0.4100 \| \| 0.0469 \| 62.0 \| 6200 \| 0.1386 \| 0.31 \| 0.8891 \| 10.1100 \| 0.31 \| 0.1637 \| 0.3134 \| 0.4609 \| \| 0.0469 \| 63.0 \| 6300 \| 0.1379 \| 0.34 \| 0.8892 \| 9.1965 \| 0.34 \| 0.1582 \| 0.3388 \| 0.4344 \| \| 0.0469 \| 64.0 \| 6400 \| 0.1375 \| 0.335 \| 0.8876 \| 9.2252 \| 0.335 \| 0.1624 \| 0.3356 \| 0.4239 \| \| 0.0469 \| 65.0 \| 6500 \| 0.1357 \| 0.345 \| 0.8868 \| 9.1887 \| 0.345 \| 0.1659 \| 0.3361 \| 0.4061 \| \| 0.0469 \| 66.0 \| 6600 \| 0.1394 \| 0.345 \| 0.8850 \| 9.1819 \| 0.345 \| 0.1641 \| 0.3398 \| 0.4265 \| \| 0.0469 \| 67.0 \| 6700 \| 0.1410 \| 0.34 \| 0.8850 \| 9.1158 \| 0.34 \| 0.1590 \| 0.3328 \| 0.4302 \| \| 0.0469 \| 68.0 \| 6800 \| 0.1387 \| 0.295 \| 0.8814 \| 9.2693 \| 0.295 \| 0.1374 \| 0.3039 \| 0.4572 \| \| 0.0469 \| 69.0 \| 6900 \| 0.1385 \| 0.335 \| 0.8814 \| 9.1526 \| 0.335 \| 0.1668 \| 0.3324 \| 0.4205 \| \| 0.0463 \| 70.0 \| 7000 \| 0.1392 \| 0.34 \| 0.8814 \| 9.1159 \| 0.34 \| 0.1546 \| 0.3405 \| 0.4263 \| \| 0.0463 \| 71.0 \| 7100 \| 0.1418 \| 0.35 \| 0.8820 \| 9.1363 \| 0.35 \| 0.1692 \| 0.3436 \| 0.4019 \| \| 0.0463 \| 72.0 \| 7200 \| 0.1379 \| 0.35 \| 0.8791 \| 9.0483 \| 0.35 \| 0.1726 \| 0.3402 \| 0.4226 \| \| 0.0463 \| 73.0 \| 7300 \| 0.1405 \| 0.33 \| 0.8760 \| 9.3563 \| 0.33 \| 0.1731 \| 0.3207 \| 0.4307 \| \| 0.0463 \| 74.0 \| 7400 \| 0.1401 \| 0.31 \| 0.8769 \| 9.4413 \| 0.31 \| 0.1676 \| 0.3099 \| 0.4383 \| \| 0.0458 \| 75.0 \| 7500 \| 0.1393 \| 0.38 \| 0.8778 \| 9.0788 \| 0.38 \| 0.1985 \| 0.3518 \| 0.3976 \| \| 0.0458 \| 76.0 \| 7600 \| 0.1384 \| 0.39 \| 0.8779 \| 9.0233 \| 0.39 \| 0.2027 \| 0.3673 \| 0.4144 \| \| 0.0458 \| 77.0 \| 7700 \| 0.1403 \| 0.365 \| 0.8818 \| 9.1567 \| 0.3650 \| 0.1953 \| 0.3518 \| 0.4181 \| \| 0.0458 \| 78.0 \| 7800 \| 0.1400 \| 0.27 \| 0.8725 \| 11.0592 \| 0.27 \| 0.1627 \| 0.2896 \| 0.4809 \| \| 0.0458 \| 79.0 \| 7900 \| 0.1402 \| 0.375 \| 0.8739 \| 9.1158 \| 0.375 \| 0.1961 \| 0.3540 \| 0.3929 \| \| 0.0455 \| 80.0 \| 8000 \| 0.1401 \| 0.315 \| 0.8722 \| 9.9114 \| 0.315 \| 0.1771 \| 0.3220 \| 0.4443 \| \| 0.0455 \| 81.0 \| 8100 \| 0.1378 \| 0.39 \| 0.8761 \| 9.0128 \| 0.39 \| 0.2048 \| 0.3642 \| 0.4020 \| \| 0.0455 \| 82.0 \| 8200 \| 0.1401 \| 0.38 \| 0.8729 \| 9.1624 \| 0.38 \| 0.2006 \| 0.3612 \| 0.3924 \| \| 0.0455 \| 83.0 \| 8300 \| 0.1391 \| 0.38 \| 0.8742 \| 8.8982 \| 0.38 \| 0.2048 \| 0.3561 \| 0.3991 \| \| 0.0455 \| 84.0 \| 8400 \| 0.1381 \| 0.375 \| 0.8734 \| 9.0598 \| 0.375 \| 0.1901 \| 0.3567 \| 0.4010 \| \| 0.0453 \| 85.0 \| 8500 \| 0.1398 \| 0.39 \| 0.8718 \| 9.1407 \| 0.39 \| 0.2057 \| 0.3693 \| 0.3892 \| \| 0.0453 \| 86.0 \| 8600 \| 0.1389 \| 0.37 \| 0.8721 \| 9.3494 \| 0.37 \| 0.2006 \| 0.3505 \| 0.3914 \| \| 0.0453 \| 87.0 \| 8700 \| 0.1390 \| 0.395 \| 0.8743 \| 8.7444 \| 0.395 \| 0.2113 \| 0.3724 \| 0.3854 \| \| 0.0453 \| 88.0 \| 8800 \| 0.1404 \| 0.395 \| 0.8739 \| 8.7654 \| 0.395 \| 0.2134 \| 0.3657 \| 0.3925 \| \| 0.0453 \| 89.0 \| 8900 \| 0.1409 \| 0.385 \| 0.8726 \| 8.7763 \| 0.3850 \| 0.2032 \| 0.3643 \| 0.3963 \| \| 0.0451 \| 90.0 \| 9000 \| 0.1403 \| 0.39 \| 0.8717 \| 8.8363 \| 0.39 \| 0.2055 \| 0.3668 \| 0.3926 \| \| 0.0451 \| 91.0 \| 9100 \| 0.1388 \| 0.39 \| 0.8719 \| 9.2985 \| 0.39 \| 0.2099 \| 0.3662 \| 0.3847 \| \| 0.0451 \| 92.0 \| 9200 \| 0.1397 \| 0.385 \| 0.8702 \| 9.4449 \| 0.3850 \| 0.2050 \| 0.3535 \| 0.3877 \| \| 0.0451 \| 93.0 \| 9300 \| 0.1403 \| 0.385 \| 0.8709 \| 8.9790 \| 0.3850 \| 0.1989 \| 0.3473 \| 0.3887 \| \| 0.0451 \| 94.0 \| 9400 \| 0.1400 \| 0.39 \| 0.8705 \| 9.1647 \| 0.39 \| 0.2053 \| 0.3569 \| 0.3865 \| \| 0.045 \| 95.0 \| 9500 \| 0.1404 \| 0.395 \| 0.8712 \| 9.1707 \| 0.395 \| 0.2087 \| 0.3688 \| 0.3815 \| \| 0.045 \| 96.0 \| 9600 \| 0.1404 \| 0.385 \| 0.8711 \| 8.6711 \| 0.3850 \| 0.1980 \| 0.3566 \| 0.3867 \| \| 0.045 \| 97.0 \| 9700 \| 0.1399 \| 0.39 \| 0.8706 \| 9.1288 \| 0.39 \| 0.2035 \| 0.3610 \| 0.3845 \| \| 0.045 \| 98.0 \| 9800 \| 0.1400 \| 0.385 \| 0.8708 \| 9.1302 \| 0.3850 \| 0.1982 \| 0.3538 \| 0.3870 \| \| 0.045 \| 99.0 \| 9900 \| 0.1398 \| 0.39 \| 0.8712 \| 8.8257 \| 0.39 \| 0.2002 \| 0.3660 \| 0.3825 \| \| 0.0449 \| 100.0 \| 10000 \| 0.1401 \| 0.385 \| 0.8709 \| 8.8462 \| 0.3850 \| 0.1979 \| 0.3606 \| 0.3874 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4277 - Accuracy: 0.835 - Brier Loss: 0.2653 - Nll: 1.5700 - F1 Micro: 0.835 - F1 Macro: 0.8164 - Ece: 0.1805 - Aurc: 0.0632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.6826 \| 0.23 \| 0.8622 \| 4.7953 \| 0.23 \| 0.1892 \| 0.2929 \| 0.7651 \| \| No log \| 2.0 \| 50 \| 1.0374 \| 0.53 \| 0.6004 \| 2.7646 \| 0.53 \| 0.4280 \| 0.2624 \| 0.2619 \| \| No log \| 3.0 \| 75 \| 0.8158 \| 0.665 \| 0.4678 \| 2.4034 \| 0.665 \| 0.5565 \| 0.2488 \| 0.1416 \| \| No log \| 4.0 \| 100 \| 0.6879 \| 0.72 \| 0.3838 \| 1.5355 \| 0.72 \| 0.6873 \| 0.2297 \| 0.1064 \| \| No log \| 5.0 \| 125 \| 0.6511 \| 0.775 \| 0.3538 \| 1.5183 \| 0.775 \| 0.7285 \| 0.2235 \| 0.0915 \| \| No log \| 6.0 \| 150 \| 0.7310 \| 0.755 \| 0.3579 \| 1.3899 \| 0.755 \| 0.7257 \| 0.2190 \| 0.0926 \| \| No log \| 7.0 \| 175 \| 0.5698 \| 0.795 \| 0.3246 \| 1.3920 \| 0.795 \| 0.7691 \| 0.2251 \| 0.0956 \| \| No log \| 8.0 \| 200 \| 0.5675 \| 0.805 \| 0.3064 \| 1.4278 \| 0.805 \| 0.7733 \| 0.2093 \| 0.0655 \| \| No log \| 9.0 \| 225 \| 0.5986 \| 0.8 \| 0.3356 \| 1.5317 \| 0.8000 \| 0.7890 \| 0.2249 \| 0.0913 \| \| No log \| 10.0 \| 250 \| 0.6158 \| 0.755 \| 0.3475 \| 1.5027 \| 0.755 \| 0.7340 \| 0.2152 \| 0.0782 \| \| No log \| 11.0 \| 275 \| 0.5353 \| 0.815 \| 0.3037 \| 1.6003 \| 0.815 \| 0.8143 \| 0.2305 \| 0.0749 \| \| No log \| 12.0 \| 300 \| 0.5460 \| 0.825 \| 0.3008 \| 1.7407 \| 0.825 \| 0.8070 \| 0.2378 \| 0.0856 \| \| No log \| 13.0 \| 325 \| 0.4905 \| 0.83 \| 0.2787 \| 1.1328 \| 0.83 \| 0.8099 \| 0.2344 \| 0.0481 \| \| No log \| 14.0 \| 350 \| 0.4913 \| 0.795 \| 0.2881 \| 1.2261 \| 0.795 \| 0.7521 \| 0.2121 \| 0.0661 \| \| No log \| 15.0 \| 375 \| 0.4773 \| 0.835 \| 0.2753 \| 1.2716 \| 0.835 \| 0.8140 \| 0.2125 \| 0.0636 \| \| No log \| 16.0 \| 400 \| 0.4848 \| 0.84 \| 0.2751 \| 1.5983 \| 0.8400 \| 0.8139 \| 0.2195 \| 0.0707 \| \| No log \| 17.0 \| 425 \| 0.4994 \| 0.805 \| 0.2886 \| 1.5637 \| 0.805 \| 0.7689 \| 0.2049 \| 0.0617 \| \| No log \| 18.0 \| 450 \| 0.4610 \| 0.835 \| 0.2871 \| 1.3906 \| 0.835 \| 0.8122 \| 0.2175 \| 0.0675 \| \| No log \| 19.0 \| 475 \| 0.4594 \| 0.84 \| 0.2669 \| 1.2217 \| 0.8400 \| 0.8214 \| 0.2022 \| 0.0516 \| \| 0.4534 \| 20.0 \| 500 \| 0.4793 \| 0.815 \| 0.2874 \| 1.4445 \| 0.815 \| 0.7965 \| 0.2024 \| 0.0641 \| \| 0.4534 \| 21.0 \| 525 \| 0.5185 \| 0.785 \| 0.3215 \| 1.8358 \| 0.785 \| 0.7743 \| 0.2250 \| 0.0850 \| \| 0.4534 \| 22.0 \| 550 \| 0.4339 \| 0.83 \| 0.2635 \| 1.2137 \| 0.83 \| 0.8200 \| 0.1944 \| 0.0610 \| \| 0.4534 \| 23.0 \| 575 \| 0.4640 \| 0.825 \| 0.2770 \| 1.4137 \| 0.825 \| 0.8086 \| 0.1800 \| 0.0674 \| \| 0.4534 \| 24.0 \| 600 \| 0.4528 \| 0.825 \| 0.2692 \| 1.3148 \| 0.825 \| 0.8077 \| 0.1912 \| 0.0678 \| \| 0.4534 \| 25.0 \| 625 \| 0.4361 \| 0.84 \| 0.2600 \| 1.4205 \| 0.8400 \| 0.8278 \| 0.2066 \| 0.0534 \| \| 0.4534 \| 26.0 \| 650 \| 0.4239 \| 0.835 \| 0.2590 \| 1.2112 \| 0.835 \| 0.8224 \| 0.1850 \| 0.0544 \| \| 0.4534 \| 27.0 \| 675 \| 0.4294 \| 0.82 \| 0.2636 \| 1.2671 \| 0.82 \| 0.8023 \| 0.1866 \| 0.0619 \| \| 0.4534 \| 28.0 \| 700 \| 0.4327 \| 0.84 \| 0.2633 \| 1.3084 \| 0.8400 \| 0.8283 \| 0.1954 \| 0.0628 \| \| 0.4534 \| 29.0 \| 725 \| 0.4309 \| 0.825 \| 0.2640 \| 1.4275 \| 0.825 \| 0.8022 \| 0.2117 \| 0.0667 \| \| 0.4534 \| 30.0 \| 750 \| 0.4299 \| 0.83 \| 0.2636 \| 1.3161 \| 0.83 \| 0.8103 \| 0.2110 \| 0.0620 \| \| 0.4534 \| 31.0 \| 775 \| 0.4345 \| 0.835 \| 0.2634 \| 1.4605 \| 0.835 \| 0.8269 \| 0.1998 \| 0.0562 \| \| 0.4534 \| 32.0 \| 800 \| 0.4404 \| 0.83 \| 0.2743 \| 1.3965 \| 0.83 \| 0.8077 \| 0.2198 \| 0.0669 \| \| 0.4534 \| 33.0 \| 825 \| 0.4254 \| 0.83 \| 0.2614 \| 1.3734 \| 0.83 \| 0.8133 \| 0.1990 \| 0.0567 \| \| 0.4534 \| 34.0 \| 850 \| 0.4271 \| 0.835 \| 0.2632 \| 1.3963 \| 0.835 \| 0.8164 \| 0.1932 \| 0.0649 \| \| 0.4534 \| 35.0 \| 875 \| 0.4284 \| 0.835 \| 0.2636 \| 1.3713 \| 0.835 \| 0.8164 \| 0.2127 \| 0.0634 \| \| 0.4534 \| 36.0 \| 900 \| 0.4262 \| 0.835 \| 0.2628 \| 1.4403 \| 0.835 \| 0.8164 \| 0.1926 \| 0.0649 \| \| 0.4534 \| 37.0 \| 925 \| 0.4253 \| 0.835 \| 0.2621 \| 1.3813 \| 0.835 \| 0.8164 \| 0.2015 \| 0.0628 \| \| 0.4534 \| 38.0 \| 950 \| 0.4262 \| 0.835 \| 0.2626 \| 1.4528 \| 0.835 \| 0.8164 \| 0.1971 \| 0.0628 \| \| 0.4534 \| 39.0 \| 975 \| 0.4271 \| 0.835 \| 0.2629 \| 1.4410 \| 0.835 \| 0.8164 \| 0.1933 \| 0.0627 \| \| 0.0663 \| 40.0 \| 1000 \| 0.4283 \| 0.835 \| 0.2639 \| 1.4647 \| 0.835 \| 0.8164 \| 0.1996 \| 0.0631 \| \| 0.0663 \| 41.0 \| 1025 \| 0.4272 \| 0.835 \| 0.2639 \| 1.4417 \| 0.835 \| 0.8164 \| 0.2088 \| 0.0630 \| \| 0.0663 \| 42.0 \| 1050 \| 0.4276 \| 0.835 \| 0.2640 \| 1.3976 \| 0.835 \| 0.8164 \| 0.1992 \| 0.0634 \| \| 0.0663 \| 43.0 \| 1075 \| 0.4270 \| 0.835 \| 0.2633 \| 1.4392 \| 0.835 \| 0.8164 \| 0.1892 \| 0.0628 \| \| 0.0663 \| 44.0 \| 1100 \| 0.4264 \| 0.835 \| 0.2635 \| 1.4429 \| 0.835 \| 0.8164 \| 0.1885 \| 0.0631 \| \| 0.0663 \| 45.0 \| 1125 \| 0.4269 \| 0.835 \| 0.2637 \| 1.4461 \| 0.835 \| 0.8164 \| 0.1974 \| 0.0629 \| \| 0.0663 \| 46.0 \| 1150 \| 0.4268 \| 0.835 \| 0.2636 \| 1.4415 \| 0.835 \| 0.8164 \| 0.1866 \| 0.0625 \| \| 0.0663 \| 47.0 \| 1175 \| 0.4269 \| 0.835 \| 0.2641 \| 1.4646 \| 0.835 \| 0.8164 \| 0.1812 \| 0.0636 \| \| 0.0663 \| 48.0 \| 1200 \| 0.4271 \| 0.835 \| 0.2639 \| 1.3990 \| 0.835 \| 0.8164 \| 0.1865 \| 0.0631 \| \| 0.0663 \| 49.0 \| 1225 \| 0.4267 \| 0.835 \| 0.2639 \| 1.4474 \| 0.835 \| 0.8164 \| 0.1946 \| 0.0629 \| \| 0.0663 \| 50.0 \| 1250 \| 0.4273 \| 0.835 \| 0.2642 \| 1.4492 \| 0.835 \| 0.8164 \| 0.1802 \| 0.0631 \| \| 0.0663 \| 51.0 \| 1275 \| 0.4272 \| 0.835 \| 0.2644 \| 1.4475 \| 0.835 \| 0.8164 \| 0.1942 \| 0.0630 \| \| 0.0663 \| 52.0 \| 1300 \| 0.4283 \| 0.835 \| 0.2648 \| 1.5157 \| 0.835 \| 0.8164 \| 0.1963 \| 0.0635 \| \| 0.0663 \| 53.0 \| 1325 \| 0.4271 \| 0.835 \| 0.2643 \| 1.5046 \| 0.835 \| 0.8164 \| 0.1955 \| 0.0633 \| \| 0.0663 \| 54.0 \| 1350 \| 0.4271 \| 0.835 \| 0.2642 \| 1.4629 \| 0.835 \| 0.8164 \| 0.1790 \| 0.0617 \| \| 0.0663 \| 55.0 \| 1375 \| 0.4278 \| 0.835 \| 0.2649 \| 1.5752 \| 0.835 \| 0.8164 \| 0.2007 \| 0.0635 \| \| 0.0663 \| 56.0 \| 1400 \| 0.4280 \| 0.835 \| 0.2648 \| 1.5165 \| 0.835 \| 0.8164 \| 0.1706 \| 0.0631 \| \| 0.0663 \| 57.0 \| 1425 \| 0.4275 \| 0.835 \| 0.2644 \| 1.5134 \| 0.835 \| 0.8164 \| 0.1864 \| 0.0629 \| \| 0.0663 \| 58.0 \| 1450 \| 0.4270 \| 0.835 \| 0.2643 \| 1.5088 \| 0.835 \| 0.8164 \| 0.1883 \| 0.0630 \| \| 0.0663 \| 59.0 \| 1475 \| 0.4273 \| 0.835 \| 0.2644 \| 1.5111 \| 0.835 \| 0.8164 \| 0.1951 \| 0.0630 \| \| 0.0615 \| 60.0 \| 1500 \| 0.4281 \| 0.835 \| 0.2651 \| 1.5727 \| 0.835 \| 0.8164 \| 0.2084 \| 0.0630 \| \| 0.0615 \| 61.0 \| 1525 \| 0.4271 \| 0.835 \| 0.2647 \| 1.5198 \| 0.835 \| 0.8164 \| 0.1957 \| 0.0631 \| \| 0.0615 \| 62.0 \| 1550 \| 0.4276 \| 0.835 \| 0.2649 \| 1.5139 \| 0.835 \| 0.8164 \| 0.1969 \| 0.0630 \| \| 0.0615 \| 63.0 \| 1575 \| 0.4269 \| 0.835 \| 0.2646 \| 1.4579 \| 0.835 \| 0.8164 \| 0.1802 \| 0.0629 \| \| 0.0615 \| 64.0 \| 1600 \| 0.4275 \| 0.835 \| 0.2648 \| 1.5144 \| 0.835 \| 0.8164 \| 0.2006 \| 0.0632 \| \| 0.0615 \| 65.0 \| 1625 \| 0.4276 \| 0.835 \| 0.2649 \| 1.5129 \| 0.835 \| 0.8164 \| 0.1846 \| 0.0632 \| \| 0.0615 \| 66.0 \| 1650 \| 0.4272 \| 0.835 \| 0.2647 \| 1.5165 \| 0.835 \| 0.8164 \| 0.1796 \| 0.0629 \| \| 0.0615 \| 67.0 \| 1675 \| 0.4273 \| 0.835 \| 0.2647 \| 1.5141 \| 0.835 \| 0.8164 \| 0.1882 \| 0.0631 \| \| 0.0615 \| 68.0 \| 1700 \| 0.4276 \| 0.835 \| 0.2649 \| 1.5146 \| 0.835 \| 0.8164 \| 0.1799 \| 0.0631 \| \| 0.0615 \| 69.0 \| 1725 \| 0.4275 \| 0.835 \| 0.2649 \| 1.5215 \| 0.835 \| 0.8164 \| 0.1799 \| 0.0631 \| \| 0.0615 \| 70.0 \| 1750 \| 0.4275 \| 0.835 \| 0.2647 \| 1.5124 \| 0.835 \| 0.8164 \| 0.1884 \| 0.0632 \| \| 0.0615 \| 71.0 \| 1775 \| 0.4278 \| 0.835 \| 0.2652 \| 1.5245 \| 0.835 \| 0.8164 \| 0.1800 \| 0.0631 \| \| 0.0615 \| 72.0 \| 1800 \| 0.4277 \| 0.835 \| 0.2650 \| 1.5169 \| 0.835 \| 0.8164 \| 0.1802 \| 0.0631 \| \| 0.0615 \| 73.0 \| 1825 \| 0.4277 \| 0.835 \| 0.2651 \| 1.5282 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0633 \| \| 0.0615 \| 74.0 \| 1850 \| 0.4273 \| 0.835 \| 0.2650 \| 1.5156 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0632 \| \| 0.0615 \| 75.0 \| 1875 \| 0.4278 \| 0.835 \| 0.2653 \| 1.5706 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0632 \| \| 0.0615 \| 76.0 \| 1900 \| 0.4275 \| 0.835 \| 0.2651 \| 1.5337 \| 0.835 \| 0.8164 \| 0.1807 \| 0.0633 \| \| 0.0615 \| 77.0 \| 1925 \| 0.4276 \| 0.835 \| 0.2652 \| 1.5357 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0633 \| \| 0.0615 \| 78.0 \| 1950 \| 0.4275 \| 0.835 \| 0.2651 \| 1.5701 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0633 \| \| 0.0615 \| 79.0 \| 1975 \| 0.4277 \| 0.835 \| 0.2651 \| 1.5161 \| 0.835 \| 0.8164 \| 0.1807 \| 0.0633 \| \| 0.0614 \| 80.0 \| 2000 \| 0.4278 \| 0.835 \| 0.2653 \| 1.5709 \| 0.835 \| 0.8164 \| 0.1808 \| 0.0632 \| \| 0.0614 \| 81.0 \| 2025 \| 0.4278 \| 0.835 \| 0.2653 \| 1.5703 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0632 \| \| 0.0614 \| 82.0 \| 2050 \| 0.4278 \| 0.835 \| 0.2653 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1806 \| 0.0633 \| \| 0.0614 \| 83.0 \| 2075 \| 0.4277 \| 0.835 \| 0.2652 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1803 \| 0.0631 \| \| 0.0614 \| 84.0 \| 2100 \| 0.4276 \| 0.835 \| 0.2652 \| 1.5694 \| 0.835 \| 0.8164 \| 0.1804 \| 0.0632 \| \| 0.0614 \| 85.0 \| 2125 \| 0.4275 \| 0.835 \| 0.2652 \| 1.5702 \| 0.835 \| 0.8164 \| 0.1807 \| 0.0633 \| \| 0.0614 \| 86.0 \| 2150 \| 0.4276 \| 0.835 \| 0.2652 \| 1.5699 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0633 \| \| 0.0614 \| 87.0 \| 2175 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5703 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0633 \| \| 0.0614 \| 88.0 \| 2200 \| 0.4277 \| 0.835 \| 0.2652 \| 1.5702 \| 0.835 \| 0.8164 \| 0.1882 \| 0.0632 \| \| 0.0614 \| 89.0 \| 2225 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5702 \| 0.835 \| 0.8164 \| 0.1806 \| 0.0633 \| \| 0.0614 \| 90.0 \| 2250 \| 0.4276 \| 0.835 \| 0.2653 \| 1.5696 \| 0.835 \| 0.8164 \| 0.1806 \| 0.0633 \| \| 0.0614 \| 91.0 \| 2275 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5698 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 92.0 \| 2300 \| 0.4276 \| 0.835 \| 0.2652 \| 1.5699 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 93.0 \| 2325 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 94.0 \| 2350 \| 0.4276 \| 0.835 \| 0.2653 \| 1.5698 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 95.0 \| 2375 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5699 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 96.0 \| 2400 \| 0.4276 \| 0.835 \| 0.2653 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 97.0 \| 2425 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5699 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 98.0 \| 2450 \| 0.4276 \| 0.835 \| 0.2653 \| 1.5699 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 99.0 \| 2475 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| \| 0.0614 \| 100.0 \| 2500 \| 0.4277 \| 0.835 \| 0.2653 \| 1.5700 \| 0.835 \| 0.8164 \| 0.1805 \| 0.0632 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4925 - Accuracy: 0.845 - Brier Loss: 0.2526 - Nll: 1.5547 - F1 Micro: 0.845 - F1 Macro: 0.8258 - Ece: 0.1785 - Aurc: 0.0736 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.8463 \| 0.245 \| 0.8631 \| 4.7256 \| 0.245 \| 0.2002 \| 0.2955 \| 0.7640 \| \| No log \| 2.0 \| 50 \| 1.1593 \| 0.535 \| 0.5972 \| 2.7208 \| 0.535 \| 0.4319 \| 0.2539 \| 0.2591 \| \| No log \| 3.0 \| 75 \| 0.9039 \| 0.67 \| 0.4555 \| 2.3747 \| 0.67 \| 0.5677 \| 0.2448 \| 0.1349 \| \| No log \| 4.0 \| 100 \| 0.7631 \| 0.73 \| 0.3757 \| 1.5518 \| 0.7300 \| 0.7026 \| 0.1947 \| 0.0987 \| \| No log \| 5.0 \| 125 \| 0.7412 \| 0.775 \| 0.3497 \| 1.4677 \| 0.775 \| 0.7456 \| 0.2239 \| 0.0892 \| \| No log \| 6.0 \| 150 \| 0.9198 \| 0.72 \| 0.3977 \| 1.7618 \| 0.72 \| 0.6958 \| 0.2190 \| 0.1118 \| \| No log \| 7.0 \| 175 \| 0.6117 \| 0.81 \| 0.2969 \| 1.2112 \| 0.81 \| 0.7726 \| 0.2244 \| 0.0661 \| \| No log \| 8.0 \| 200 \| 0.6296 \| 0.78 \| 0.3090 \| 1.3439 \| 0.78 \| 0.7443 \| 0.1959 \| 0.0771 \| \| No log \| 9.0 \| 225 \| 0.6850 \| 0.785 \| 0.3187 \| 1.6325 \| 0.785 \| 0.7651 \| 0.2194 \| 0.0986 \| \| No log \| 10.0 \| 250 \| 0.6304 \| 0.79 \| 0.3111 \| 1.3598 \| 0.79 \| 0.7821 \| 0.2106 \| 0.0838 \| \| No log \| 11.0 \| 275 \| 0.6668 \| 0.775 \| 0.3242 \| 1.9754 \| 0.775 \| 0.6942 \| 0.2005 \| 0.0947 \| \| No log \| 12.0 \| 300 \| 0.6795 \| 0.775 \| 0.3263 \| 1.6182 \| 0.775 \| 0.7692 \| 0.2155 \| 0.0875 \| \| No log \| 13.0 \| 325 \| 0.5156 \| 0.85 \| 0.2454 \| 0.9647 \| 0.85 \| 0.8378 \| 0.2033 \| 0.0515 \| \| No log \| 14.0 \| 350 \| 0.5341 \| 0.845 \| 0.2644 \| 1.0410 \| 0.845 \| 0.8402 \| 0.2050 \| 0.0503 \| \| No log \| 15.0 \| 375 \| 0.4678 \| 0.865 \| 0.2245 \| 0.9232 \| 0.865 \| 0.8564 \| 0.1836 \| 0.0363 \| \| No log \| 16.0 \| 400 \| 0.5620 \| 0.82 \| 0.2819 \| 1.1475 \| 0.82 \| 0.7980 \| 0.2050 \| 0.0710 \| \| No log \| 17.0 \| 425 \| 0.5253 \| 0.83 \| 0.2642 \| 0.8809 \| 0.83 \| 0.8145 \| 0.1811 \| 0.0723 \| \| No log \| 18.0 \| 450 \| 0.6295 \| 0.815 \| 0.2997 \| 1.8144 \| 0.815 \| 0.8062 \| 0.2120 \| 0.0636 \| \| No log \| 19.0 \| 475 \| 0.5748 \| 0.83 \| 0.2774 \| 1.7900 \| 0.83 \| 0.8200 \| 0.1920 \| 0.0506 \| \| 0.466 \| 20.0 \| 500 \| 0.4704 \| 0.84 \| 0.2275 \| 0.8869 \| 0.8400 \| 0.8135 \| 0.1882 \| 0.0472 \| \| 0.466 \| 21.0 \| 525 \| 0.5693 \| 0.82 \| 0.2820 \| 1.3315 \| 0.82 \| 0.8013 \| 0.2011 \| 0.0821 \| \| 0.466 \| 22.0 \| 550 \| 0.5251 \| 0.81 \| 0.2677 \| 1.2663 \| 0.81 \| 0.7890 \| 0.2037 \| 0.0745 \| \| 0.466 \| 23.0 \| 575 \| 0.5158 \| 0.83 \| 0.2638 \| 1.2621 \| 0.83 \| 0.8070 \| 0.1927 \| 0.0614 \| \| 0.466 \| 24.0 \| 600 \| 0.5056 \| 0.835 \| 0.2590 \| 1.5337 \| 0.835 \| 0.8080 \| 0.1887 \| 0.0617 \| \| 0.466 \| 25.0 \| 625 \| 0.4897 \| 0.85 \| 0.2476 \| 1.4341 \| 0.85 \| 0.8361 \| 0.1870 \| 0.0627 \| \| 0.466 \| 26.0 \| 650 \| 0.4994 \| 0.85 \| 0.2556 \| 1.5846 \| 0.85 \| 0.8302 \| 0.1965 \| 0.0718 \| \| 0.466 \| 27.0 \| 675 \| 0.4720 \| 0.845 \| 0.2406 \| 1.3093 \| 0.845 \| 0.8234 \| 0.1873 \| 0.0704 \| \| 0.466 \| 28.0 \| 700 \| 0.4858 \| 0.84 \| 0.2486 \| 1.4459 \| 0.8400 \| 0.8192 \| 0.1676 \| 0.0730 \| \| 0.466 \| 29.0 \| 725 \| 0.4908 \| 0.84 \| 0.2510 \| 1.4941 \| 0.8400 \| 0.8159 \| 0.1754 \| 0.0717 \| \| 0.466 \| 30.0 \| 750 \| 0.4805 \| 0.855 \| 0.2442 \| 1.3279 \| 0.855 \| 0.8334 \| 0.1827 \| 0.0667 \| \| 0.466 \| 31.0 \| 775 \| 0.4783 \| 0.845 \| 0.2428 \| 1.4150 \| 0.845 \| 0.8264 \| 0.1759 \| 0.0660 \| \| 0.466 \| 32.0 \| 800 \| 0.4822 \| 0.855 \| 0.2449 \| 1.4848 \| 0.855 \| 0.8322 \| 0.1928 \| 0.0702 \| \| 0.466 \| 33.0 \| 825 \| 0.4845 \| 0.84 \| 0.2462 \| 1.4925 \| 0.8400 \| 0.8227 \| 0.1837 \| 0.0692 \| \| 0.466 \| 34.0 \| 850 \| 0.4843 \| 0.85 \| 0.2466 \| 1.4881 \| 0.85 \| 0.8295 \| 0.1752 \| 0.0683 \| \| 0.466 \| 35.0 \| 875 \| 0.4837 \| 0.85 \| 0.2464 \| 1.4939 \| 0.85 \| 0.8295 \| 0.1842 \| 0.0718 \| \| 0.466 \| 36.0 \| 900 \| 0.4843 \| 0.85 \| 0.2467 \| 1.4910 \| 0.85 \| 0.8295 \| 0.1950 \| 0.0705 \| \| 0.466 \| 37.0 \| 925 \| 0.4862 \| 0.85 \| 0.2479 \| 1.4938 \| 0.85 \| 0.8295 \| 0.1871 \| 0.0713 \| \| 0.466 \| 38.0 \| 950 \| 0.4854 \| 0.85 \| 0.2478 \| 1.4945 \| 0.85 \| 0.8295 \| 0.1859 \| 0.0719 \| \| 0.466 \| 39.0 \| 975 \| 0.4850 \| 0.85 \| 0.2471 \| 1.4891 \| 0.85 \| 0.8295 \| 0.1855 \| 0.0724 \| \| 0.0749 \| 40.0 \| 1000 \| 0.4869 \| 0.85 \| 0.2484 \| 1.4967 \| 0.85 \| 0.8295 \| 0.1969 \| 0.0718 \| \| 0.0749 \| 41.0 \| 1025 \| 0.4857 \| 0.85 \| 0.2482 \| 1.5544 \| 0.85 \| 0.8295 \| 0.1904 \| 0.0726 \| \| 0.0749 \| 42.0 \| 1050 \| 0.4872 \| 0.85 \| 0.2487 \| 1.5559 \| 0.85 \| 0.8295 \| 0.1877 \| 0.0732 \| \| 0.0749 \| 43.0 \| 1075 \| 0.4873 \| 0.85 \| 0.2488 \| 1.5534 \| 0.85 \| 0.8295 \| 0.1871 \| 0.0723 \| \| 0.0749 \| 44.0 \| 1100 \| 0.4870 \| 0.85 \| 0.2489 \| 1.5542 \| 0.85 \| 0.8295 \| 0.1787 \| 0.0730 \| \| 0.0749 \| 45.0 \| 1125 \| 0.4874 \| 0.85 \| 0.2490 \| 1.5544 \| 0.85 \| 0.8295 \| 0.1867 \| 0.0724 \| \| 0.0749 \| 46.0 \| 1150 \| 0.4868 \| 0.85 \| 0.2486 \| 1.5531 \| 0.85 \| 0.8295 \| 0.1954 \| 0.0723 \| \| 0.0749 \| 47.0 \| 1175 \| 0.4879 \| 0.85 \| 0.2493 \| 1.5546 \| 0.85 \| 0.8295 \| 0.1842 \| 0.0727 \| \| 0.0749 \| 48.0 \| 1200 \| 0.4882 \| 0.85 \| 0.2495 \| 1.5537 \| 0.85 \| 0.8295 \| 0.1864 \| 0.0730 \| \| 0.0749 \| 49.0 \| 1225 \| 0.4875 \| 0.85 \| 0.2492 \| 1.5537 \| 0.85 \| 0.8295 \| 0.1884 \| 0.0727 \| \| 0.0749 \| 50.0 \| 1250 \| 0.4880 \| 0.85 \| 0.2494 \| 1.5528 \| 0.85 \| 0.8295 \| 0.1877 \| 0.0726 \| \| 0.0749 \| 51.0 \| 1275 \| 0.4888 \| 0.85 \| 0.2499 \| 1.5539 \| 0.85 \| 0.8295 \| 0.1754 \| 0.0725 \| \| 0.0749 \| 52.0 \| 1300 \| 0.4894 \| 0.85 \| 0.2501 \| 1.5540 \| 0.85 \| 0.8295 \| 0.1883 \| 0.0736 \| \| 0.0749 \| 53.0 \| 1325 \| 0.4889 \| 0.85 \| 0.2501 \| 1.5533 \| 0.85 \| 0.8295 \| 0.1708 \| 0.0727 \| \| 0.0749 \| 54.0 \| 1350 \| 0.4891 \| 0.85 \| 0.2500 \| 1.5531 \| 0.85 \| 0.8295 \| 0.1785 \| 0.0729 \| \| 0.0749 \| 55.0 \| 1375 \| 0.4904 \| 0.85 \| 0.2509 \| 1.5541 \| 0.85 \| 0.8295 \| 0.1744 \| 0.0730 \| \| 0.0749 \| 56.0 \| 1400 \| 0.4903 \| 0.85 \| 0.2507 \| 1.5541 \| 0.85 \| 0.8295 \| 0.1897 \| 0.0730 \| \| 0.0749 \| 57.0 \| 1425 \| 0.4894 \| 0.85 \| 0.2503 \| 1.5536 \| 0.85 \| 0.8295 \| 0.1792 \| 0.0730 \| \| 0.0749 \| 58.0 \| 1450 \| 0.4889 \| 0.85 \| 0.2501 \| 1.5531 \| 0.85 \| 0.8295 \| 0.1892 \| 0.0730 \| \| 0.0749 \| 59.0 \| 1475 \| 0.4907 \| 0.85 \| 0.2511 \| 1.5542 \| 0.85 \| 0.8295 \| 0.1767 \| 0.0733 \| \| 0.0712 \| 60.0 \| 1500 \| 0.4897 \| 0.85 \| 0.2506 \| 1.5540 \| 0.85 \| 0.8295 \| 0.1813 \| 0.0732 \| \| 0.0712 \| 61.0 \| 1525 \| 0.4906 \| 0.85 \| 0.2512 \| 1.5545 \| 0.85 \| 0.8295 \| 0.1853 \| 0.0733 \| \| 0.0712 \| 62.0 \| 1550 \| 0.4905 \| 0.85 \| 0.2512 \| 1.5541 \| 0.85 \| 0.8295 \| 0.1723 \| 0.0733 \| \| 0.0712 \| 63.0 \| 1575 \| 0.4904 \| 0.85 \| 0.2512 \| 1.5543 \| 0.85 \| 0.8295 \| 0.1817 \| 0.0732 \| \| 0.0712 \| 64.0 \| 1600 \| 0.4915 \| 0.85 \| 0.2515 \| 1.5544 \| 0.85 \| 0.8295 \| 0.1942 \| 0.0736 \| \| 0.0712 \| 65.0 \| 1625 \| 0.4898 \| 0.85 \| 0.2506 \| 1.5534 \| 0.85 \| 0.8295 \| 0.1712 \| 0.0735 \| \| 0.0712 \| 66.0 \| 1650 \| 0.4911 \| 0.85 \| 0.2516 \| 1.5548 \| 0.85 \| 0.8295 \| 0.1824 \| 0.0733 \| \| 0.0712 \| 67.0 \| 1675 \| 0.4908 \| 0.85 \| 0.2513 \| 1.5546 \| 0.85 \| 0.8295 \| 0.1896 \| 0.0734 \| \| 0.0712 \| 68.0 \| 1700 \| 0.4911 \| 0.85 \| 0.2516 \| 1.5548 \| 0.85 \| 0.8295 \| 0.1744 \| 0.0734 \| \| 0.0712 \| 69.0 \| 1725 \| 0.4912 \| 0.85 \| 0.2516 \| 1.5541 \| 0.85 \| 0.8295 \| 0.1726 \| 0.0733 \| \| 0.0712 \| 70.0 \| 1750 \| 0.4910 \| 0.85 \| 0.2514 \| 1.5543 \| 0.85 \| 0.8295 \| 0.1827 \| 0.0736 \| \| 0.0712 \| 71.0 \| 1775 \| 0.4918 \| 0.85 \| 0.2520 \| 1.5546 \| 0.85 \| 0.8295 \| 0.1909 \| 0.0736 \| \| 0.0712 \| 72.0 \| 1800 \| 0.4916 \| 0.85 \| 0.2519 \| 1.5545 \| 0.85 \| 0.8295 \| 0.1830 \| 0.0734 \| \| 0.0712 \| 73.0 \| 1825 \| 0.4913 \| 0.85 \| 0.2517 \| 1.5540 \| 0.85 \| 0.8295 \| 0.1835 \| 0.0733 \| \| 0.0712 \| 74.0 \| 1850 \| 0.4918 \| 0.85 \| 0.2521 \| 1.5544 \| 0.85 \| 0.8295 \| 0.1831 \| 0.0736 \| \| 0.0712 \| 75.0 \| 1875 \| 0.4919 \| 0.85 \| 0.2521 \| 1.5548 \| 0.85 \| 0.8295 \| 0.1829 \| 0.0734 \| \| 0.0712 \| 76.0 \| 1900 \| 0.4916 \| 0.85 \| 0.2520 \| 1.5547 \| 0.85 \| 0.8295 \| 0.1831 \| 0.0733 \| \| 0.0712 \| 77.0 \| 1925 \| 0.4919 \| 0.85 \| 0.2521 \| 1.5542 \| 0.85 \| 0.8295 \| 0.1732 \| 0.0735 \| \| 0.0712 \| 78.0 \| 1950 \| 0.4920 \| 0.85 \| 0.2521 \| 1.5541 \| 0.85 \| 0.8295 \| 0.1831 \| 0.0734 \| \| 0.0712 \| 79.0 \| 1975 \| 0.4920 \| 0.85 \| 0.2522 \| 1.5544 \| 0.85 \| 0.8295 \| 0.1833 \| 0.0734 \| \| 0.0712 \| 80.0 \| 2000 \| 0.4922 \| 0.845 \| 0.2523 \| 1.5549 \| 0.845 \| 0.8258 \| 0.1859 \| 0.0735 \| \| 0.0712 \| 81.0 \| 2025 \| 0.4920 \| 0.85 \| 0.2522 \| 1.5542 \| 0.85 \| 0.8295 \| 0.1830 \| 0.0732 \| \| 0.0712 \| 82.0 \| 2050 \| 0.4920 \| 0.845 \| 0.2522 \| 1.5549 \| 0.845 \| 0.8258 \| 0.1783 \| 0.0734 \| \| 0.0712 \| 83.0 \| 2075 \| 0.4922 \| 0.85 \| 0.2524 \| 1.5546 \| 0.85 \| 0.8295 \| 0.1832 \| 0.0734 \| \| 0.0712 \| 84.0 \| 2100 \| 0.4920 \| 0.845 \| 0.2522 \| 1.5543 \| 0.845 \| 0.8258 \| 0.1784 \| 0.0735 \| \| 0.0712 \| 85.0 \| 2125 \| 0.4921 \| 0.845 \| 0.2523 \| 1.5547 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0735 \| \| 0.0712 \| 86.0 \| 2150 \| 0.4921 \| 0.85 \| 0.2523 \| 1.5545 \| 0.85 \| 0.8295 \| 0.1836 \| 0.0733 \| \| 0.0712 \| 87.0 \| 2175 \| 0.4924 \| 0.85 \| 0.2524 \| 1.5547 \| 0.85 \| 0.8295 \| 0.1836 \| 0.0734 \| \| 0.0712 \| 88.0 \| 2200 \| 0.4925 \| 0.845 \| 0.2524 \| 1.5548 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0735 \| \| 0.0712 \| 89.0 \| 2225 \| 0.4924 \| 0.85 \| 0.2525 \| 1.5548 \| 0.85 \| 0.8295 \| 0.1835 \| 0.0734 \| \| 0.0712 \| 90.0 \| 2250 \| 0.4921 \| 0.845 \| 0.2523 \| 1.5545 \| 0.845 \| 0.8258 \| 0.1688 \| 0.0735 \| \| 0.0712 \| 91.0 \| 2275 \| 0.4925 \| 0.845 \| 0.2525 \| 1.5546 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0735 \| \| 0.0712 \| 92.0 \| 2300 \| 0.4924 \| 0.845 \| 0.2524 \| 1.5546 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0712 \| 93.0 \| 2325 \| 0.4925 \| 0.845 \| 0.2526 \| 1.5548 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0712 \| 94.0 \| 2350 \| 0.4924 \| 0.845 \| 0.2525 \| 1.5547 \| 0.845 \| 0.8258 \| 0.1786 \| 0.0736 \| \| 0.0712 \| 95.0 \| 2375 \| 0.4926 \| 0.845 \| 0.2526 \| 1.5547 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0712 \| 96.0 \| 2400 \| 0.4925 \| 0.845 \| 0.2526 \| 1.5548 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0712 \| 97.0 \| 2425 \| 0.4925 \| 0.845 \| 0.2526 \| 1.5547 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0735 \| \| 0.0712 \| 98.0 \| 2450 \| 0.4926 \| 0.845 \| 0.2526 \| 1.5548 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0712 \| 99.0 \| 2475 \| 0.4925 \| 0.845 \| 0.2526 \| 1.5548 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| \| 0.0711 \| 100.0 \| 2500 \| 0.4925 \| 0.845 \| 0.2526 \| 1.5547 \| 0.845 \| 0.8258 \| 0.1785 \| 0.0736 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5536 - Accuracy: 0.82 - Brier Loss: 0.2571 - Nll: 1.4560 - F1 Micro: 0.82 - F1 Macro: 0.7994 - Ece: 0.1404 - Aurc: 0.0578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 2.0125 \| 0.23 \| 0.8650 \| 4.4951 \| 0.23 \| 0.1799 \| 0.2806 \| 0.7660 \| \| No log \| 2.0 \| 50 \| 1.2756 \| 0.555 \| 0.5948 \| 2.6781 \| 0.555 \| 0.4537 \| 0.2800 \| 0.2519 \| \| No log \| 3.0 \| 75 \| 0.9515 \| 0.685 \| 0.4392 \| 1.9416 \| 0.685 \| 0.5937 \| 0.2067 \| 0.1288 \| \| No log \| 4.0 \| 100 \| 0.7861 \| 0.72 \| 0.3622 \| 1.5125 \| 0.72 \| 0.6675 \| 0.2050 \| 0.0961 \| \| No log \| 5.0 \| 125 \| 0.7551 \| 0.77 \| 0.3362 \| 1.5478 \| 0.7700 \| 0.7318 \| 0.2043 \| 0.0838 \| \| No log \| 6.0 \| 150 \| 0.8056 \| 0.77 \| 0.3525 \| 1.4305 \| 0.7700 \| 0.7589 \| 0.1943 \| 0.0891 \| \| No log \| 7.0 \| 175 \| 0.7942 \| 0.775 \| 0.3310 \| 1.8237 \| 0.775 \| 0.7454 \| 0.1812 \| 0.0924 \| \| No log \| 8.0 \| 200 \| 0.7735 \| 0.77 \| 0.3384 \| 1.5161 \| 0.7700 \| 0.7530 \| 0.1987 \| 0.0931 \| \| No log \| 9.0 \| 225 \| 0.6992 \| 0.79 \| 0.3025 \| 1.5664 \| 0.79 \| 0.7777 \| 0.1631 \| 0.0774 \| \| No log \| 10.0 \| 250 \| 0.6753 \| 0.8 \| 0.2955 \| 1.5189 \| 0.8000 \| 0.7900 \| 0.1654 \| 0.0633 \| \| No log \| 11.0 \| 275 \| 0.7701 \| 0.805 \| 0.3018 \| 1.4787 \| 0.805 \| 0.7932 \| 0.1581 \| 0.0881 \| \| No log \| 12.0 \| 300 \| 0.7164 \| 0.79 \| 0.3292 \| 1.3527 \| 0.79 \| 0.7892 \| 0.1946 \| 0.0871 \| \| No log \| 13.0 \| 325 \| 0.6376 \| 0.8 \| 0.2901 \| 1.4953 \| 0.8000 \| 0.7824 \| 0.1770 \| 0.0659 \| \| No log \| 14.0 \| 350 \| 0.7319 \| 0.77 \| 0.3247 \| 1.6062 \| 0.7700 \| 0.7424 \| 0.1803 \| 0.0816 \| \| No log \| 15.0 \| 375 \| 0.5749 \| 0.805 \| 0.2738 \| 0.8483 \| 0.805 \| 0.8010 \| 0.1569 \| 0.0647 \| \| No log \| 16.0 \| 400 \| 0.6879 \| 0.775 \| 0.3085 \| 1.3379 \| 0.775 \| 0.7759 \| 0.1909 \| 0.0730 \| \| No log \| 17.0 \| 425 \| 0.5094 \| 0.85 \| 0.2241 \| 1.4391 \| 0.85 \| 0.8360 \| 0.1589 \| 0.0441 \| \| No log \| 18.0 \| 450 \| 0.6826 \| 0.8 \| 0.3015 \| 1.6933 \| 0.8000 \| 0.7969 \| 0.1651 \| 0.0792 \| \| No log \| 19.0 \| 475 \| 0.5677 \| 0.825 \| 0.2622 \| 1.5426 \| 0.825 \| 0.8051 \| 0.1600 \| 0.0515 \| \| 0.4493 \| 20.0 \| 500 \| 0.5156 \| 0.85 \| 0.2312 \| 1.5882 \| 0.85 \| 0.8471 \| 0.1466 \| 0.0427 \| \| 0.4493 \| 21.0 \| 525 \| 0.5743 \| 0.83 \| 0.2600 \| 1.5702 \| 0.83 \| 0.8187 \| 0.1604 \| 0.0540 \| \| 0.4493 \| 22.0 \| 550 \| 0.5872 \| 0.825 \| 0.2712 \| 1.6270 \| 0.825 \| 0.8056 \| 0.1687 \| 0.0572 \| \| 0.4493 \| 23.0 \| 575 \| 0.5770 \| 0.81 \| 0.2701 \| 1.5089 \| 0.81 \| 0.7969 \| 0.1559 \| 0.0655 \| \| 0.4493 \| 24.0 \| 600 \| 0.5621 \| 0.82 \| 0.2590 \| 1.3500 \| 0.82 \| 0.8052 \| 0.1621 \| 0.0587 \| \| 0.4493 \| 25.0 \| 625 \| 0.5480 \| 0.805 \| 0.2518 \| 1.2519 \| 0.805 \| 0.7884 \| 0.1483 \| 0.0619 \| \| 0.4493 \| 26.0 \| 650 \| 0.5555 \| 0.81 \| 0.2575 \| 1.3183 \| 0.81 \| 0.7926 \| 0.1585 \| 0.0598 \| \| 0.4493 \| 27.0 \| 675 \| 0.5449 \| 0.82 \| 0.2524 \| 1.4400 \| 0.82 \| 0.8059 \| 0.1713 \| 0.0579 \| \| 0.4493 \| 28.0 \| 700 \| 0.5483 \| 0.81 \| 0.2545 \| 1.4400 \| 0.81 \| 0.7894 \| 0.1450 \| 0.0580 \| \| 0.4493 \| 29.0 \| 725 \| 0.5448 \| 0.81 \| 0.2524 \| 1.3070 \| 0.81 \| 0.7931 \| 0.1447 \| 0.0595 \| \| 0.4493 \| 30.0 \| 750 \| 0.5476 \| 0.815 \| 0.2538 \| 1.3101 \| 0.815 \| 0.7982 \| 0.1536 \| 0.0582 \| \| 0.4493 \| 31.0 \| 775 \| 0.5433 \| 0.82 \| 0.2529 \| 1.3812 \| 0.82 \| 0.8011 \| 0.1637 \| 0.0575 \| \| 0.4493 \| 32.0 \| 800 \| 0.5469 \| 0.805 \| 0.2528 \| 1.2973 \| 0.805 \| 0.7905 \| 0.1668 \| 0.0600 \| \| 0.4493 \| 33.0 \| 825 \| 0.5443 \| 0.815 \| 0.2525 \| 1.3020 \| 0.815 \| 0.7933 \| 0.1768 \| 0.0579 \| \| 0.4493 \| 34.0 \| 850 \| 0.5442 \| 0.82 \| 0.2521 \| 1.3234 \| 0.82 \| 0.8011 \| 0.1555 \| 0.0580 \| \| 0.4493 \| 35.0 \| 875 \| 0.5434 \| 0.82 \| 0.2531 \| 1.4362 \| 0.82 \| 0.8011 \| 0.1430 \| 0.0564 \| \| 0.4493 \| 36.0 \| 900 \| 0.5469 \| 0.815 \| 0.2534 \| 1.3075 \| 0.815 \| 0.7933 \| 0.1590 \| 0.0578 \| \| 0.4493 \| 37.0 \| 925 \| 0.5468 \| 0.815 \| 0.2546 \| 1.3204 \| 0.815 \| 0.7933 \| 0.1623 \| 0.0567 \| \| 0.4493 \| 38.0 \| 950 \| 0.5473 \| 0.815 \| 0.2540 \| 1.3722 \| 0.815 \| 0.7933 \| 0.1514 \| 0.0582 \| \| 0.4493 \| 39.0 \| 975 \| 0.5453 \| 0.82 \| 0.2532 \| 1.3874 \| 0.82 \| 0.8011 \| 0.1751 \| 0.0568 \| \| 0.0581 \| 40.0 \| 1000 \| 0.5475 \| 0.815 \| 0.2543 \| 1.3116 \| 0.815 \| 0.7933 \| 0.1654 \| 0.0573 \| \| 0.0581 \| 41.0 \| 1025 \| 0.5452 \| 0.815 \| 0.2533 \| 1.4421 \| 0.815 \| 0.7933 \| 0.1459 \| 0.0579 \| \| 0.0581 \| 42.0 \| 1050 \| 0.5467 \| 0.815 \| 0.2538 \| 1.3730 \| 0.815 \| 0.7933 \| 0.1642 \| 0.0576 \| \| 0.0581 \| 43.0 \| 1075 \| 0.5478 \| 0.815 \| 0.2544 \| 1.3086 \| 0.815 \| 0.7933 \| 0.1657 \| 0.0581 \| \| 0.0581 \| 44.0 \| 1100 \| 0.5482 \| 0.815 \| 0.2545 \| 1.3744 \| 0.815 \| 0.7933 \| 0.1629 \| 0.0583 \| \| 0.0581 \| 45.0 \| 1125 \| 0.5493 \| 0.815 \| 0.2550 \| 1.3676 \| 0.815 \| 0.7933 \| 0.1638 \| 0.0594 \| \| 0.0581 \| 46.0 \| 1150 \| 0.5478 \| 0.82 \| 0.2547 \| 1.4645 \| 0.82 \| 0.8011 \| 0.1631 \| 0.0572 \| \| 0.0581 \| 47.0 \| 1175 \| 0.5487 \| 0.815 \| 0.2547 \| 1.3795 \| 0.815 \| 0.7933 \| 0.1634 \| 0.0577 \| \| 0.0581 \| 48.0 \| 1200 \| 0.5471 \| 0.825 \| 0.2546 \| 1.4421 \| 0.825 \| 0.8067 \| 0.1436 \| 0.0564 \| \| 0.0581 \| 49.0 \| 1225 \| 0.5489 \| 0.815 \| 0.2547 \| 1.3676 \| 0.815 \| 0.7933 \| 0.1663 \| 0.0578 \| \| 0.0581 \| 50.0 \| 1250 \| 0.5482 \| 0.82 \| 0.2549 \| 1.4346 \| 0.82 \| 0.7990 \| 0.1481 \| 0.0574 \| \| 0.0581 \| 51.0 \| 1275 \| 0.5472 \| 0.82 \| 0.2540 \| 1.5012 \| 0.82 \| 0.8011 \| 0.1565 \| 0.0569 \| \| 0.0581 \| 52.0 \| 1300 \| 0.5489 \| 0.825 \| 0.2553 \| 1.4351 \| 0.825 \| 0.8051 \| 0.1608 \| 0.0576 \| \| 0.0581 \| 53.0 \| 1325 \| 0.5486 \| 0.815 \| 0.2549 \| 1.3799 \| 0.815 \| 0.7933 \| 0.1483 \| 0.0573 \| \| 0.0581 \| 54.0 \| 1350 \| 0.5498 \| 0.815 \| 0.2552 \| 1.4434 \| 0.815 \| 0.7933 \| 0.1542 \| 0.0578 \| \| 0.0581 \| 55.0 \| 1375 \| 0.5508 \| 0.82 \| 0.2559 \| 1.4394 \| 0.82 \| 0.7994 \| 0.1562 \| 0.0576 \| \| 0.0581 \| 56.0 \| 1400 \| 0.5492 \| 0.825 \| 0.2552 \| 1.4368 \| 0.825 \| 0.8051 \| 0.1483 \| 0.0572 \| \| 0.0581 \| 57.0 \| 1425 \| 0.5501 \| 0.815 \| 0.2552 \| 1.3874 \| 0.815 \| 0.7933 \| 0.1390 \| 0.0579 \| \| 0.0581 \| 58.0 \| 1450 \| 0.5497 \| 0.82 \| 0.2553 \| 1.4365 \| 0.82 \| 0.7994 \| 0.1437 \| 0.0579 \| \| 0.0581 \| 59.0 \| 1475 \| 0.5507 \| 0.82 \| 0.2557 \| 1.4343 \| 0.82 \| 0.7994 \| 0.1389 \| 0.0584 \| \| 0.056 \| 60.0 \| 1500 \| 0.5501 \| 0.825 \| 0.2555 \| 1.4410 \| 0.825 \| 0.8051 \| 0.1585 \| 0.0583 \| \| 0.056 \| 61.0 \| 1525 \| 0.5510 \| 0.82 \| 0.2559 \| 1.4380 \| 0.82 \| 0.7994 \| 0.1395 \| 0.0578 \| \| 0.056 \| 62.0 \| 1550 \| 0.5510 \| 0.82 \| 0.2558 \| 1.4421 \| 0.82 \| 0.7994 \| 0.1441 \| 0.0573 \| \| 0.056 \| 63.0 \| 1575 \| 0.5508 \| 0.82 \| 0.2559 \| 1.4369 \| 0.82 \| 0.7994 \| 0.1395 \| 0.0575 \| \| 0.056 \| 64.0 \| 1600 \| 0.5514 \| 0.82 \| 0.2560 \| 1.4410 \| 0.82 \| 0.7994 \| 0.1393 \| 0.0579 \| \| 0.056 \| 65.0 \| 1625 \| 0.5519 \| 0.825 \| 0.2563 \| 1.4544 \| 0.825 \| 0.8051 \| 0.1427 \| 0.0575 \| \| 0.056 \| 66.0 \| 1650 \| 0.5510 \| 0.82 \| 0.2560 \| 1.4400 \| 0.82 \| 0.7994 \| 0.1391 \| 0.0576 \| \| 0.056 \| 67.0 \| 1675 \| 0.5520 \| 0.825 \| 0.2563 \| 1.4396 \| 0.825 \| 0.8051 \| 0.1422 \| 0.0580 \| \| 0.056 \| 68.0 \| 1700 \| 0.5516 \| 0.82 \| 0.2561 \| 1.4412 \| 0.82 \| 0.7994 \| 0.1394 \| 0.0580 \| \| 0.056 \| 69.0 \| 1725 \| 0.5512 \| 0.82 \| 0.2560 \| 1.4433 \| 0.82 \| 0.7994 \| 0.1393 \| 0.0577 \| \| 0.056 \| 70.0 \| 1750 \| 0.5515 \| 0.82 \| 0.2561 \| 1.4418 \| 0.82 \| 0.7994 \| 0.1391 \| 0.0576 \| \| 0.056 \| 71.0 \| 1775 \| 0.5517 \| 0.82 \| 0.2562 \| 1.4448 \| 0.82 \| 0.7994 \| 0.1449 \| 0.0581 \| \| 0.056 \| 72.0 \| 1800 \| 0.5524 \| 0.825 \| 0.2566 \| 1.4421 \| 0.825 \| 0.8051 \| 0.1437 \| 0.0579 \| \| 0.056 \| 73.0 \| 1825 \| 0.5518 \| 0.82 \| 0.2562 \| 1.4403 \| 0.82 \| 0.7994 \| 0.1469 \| 0.0576 \| \| 0.056 \| 74.0 \| 1850 \| 0.5529 \| 0.825 \| 0.2568 \| 1.4450 \| 0.825 \| 0.8051 \| 0.1434 \| 0.0580 \| \| 0.056 \| 75.0 \| 1875 \| 0.5528 \| 0.82 \| 0.2566 \| 1.4475 \| 0.82 \| 0.7994 \| 0.1447 \| 0.0585 \| \| 0.056 \| 76.0 \| 1900 \| 0.5529 \| 0.82 \| 0.2568 \| 1.4463 \| 0.82 \| 0.7994 \| 0.1447 \| 0.0578 \| \| 0.056 \| 77.0 \| 1925 \| 0.5528 \| 0.82 \| 0.2567 \| 1.4469 \| 0.82 \| 0.7994 \| 0.1401 \| 0.0577 \| \| 0.056 \| 78.0 \| 1950 \| 0.5525 \| 0.82 \| 0.2565 \| 1.4506 \| 0.82 \| 0.7994 \| 0.1444 \| 0.0576 \| \| 0.056 \| 79.0 \| 1975 \| 0.5527 \| 0.825 \| 0.2567 \| 1.4479 \| 0.825 \| 0.8051 \| 0.1423 \| 0.0576 \| \| 0.0559 \| 80.0 \| 2000 \| 0.5530 \| 0.825 \| 0.2568 \| 1.4429 \| 0.825 \| 0.8051 \| 0.1423 \| 0.0578 \| \| 0.0559 \| 81.0 \| 2025 \| 0.5529 \| 0.825 \| 0.2567 \| 1.4489 \| 0.825 \| 0.8051 \| 0.1422 \| 0.0581 \| \| 0.0559 \| 82.0 \| 2050 \| 0.5529 \| 0.82 \| 0.2568 \| 1.4550 \| 0.82 \| 0.7994 \| 0.1401 \| 0.0576 \| \| 0.0559 \| 83.0 \| 2075 \| 0.5534 \| 0.82 \| 0.2570 \| 1.4458 \| 0.82 \| 0.7994 \| 0.1399 \| 0.0580 \| \| 0.0559 \| 84.0 \| 2100 \| 0.5530 \| 0.82 \| 0.2568 \| 1.4497 \| 0.82 \| 0.7994 \| 0.1399 \| 0.0577 \| \| 0.0559 \| 85.0 \| 2125 \| 0.5533 \| 0.82 \| 0.2570 \| 1.4507 \| 0.82 \| 0.7994 \| 0.1401 \| 0.0577 \| \| 0.0559 \| 86.0 \| 2150 \| 0.5531 \| 0.825 \| 0.2568 \| 1.4515 \| 0.825 \| 0.8051 \| 0.1428 \| 0.0577 \| \| 0.0559 \| 87.0 \| 2175 \| 0.5534 \| 0.82 \| 0.2569 \| 1.4503 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0577 \| \| 0.0559 \| 88.0 \| 2200 \| 0.5534 \| 0.82 \| 0.2569 \| 1.4532 \| 0.82 \| 0.7994 \| 0.1399 \| 0.0581 \| \| 0.0559 \| 89.0 \| 2225 \| 0.5533 \| 0.825 \| 0.2569 \| 1.4499 \| 0.825 \| 0.8051 \| 0.1423 \| 0.0578 \| \| 0.0559 \| 90.0 \| 2250 \| 0.5534 \| 0.82 \| 0.2570 \| 1.4517 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0577 \| \| 0.0559 \| 91.0 \| 2275 \| 0.5533 \| 0.82 \| 0.2569 \| 1.4526 \| 0.82 \| 0.7994 \| 0.1405 \| 0.0579 \| \| 0.0559 \| 92.0 \| 2300 \| 0.5534 \| 0.825 \| 0.2570 \| 1.4533 \| 0.825 \| 0.8051 \| 0.1424 \| 0.0577 \| \| 0.0559 \| 93.0 \| 2325 \| 0.5535 \| 0.82 \| 0.2570 \| 1.4527 \| 0.82 \| 0.7994 \| 0.1399 \| 0.0580 \| \| 0.0559 \| 94.0 \| 2350 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4533 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0577 \| \| 0.0559 \| 95.0 \| 2375 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4547 \| 0.82 \| 0.7994 \| 0.1400 \| 0.0579 \| \| 0.0559 \| 96.0 \| 2400 \| 0.5535 \| 0.82 \| 0.2570 \| 1.4567 \| 0.82 \| 0.7994 \| 0.1400 \| 0.0578 \| \| 0.0559 \| 97.0 \| 2425 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4523 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0579 \| \| 0.0559 \| 98.0 \| 2450 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4570 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0578 \| \| 0.0559 \| 99.0 \| 2475 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4570 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0578 \| \| 0.0559 \| 100.0 \| 2500 \| 0.5536 \| 0.82 \| 0.2571 \| 1.4560 \| 0.82 \| 0.7994 \| 0.1404 \| 0.0578 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4137 - Accuracy: 0.83 - Brier Loss: 0.2631 - Nll: 1.5189 - F1 Micro: 0.83 - F1 Macro: 0.8172 - Ece: 0.2007 - Aurc: 0.0591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.6265 \| 0.23 \| 0.8647 \| 5.1432 \| 0.23 \| 0.1847 \| 0.2751 \| 0.7516 \| \| No log \| 2.0 \| 50 \| 1.0240 \| 0.505 \| 0.6074 \| 2.7425 \| 0.505 \| 0.3980 \| 0.2977 \| 0.2705 \| \| No log \| 3.0 \| 75 \| 0.8130 \| 0.655 \| 0.4809 \| 2.4939 \| 0.655 \| 0.5584 \| 0.2576 \| 0.1502 \| \| No log \| 4.0 \| 100 \| 0.6703 \| 0.735 \| 0.3867 \| 1.3509 \| 0.735 \| 0.6895 \| 0.2334 \| 0.1109 \| \| No log \| 5.0 \| 125 \| 0.6313 \| 0.755 \| 0.3420 \| 1.2521 \| 0.755 \| 0.7207 \| 0.2081 \| 0.0789 \| \| No log \| 6.0 \| 150 \| 0.6598 \| 0.76 \| 0.3543 \| 1.4171 \| 0.76 \| 0.7103 \| 0.2275 \| 0.0886 \| \| No log \| 7.0 \| 175 \| 0.5669 \| 0.77 \| 0.3368 \| 1.5060 \| 0.7700 \| 0.7351 \| 0.2247 \| 0.0941 \| \| No log \| 8.0 \| 200 \| 0.5486 \| 0.775 \| 0.3004 \| 1.1511 \| 0.775 \| 0.7413 \| 0.2252 \| 0.0640 \| \| No log \| 9.0 \| 225 \| 0.5456 \| 0.795 \| 0.3141 \| 1.4663 \| 0.795 \| 0.7762 \| 0.2198 \| 0.0889 \| \| No log \| 10.0 \| 250 \| 0.4954 \| 0.82 \| 0.2819 \| 1.4644 \| 0.82 \| 0.7981 \| 0.2150 \| 0.0649 \| \| No log \| 11.0 \| 275 \| 0.4804 \| 0.805 \| 0.2866 \| 1.3705 \| 0.805 \| 0.7927 \| 0.2078 \| 0.0658 \| \| No log \| 12.0 \| 300 \| 0.5234 \| 0.785 \| 0.3152 \| 1.5290 \| 0.785 \| 0.7681 \| 0.2149 \| 0.0637 \| \| No log \| 13.0 \| 325 \| 0.4701 \| 0.815 \| 0.2839 \| 1.4490 \| 0.815 \| 0.8010 \| 0.2315 \| 0.0586 \| \| No log \| 14.0 \| 350 \| 0.4859 \| 0.795 \| 0.2807 \| 1.1224 \| 0.795 \| 0.7957 \| 0.2170 \| 0.0512 \| \| No log \| 15.0 \| 375 \| 0.5580 \| 0.79 \| 0.3272 \| 1.7539 \| 0.79 \| 0.7735 \| 0.2376 \| 0.0708 \| \| No log \| 16.0 \| 400 \| 0.4918 \| 0.8 \| 0.2961 \| 1.5112 \| 0.8000 \| 0.7988 \| 0.1850 \| 0.0568 \| \| No log \| 17.0 \| 425 \| 0.4442 \| 0.8 \| 0.2846 \| 1.6182 \| 0.8000 \| 0.7767 \| 0.2083 \| 0.0712 \| \| No log \| 18.0 \| 450 \| 0.4460 \| 0.82 \| 0.2760 \| 1.6839 \| 0.82 \| 0.8027 \| 0.2127 \| 0.0523 \| \| No log \| 19.0 \| 475 \| 0.4423 \| 0.825 \| 0.2676 \| 1.3774 \| 0.825 \| 0.8176 \| 0.1853 \| 0.0557 \| \| 0.4472 \| 20.0 \| 500 \| 0.4998 \| 0.81 \| 0.2910 \| 1.7711 \| 0.81 \| 0.8152 \| 0.2181 \| 0.0635 \| \| 0.4472 \| 21.0 \| 525 \| 0.4579 \| 0.83 \| 0.2871 \| 1.7025 \| 0.83 \| 0.8135 \| 0.1927 \| 0.0696 \| \| 0.4472 \| 22.0 \| 550 \| 0.4421 \| 0.825 \| 0.2683 \| 1.6453 \| 0.825 \| 0.8215 \| 0.1929 \| 0.0613 \| \| 0.4472 \| 23.0 \| 575 \| 0.4368 \| 0.8 \| 0.2821 \| 1.7298 \| 0.8000 \| 0.7684 \| 0.2060 \| 0.0771 \| \| 0.4472 \| 24.0 \| 600 \| 0.4310 \| 0.83 \| 0.2689 \| 1.4699 \| 0.83 \| 0.8163 \| 0.2067 \| 0.0556 \| \| 0.4472 \| 25.0 \| 625 \| 0.4394 \| 0.83 \| 0.2751 \| 1.5955 \| 0.83 \| 0.8166 \| 0.2138 \| 0.0681 \| \| 0.4472 \| 26.0 \| 650 \| 0.4395 \| 0.815 \| 0.2786 \| 1.6788 \| 0.815 \| 0.8033 \| 0.2034 \| 0.0643 \| \| 0.4472 \| 27.0 \| 675 \| 0.4118 \| 0.84 \| 0.2578 \| 1.5641 \| 0.8400 \| 0.8293 \| 0.2024 \| 0.0554 \| \| 0.4472 \| 28.0 \| 700 \| 0.4273 \| 0.82 \| 0.2707 \| 1.7118 \| 0.82 \| 0.8090 \| 0.2133 \| 0.0674 \| \| 0.4472 \| 29.0 \| 725 \| 0.4207 \| 0.835 \| 0.2648 \| 1.6469 \| 0.835 \| 0.8206 \| 0.1948 \| 0.0652 \| \| 0.4472 \| 30.0 \| 750 \| 0.4172 \| 0.825 \| 0.2620 \| 1.5024 \| 0.825 \| 0.8114 \| 0.1833 \| 0.0601 \| \| 0.4472 \| 31.0 \| 775 \| 0.4148 \| 0.825 \| 0.2610 \| 1.4994 \| 0.825 \| 0.8070 \| 0.2052 \| 0.0593 \| \| 0.4472 \| 32.0 \| 800 \| 0.4148 \| 0.825 \| 0.2627 \| 1.6293 \| 0.825 \| 0.8088 \| 0.2080 \| 0.0618 \| \| 0.4472 \| 33.0 \| 825 \| 0.4159 \| 0.825 \| 0.2625 \| 1.5069 \| 0.825 \| 0.8135 \| 0.2082 \| 0.0604 \| \| 0.4472 \| 34.0 \| 850 \| 0.4168 \| 0.825 \| 0.2638 \| 1.5770 \| 0.825 \| 0.8137 \| 0.1888 \| 0.0588 \| \| 0.4472 \| 35.0 \| 875 \| 0.4181 \| 0.82 \| 0.2640 \| 1.5404 \| 0.82 \| 0.8043 \| 0.2145 \| 0.0582 \| \| 0.4472 \| 36.0 \| 900 \| 0.4154 \| 0.83 \| 0.2618 \| 1.5719 \| 0.83 \| 0.8165 \| 0.1965 \| 0.0586 \| \| 0.4472 \| 37.0 \| 925 \| 0.4160 \| 0.825 \| 0.2632 \| 1.5840 \| 0.825 \| 0.8137 \| 0.2003 \| 0.0604 \| \| 0.4472 \| 38.0 \| 950 \| 0.4133 \| 0.83 \| 0.2616 \| 1.5711 \| 0.83 \| 0.8163 \| 0.2040 \| 0.0596 \| \| 0.4472 \| 39.0 \| 975 \| 0.4167 \| 0.825 \| 0.2635 \| 1.5210 \| 0.825 \| 0.8138 \| 0.1930 \| 0.0590 \| \| 0.0652 \| 40.0 \| 1000 \| 0.4162 \| 0.83 \| 0.2630 \| 1.6312 \| 0.83 \| 0.8163 \| 0.1973 \| 0.0593 \| \| 0.0652 \| 41.0 \| 1025 \| 0.4144 \| 0.83 \| 0.2626 \| 1.5787 \| 0.83 \| 0.8163 \| 0.2068 \| 0.0603 \| \| 0.0652 \| 42.0 \| 1050 \| 0.4150 \| 0.83 \| 0.2631 \| 1.5789 \| 0.83 \| 0.8163 \| 0.1970 \| 0.0588 \| \| 0.0652 \| 43.0 \| 1075 \| 0.4158 \| 0.825 \| 0.2635 \| 1.5833 \| 0.825 \| 0.8138 \| 0.1927 \| 0.0597 \| \| 0.0652 \| 44.0 \| 1100 \| 0.4132 \| 0.83 \| 0.2622 \| 1.5130 \| 0.83 \| 0.8163 \| 0.2030 \| 0.0593 \| \| 0.0652 \| 45.0 \| 1125 \| 0.4146 \| 0.83 \| 0.2630 \| 1.6312 \| 0.83 \| 0.8165 \| 0.2010 \| 0.0587 \| \| 0.0652 \| 46.0 \| 1150 \| 0.4138 \| 0.825 \| 0.2624 \| 1.5301 \| 0.825 \| 0.8135 \| 0.2065 \| 0.0587 \| \| 0.0652 \| 47.0 \| 1175 \| 0.4142 \| 0.83 \| 0.2627 \| 1.6292 \| 0.83 \| 0.8163 \| 0.1984 \| 0.0591 \| \| 0.0652 \| 48.0 \| 1200 \| 0.4146 \| 0.825 \| 0.2629 \| 1.5735 \| 0.825 \| 0.8137 \| 0.1998 \| 0.0589 \| \| 0.0652 \| 49.0 \| 1225 \| 0.4143 \| 0.83 \| 0.2630 \| 1.5276 \| 0.83 \| 0.8163 \| 0.2116 \| 0.0599 \| \| 0.0652 \| 50.0 \| 1250 \| 0.4140 \| 0.83 \| 0.2628 \| 1.5705 \| 0.83 \| 0.8163 \| 0.1966 \| 0.0590 \| \| 0.0652 \| 51.0 \| 1275 \| 0.4152 \| 0.825 \| 0.2637 \| 1.5747 \| 0.825 \| 0.8138 \| 0.1835 \| 0.0593 \| \| 0.0652 \| 52.0 \| 1300 \| 0.4145 \| 0.825 \| 0.2629 \| 1.5796 \| 0.825 \| 0.8137 \| 0.1926 \| 0.0593 \| \| 0.0652 \| 53.0 \| 1325 \| 0.4147 \| 0.825 \| 0.2631 \| 1.6323 \| 0.825 \| 0.8138 \| 0.1838 \| 0.0588 \| \| 0.0652 \| 54.0 \| 1350 \| 0.4141 \| 0.83 \| 0.2628 \| 1.5763 \| 0.83 \| 0.8163 \| 0.2035 \| 0.0592 \| \| 0.0652 \| 55.0 \| 1375 \| 0.4137 \| 0.83 \| 0.2630 \| 1.5751 \| 0.83 \| 0.8163 \| 0.2042 \| 0.0590 \| \| 0.0652 \| 56.0 \| 1400 \| 0.4145 \| 0.83 \| 0.2632 \| 1.6307 \| 0.83 \| 0.8163 \| 0.1981 \| 0.0588 \| \| 0.0652 \| 57.0 \| 1425 \| 0.4149 \| 0.825 \| 0.2634 \| 1.5225 \| 0.825 \| 0.8137 \| 0.2008 \| 0.0589 \| \| 0.0652 \| 58.0 \| 1450 \| 0.4146 \| 0.83 \| 0.2634 \| 1.5725 \| 0.83 \| 0.8163 \| 0.2121 \| 0.0589 \| \| 0.0652 \| 59.0 \| 1475 \| 0.4142 \| 0.83 \| 0.2632 \| 1.5214 \| 0.83 \| 0.8163 \| 0.2028 \| 0.0590 \| \| 0.0614 \| 60.0 \| 1500 \| 0.4145 \| 0.83 \| 0.2634 \| 1.5237 \| 0.83 \| 0.8163 \| 0.1981 \| 0.0585 \| \| 0.0614 \| 61.0 \| 1525 \| 0.4142 \| 0.83 \| 0.2630 \| 1.5710 \| 0.83 \| 0.8163 \| 0.2070 \| 0.0591 \| \| 0.0614 \| 62.0 \| 1550 \| 0.4139 \| 0.825 \| 0.2631 \| 1.5733 \| 0.825 \| 0.8135 \| 0.1986 \| 0.0594 \| \| 0.0614 \| 63.0 \| 1575 \| 0.4139 \| 0.825 \| 0.2630 \| 1.5813 \| 0.825 \| 0.8136 \| 0.1984 \| 0.0593 \| \| 0.0614 \| 64.0 \| 1600 \| 0.4138 \| 0.83 \| 0.2629 \| 1.5729 \| 0.83 \| 0.8163 \| 0.2035 \| 0.0590 \| \| 0.0614 \| 65.0 \| 1625 \| 0.4139 \| 0.825 \| 0.2629 \| 1.5715 \| 0.825 \| 0.8136 \| 0.2026 \| 0.0593 \| \| 0.0614 \| 66.0 \| 1650 \| 0.4136 \| 0.825 \| 0.2629 \| 1.5768 \| 0.825 \| 0.8135 \| 0.1988 \| 0.0592 \| \| 0.0614 \| 67.0 \| 1675 \| 0.4139 \| 0.825 \| 0.2629 \| 1.5709 \| 0.825 \| 0.8135 \| 0.1987 \| 0.0593 \| \| 0.0614 \| 68.0 \| 1700 \| 0.4143 \| 0.825 \| 0.2633 \| 1.5744 \| 0.825 \| 0.8138 \| 0.1896 \| 0.0595 \| \| 0.0614 \| 69.0 \| 1725 \| 0.4142 \| 0.825 \| 0.2632 \| 1.5752 \| 0.825 \| 0.8138 \| 0.1896 \| 0.0593 \| \| 0.0614 \| 70.0 \| 1750 \| 0.4142 \| 0.825 \| 0.2632 \| 1.5769 \| 0.825 \| 0.8138 \| 0.1879 \| 0.0594 \| \| 0.0614 \| 71.0 \| 1775 \| 0.4138 \| 0.83 \| 0.2630 \| 1.5734 \| 0.83 \| 0.8163 \| 0.2073 \| 0.0588 \| \| 0.0614 \| 72.0 \| 1800 \| 0.4140 \| 0.825 \| 0.2631 \| 1.5734 \| 0.825 \| 0.8138 \| 0.1977 \| 0.0593 \| \| 0.0614 \| 73.0 \| 1825 \| 0.4135 \| 0.83 \| 0.2629 \| 1.5711 \| 0.83 \| 0.8163 \| 0.2035 \| 0.0589 \| \| 0.0614 \| 74.0 \| 1850 \| 0.4140 \| 0.83 \| 0.2632 \| 1.5717 \| 0.83 \| 0.8163 \| 0.2038 \| 0.0590 \| \| 0.0614 \| 75.0 \| 1875 \| 0.4141 \| 0.825 \| 0.2633 \| 1.5205 \| 0.825 \| 0.8138 \| 0.1838 \| 0.0593 \| \| 0.0614 \| 76.0 \| 1900 \| 0.4138 \| 0.825 \| 0.2631 \| 1.5218 \| 0.825 \| 0.8137 \| 0.1838 \| 0.0595 \| \| 0.0614 \| 77.0 \| 1925 \| 0.4134 \| 0.825 \| 0.2628 \| 1.5710 \| 0.825 \| 0.8135 \| 0.1937 \| 0.0591 \| \| 0.0614 \| 78.0 \| 1950 \| 0.4135 \| 0.83 \| 0.2629 \| 1.5688 \| 0.83 \| 0.8163 \| 0.2067 \| 0.0588 \| \| 0.0614 \| 79.0 \| 1975 \| 0.4138 \| 0.825 \| 0.2631 \| 1.5143 \| 0.825 \| 0.8137 \| 0.1942 \| 0.0592 \| \| 0.0613 \| 80.0 \| 2000 \| 0.4134 \| 0.825 \| 0.2628 \| 1.5152 \| 0.825 \| 0.8135 \| 0.1939 \| 0.0591 \| \| 0.0613 \| 81.0 \| 2025 \| 0.4139 \| 0.825 \| 0.2632 \| 1.5144 \| 0.825 \| 0.8136 \| 0.1903 \| 0.0593 \| \| 0.0613 \| 82.0 \| 2050 \| 0.4139 \| 0.83 \| 0.2632 \| 1.5242 \| 0.83 \| 0.8163 \| 0.1894 \| 0.0589 \| \| 0.0613 \| 83.0 \| 2075 \| 0.4138 \| 0.825 \| 0.2631 \| 1.5159 \| 0.825 \| 0.8136 \| 0.2014 \| 0.0594 \| \| 0.0613 \| 84.0 \| 2100 \| 0.4137 \| 0.825 \| 0.2631 \| 1.5707 \| 0.825 \| 0.8136 \| 0.1954 \| 0.0592 \| \| 0.0613 \| 85.0 \| 2125 \| 0.4136 \| 0.825 \| 0.2630 \| 1.5252 \| 0.825 \| 0.8136 \| 0.1878 \| 0.0592 \| \| 0.0613 \| 86.0 \| 2150 \| 0.4138 \| 0.83 \| 0.2630 \| 1.5186 \| 0.83 \| 0.8172 \| 0.2024 \| 0.0588 \| \| 0.0613 \| 87.0 \| 2175 \| 0.4139 \| 0.825 \| 0.2632 \| 1.5201 \| 0.825 \| 0.8138 \| 0.1927 \| 0.0592 \| \| 0.0613 \| 88.0 \| 2200 \| 0.4138 \| 0.83 \| 0.2631 \| 1.5285 \| 0.83 \| 0.8172 \| 0.1897 \| 0.0591 \| \| 0.0613 \| 89.0 \| 2225 \| 0.4137 \| 0.825 \| 0.2631 \| 1.5185 \| 0.825 \| 0.8136 \| 0.1956 \| 0.0593 \| \| 0.0613 \| 90.0 \| 2250 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5212 \| 0.83 \| 0.8172 \| 0.2007 \| 0.0591 \| \| 0.0613 \| 91.0 \| 2275 \| 0.4138 \| 0.825 \| 0.2631 \| 1.5185 \| 0.825 \| 0.8138 \| 0.1915 \| 0.0593 \| \| 0.0613 \| 92.0 \| 2300 \| 0.4136 \| 0.83 \| 0.2630 \| 1.5174 \| 0.83 \| 0.8172 \| 0.2067 \| 0.0590 \| \| 0.0613 \| 93.0 \| 2325 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5204 \| 0.83 \| 0.8172 \| 0.1939 \| 0.0591 \| \| 0.0613 \| 94.0 \| 2350 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5255 \| 0.83 \| 0.8172 \| 0.2007 \| 0.0592 \| \| 0.0613 \| 95.0 \| 2375 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5161 \| 0.83 \| 0.8172 \| 0.1966 \| 0.0591 \| \| 0.0613 \| 96.0 \| 2400 \| 0.4136 \| 0.83 \| 0.2630 \| 1.5180 \| 0.83 \| 0.8172 \| 0.2007 \| 0.0590 \| \| 0.0613 \| 97.0 \| 2425 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5176 \| 0.83 \| 0.8172 \| 0.1966 \| 0.0591 \| \| 0.0613 \| 98.0 \| 2450 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5194 \| 0.83 \| 0.8172 \| 0.1966 \| 0.0590 \| \| 0.0613 \| 99.0 \| 2475 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5195 \| 0.83 \| 0.8172 \| 0.2005 \| 0.0591 \| \| 0.0613 \| 100.0 \| 2500 \| 0.4137 \| 0.83 \| 0.2631 \| 1.5189 \| 0.83 \| 0.8172 \| 0.2007 \| 0.0591 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4858 - Accuracy: 0.845 - Brier Loss: 0.2480 - Nll: 1.6086 - F1 Micro: 0.845 - F1 Macro: 0.8265 - Ece: 0.1885 - Aurc: 0.0556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.8068 \| 0.235 \| 0.8615 \| 4.8212 \| 0.235 \| 0.1982 \| 0.2851 \| 0.7569 \| \| No log \| 2.0 \| 50 \| 1.1592 \| 0.525 \| 0.6018 \| 2.7552 \| 0.525 \| 0.4251 \| 0.2868 \| 0.2632 \| \| No log \| 3.0 \| 75 \| 0.9152 \| 0.66 \| 0.4661 \| 2.3855 \| 0.66 \| 0.5569 \| 0.2425 \| 0.1423 \| \| No log \| 4.0 \| 100 \| 0.7630 \| 0.72 \| 0.3764 \| 1.5145 \| 0.72 \| 0.6868 \| 0.2192 \| 0.1030 \| \| No log \| 5.0 \| 125 \| 0.7141 \| 0.77 \| 0.3437 \| 1.5182 \| 0.7700 \| 0.7351 \| 0.2394 \| 0.0885 \| \| No log \| 6.0 \| 150 \| 0.7497 \| 0.765 \| 0.3420 \| 1.4392 \| 0.765 \| 0.7401 \| 0.2226 \| 0.0850 \| \| No log \| 7.0 \| 175 \| 0.6465 \| 0.8 \| 0.3252 \| 1.4412 \| 0.8000 \| 0.7688 \| 0.2262 \| 0.0878 \| \| No log \| 8.0 \| 200 \| 0.6100 \| 0.79 \| 0.3013 \| 1.1306 \| 0.79 \| 0.7626 \| 0.2135 \| 0.0732 \| \| No log \| 9.0 \| 225 \| 0.7123 \| 0.78 \| 0.3429 \| 1.5207 \| 0.78 \| 0.7679 \| 0.2302 \| 0.1021 \| \| No log \| 10.0 \| 250 \| 0.5881 \| 0.83 \| 0.2873 \| 1.3827 \| 0.83 \| 0.8274 \| 0.2288 \| 0.0503 \| \| No log \| 11.0 \| 275 \| 0.6079 \| 0.805 \| 0.2958 \| 1.3646 \| 0.805 \| 0.7659 \| 0.1826 \| 0.0795 \| \| No log \| 12.0 \| 300 \| 0.5697 \| 0.815 \| 0.2829 \| 1.4925 \| 0.815 \| 0.7846 \| 0.1783 \| 0.0637 \| \| No log \| 13.0 \| 325 \| 0.5641 \| 0.815 \| 0.2834 \| 1.7183 \| 0.815 \| 0.7950 \| 0.1949 \| 0.0587 \| \| No log \| 14.0 \| 350 \| 0.5287 \| 0.815 \| 0.2615 \| 1.1254 \| 0.815 \| 0.7990 \| 0.1903 \| 0.0508 \| \| No log \| 15.0 \| 375 \| 0.5220 \| 0.82 \| 0.2627 \| 1.5375 \| 0.82 \| 0.8024 \| 0.2039 \| 0.0477 \| \| No log \| 16.0 \| 400 \| 0.5982 \| 0.8 \| 0.3044 \| 1.9197 \| 0.8000 \| 0.7762 \| 0.2026 \| 0.0664 \| \| No log \| 17.0 \| 425 \| 0.5165 \| 0.83 \| 0.2653 \| 1.6907 \| 0.83 \| 0.8120 \| 0.2057 \| 0.0505 \| \| No log \| 18.0 \| 450 \| 0.5345 \| 0.84 \| 0.2695 \| 1.8508 \| 0.8400 \| 0.8259 \| 0.1987 \| 0.0505 \| \| No log \| 19.0 \| 475 \| 0.5325 \| 0.83 \| 0.2670 \| 1.3625 \| 0.83 \| 0.8226 \| 0.1937 \| 0.0450 \| \| 0.4632 \| 20.0 \| 500 \| 0.5102 \| 0.835 \| 0.2561 \| 1.4043 \| 0.835 \| 0.8179 \| 0.1957 \| 0.0568 \| \| 0.4632 \| 21.0 \| 525 \| 0.5171 \| 0.83 \| 0.2605 \| 1.6651 \| 0.83 \| 0.8199 \| 0.1834 \| 0.0508 \| \| 0.4632 \| 22.0 \| 550 \| 0.5294 \| 0.825 \| 0.2594 \| 1.6140 \| 0.825 \| 0.7987 \| 0.1745 \| 0.0559 \| \| 0.4632 \| 23.0 \| 575 \| 0.5234 \| 0.835 \| 0.2633 \| 1.7530 \| 0.835 \| 0.8190 \| 0.1937 \| 0.0534 \| \| 0.4632 \| 24.0 \| 600 \| 0.4999 \| 0.845 \| 0.2469 \| 1.4458 \| 0.845 \| 0.8181 \| 0.1650 \| 0.0580 \| \| 0.4632 \| 25.0 \| 625 \| 0.5025 \| 0.825 \| 0.2571 \| 1.6308 \| 0.825 \| 0.8111 \| 0.1712 \| 0.0493 \| \| 0.4632 \| 26.0 \| 650 \| 0.5058 \| 0.83 \| 0.2563 \| 1.5906 \| 0.83 \| 0.8074 \| 0.1828 \| 0.0566 \| \| 0.4632 \| 27.0 \| 675 \| 0.4873 \| 0.835 \| 0.2476 \| 1.4946 \| 0.835 \| 0.8148 \| 0.1727 \| 0.0584 \| \| 0.4632 \| 28.0 \| 700 \| 0.4894 \| 0.835 \| 0.2486 \| 1.6165 \| 0.835 \| 0.8153 \| 0.1538 \| 0.0573 \| \| 0.4632 \| 29.0 \| 725 \| 0.4834 \| 0.835 \| 0.2455 \| 1.6185 \| 0.835 \| 0.8178 \| 0.1869 \| 0.0523 \| \| 0.4632 \| 30.0 \| 750 \| 0.4842 \| 0.835 \| 0.2460 \| 1.4898 \| 0.835 \| 0.8115 \| 0.1683 \| 0.0524 \| \| 0.4632 \| 31.0 \| 775 \| 0.4869 \| 0.835 \| 0.2480 \| 1.5350 \| 0.835 \| 0.8144 \| 0.1796 \| 0.0543 \| \| 0.4632 \| 32.0 \| 800 \| 0.4865 \| 0.835 \| 0.2473 \| 1.6052 \| 0.835 \| 0.8179 \| 0.1792 \| 0.0560 \| \| 0.4632 \| 33.0 \| 825 \| 0.4856 \| 0.83 \| 0.2479 \| 1.5508 \| 0.83 \| 0.8118 \| 0.1773 \| 0.0550 \| \| 0.4632 \| 34.0 \| 850 \| 0.4861 \| 0.835 \| 0.2477 \| 1.5486 \| 0.835 \| 0.8144 \| 0.1779 \| 0.0568 \| \| 0.4632 \| 35.0 \| 875 \| 0.4873 \| 0.835 \| 0.2484 \| 1.6076 \| 0.835 \| 0.8179 \| 0.1803 \| 0.0556 \| \| 0.4632 \| 36.0 \| 900 \| 0.4867 \| 0.84 \| 0.2480 \| 1.5438 \| 0.8400 \| 0.8205 \| 0.1791 \| 0.0561 \| \| 0.4632 \| 37.0 \| 925 \| 0.4863 \| 0.84 \| 0.2480 \| 1.6035 \| 0.8400 \| 0.8205 \| 0.1768 \| 0.0558 \| \| 0.4632 \| 38.0 \| 950 \| 0.4864 \| 0.835 \| 0.2479 \| 1.5469 \| 0.835 \| 0.8144 \| 0.1876 \| 0.0563 \| \| 0.4632 \| 39.0 \| 975 \| 0.4866 \| 0.835 \| 0.2483 \| 1.5996 \| 0.835 \| 0.8144 \| 0.1759 \| 0.0546 \| \| 0.0757 \| 40.0 \| 1000 \| 0.4869 \| 0.84 \| 0.2480 \| 1.5412 \| 0.8400 \| 0.8205 \| 0.1726 \| 0.0550 \| \| 0.0757 \| 41.0 \| 1025 \| 0.4869 \| 0.84 \| 0.2482 \| 1.6060 \| 0.8400 \| 0.8205 \| 0.1848 \| 0.0545 \| \| 0.0757 \| 42.0 \| 1050 \| 0.4872 \| 0.835 \| 0.2485 \| 1.6080 \| 0.835 \| 0.8179 \| 0.1812 \| 0.0558 \| \| 0.0757 \| 43.0 \| 1075 \| 0.4863 \| 0.84 \| 0.2476 \| 1.6046 \| 0.8400 \| 0.8205 \| 0.1773 \| 0.0538 \| \| 0.0757 \| 44.0 \| 1100 \| 0.4866 \| 0.84 \| 0.2482 \| 1.6117 \| 0.8400 \| 0.8205 \| 0.1719 \| 0.0557 \| \| 0.0757 \| 45.0 \| 1125 \| 0.4858 \| 0.835 \| 0.2478 \| 1.6046 \| 0.835 \| 0.8144 \| 0.1728 \| 0.0554 \| \| 0.0757 \| 46.0 \| 1150 \| 0.4860 \| 0.845 \| 0.2478 \| 1.6034 \| 0.845 \| 0.8265 \| 0.2000 \| 0.0551 \| \| 0.0757 \| 47.0 \| 1175 \| 0.4863 \| 0.845 \| 0.2480 \| 1.5422 \| 0.845 \| 0.8265 \| 0.1918 \| 0.0560 \| \| 0.0757 \| 48.0 \| 1200 \| 0.4871 \| 0.84 \| 0.2486 \| 1.5468 \| 0.8400 \| 0.8205 \| 0.1795 \| 0.0558 \| \| 0.0757 \| 49.0 \| 1225 \| 0.4855 \| 0.835 \| 0.2475 \| 1.5511 \| 0.835 \| 0.8144 \| 0.1814 \| 0.0555 \| \| 0.0757 \| 50.0 \| 1250 \| 0.4854 \| 0.84 \| 0.2477 \| 1.5488 \| 0.8400 \| 0.8205 \| 0.1830 \| 0.0549 \| \| 0.0757 \| 51.0 \| 1275 \| 0.4854 \| 0.835 \| 0.2475 \| 1.5505 \| 0.835 \| 0.8144 \| 0.1879 \| 0.0547 \| \| 0.0757 \| 52.0 \| 1300 \| 0.4867 \| 0.845 \| 0.2483 \| 1.6045 \| 0.845 \| 0.8265 \| 0.1700 \| 0.0560 \| \| 0.0757 \| 53.0 \| 1325 \| 0.4849 \| 0.835 \| 0.2473 \| 1.6070 \| 0.835 \| 0.8179 \| 0.1736 \| 0.0546 \| \| 0.0757 \| 54.0 \| 1350 \| 0.4860 \| 0.845 \| 0.2479 \| 1.5524 \| 0.845 \| 0.8265 \| 0.1762 \| 0.0549 \| \| 0.0757 \| 55.0 \| 1375 \| 0.4863 \| 0.84 \| 0.2481 \| 1.5535 \| 0.8400 \| 0.8205 \| 0.1864 \| 0.0560 \| \| 0.0757 \| 56.0 \| 1400 \| 0.4871 \| 0.84 \| 0.2484 \| 1.6074 \| 0.8400 \| 0.8205 \| 0.1821 \| 0.0562 \| \| 0.0757 \| 57.0 \| 1425 \| 0.4868 \| 0.845 \| 0.2483 \| 1.5544 \| 0.845 \| 0.8265 \| 0.1832 \| 0.0557 \| \| 0.0757 \| 58.0 \| 1450 \| 0.4848 \| 0.845 \| 0.2474 \| 1.5455 \| 0.845 \| 0.8265 \| 0.1770 \| 0.0551 \| \| 0.0757 \| 59.0 \| 1475 \| 0.4861 \| 0.845 \| 0.2481 \| 1.6056 \| 0.845 \| 0.8265 \| 0.1824 \| 0.0552 \| \| 0.0718 \| 60.0 \| 1500 \| 0.4860 \| 0.845 \| 0.2481 \| 1.5485 \| 0.845 \| 0.8265 \| 0.1874 \| 0.0555 \| \| 0.0718 \| 61.0 \| 1525 \| 0.4859 \| 0.845 \| 0.2480 \| 1.5471 \| 0.845 \| 0.8265 \| 0.1833 \| 0.0545 \| \| 0.0718 \| 62.0 \| 1550 \| 0.4855 \| 0.84 \| 0.2477 \| 1.5557 \| 0.8400 \| 0.8205 \| 0.1892 \| 0.0556 \| \| 0.0718 \| 63.0 \| 1575 \| 0.4856 \| 0.845 \| 0.2479 \| 1.5511 \| 0.845 \| 0.8265 \| 0.1804 \| 0.0555 \| \| 0.0718 \| 64.0 \| 1600 \| 0.4863 \| 0.845 \| 0.2482 \| 1.5482 \| 0.845 \| 0.8265 \| 0.1761 \| 0.0559 \| \| 0.0718 \| 65.0 \| 1625 \| 0.4857 \| 0.845 \| 0.2478 \| 1.5503 \| 0.845 \| 0.8265 \| 0.1944 \| 0.0559 \| \| 0.0718 \| 66.0 \| 1650 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5463 \| 0.845 \| 0.8265 \| 0.1832 \| 0.0556 \| \| 0.0718 \| 67.0 \| 1675 \| 0.4855 \| 0.845 \| 0.2478 \| 1.5493 \| 0.845 \| 0.8265 \| 0.1947 \| 0.0558 \| \| 0.0718 \| 68.0 \| 1700 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5482 \| 0.845 \| 0.8265 \| 0.1831 \| 0.0553 \| \| 0.0718 \| 69.0 \| 1725 \| 0.4863 \| 0.845 \| 0.2483 \| 1.5470 \| 0.845 \| 0.8265 \| 0.1904 \| 0.0558 \| \| 0.0718 \| 70.0 \| 1750 \| 0.4859 \| 0.845 \| 0.2479 \| 1.5449 \| 0.845 \| 0.8265 \| 0.1836 \| 0.0556 \| \| 0.0718 \| 71.0 \| 1775 \| 0.4865 \| 0.845 \| 0.2484 \| 1.5476 \| 0.845 \| 0.8265 \| 0.1999 \| 0.0557 \| \| 0.0718 \| 72.0 \| 1800 \| 0.4855 \| 0.845 \| 0.2479 \| 1.5472 \| 0.845 \| 0.8265 \| 0.1875 \| 0.0555 \| \| 0.0718 \| 73.0 \| 1825 \| 0.4859 \| 0.845 \| 0.2481 \| 1.5514 \| 0.845 \| 0.8265 \| 0.1946 \| 0.0557 \| \| 0.0718 \| 74.0 \| 1850 \| 0.4863 \| 0.845 \| 0.2483 \| 1.6121 \| 0.845 \| 0.8265 \| 0.1904 \| 0.0559 \| \| 0.0718 \| 75.0 \| 1875 \| 0.4861 \| 0.845 \| 0.2482 \| 1.6150 \| 0.845 \| 0.8265 \| 0.1807 \| 0.0559 \| \| 0.0718 \| 76.0 \| 1900 \| 0.4856 \| 0.845 \| 0.2479 \| 1.5755 \| 0.845 \| 0.8265 \| 0.1877 \| 0.0555 \| \| 0.0718 \| 77.0 \| 1925 \| 0.4857 \| 0.845 \| 0.2480 \| 1.5550 \| 0.845 \| 0.8265 \| 0.1880 \| 0.0557 \| \| 0.0718 \| 78.0 \| 1950 \| 0.4856 \| 0.845 \| 0.2479 \| 1.6081 \| 0.845 \| 0.8265 \| 0.1877 \| 0.0553 \| \| 0.0718 \| 79.0 \| 1975 \| 0.4858 \| 0.845 \| 0.2481 \| 1.5526 \| 0.845 \| 0.8265 \| 0.1878 \| 0.0556 \| \| 0.0718 \| 80.0 \| 2000 \| 0.4860 \| 0.845 \| 0.2481 \| 1.5562 \| 0.845 \| 0.8265 \| 0.1881 \| 0.0556 \| \| 0.0718 \| 81.0 \| 2025 \| 0.4860 \| 0.845 \| 0.2481 \| 1.5569 \| 0.845 \| 0.8265 \| 0.1837 \| 0.0553 \| \| 0.0718 \| 82.0 \| 2050 \| 0.4863 \| 0.845 \| 0.2483 \| 1.5522 \| 0.845 \| 0.8265 \| 0.1937 \| 0.0558 \| \| 0.0718 \| 83.0 \| 2075 \| 0.4859 \| 0.845 \| 0.2481 \| 1.5539 \| 0.845 \| 0.8265 \| 0.1878 \| 0.0558 \| \| 0.0718 \| 84.0 \| 2100 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5575 \| 0.845 \| 0.8265 \| 0.1877 \| 0.0557 \| \| 0.0718 \| 85.0 \| 2125 \| 0.4859 \| 0.845 \| 0.2481 \| 1.5652 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0558 \| \| 0.0718 \| 86.0 \| 2150 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5511 \| 0.845 \| 0.8265 \| 0.1840 \| 0.0555 \| \| 0.0718 \| 87.0 \| 2175 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5571 \| 0.845 \| 0.8265 \| 0.1883 \| 0.0556 \| \| 0.0718 \| 88.0 \| 2200 \| 0.4859 \| 0.845 \| 0.2481 \| 1.5554 \| 0.845 \| 0.8265 \| 0.1842 \| 0.0556 \| \| 0.0718 \| 89.0 \| 2225 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5627 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0557 \| \| 0.0718 \| 90.0 \| 2250 \| 0.4856 \| 0.845 \| 0.2479 \| 1.6086 \| 0.845 \| 0.8265 \| 0.1883 \| 0.0557 \| \| 0.0718 \| 91.0 \| 2275 \| 0.4859 \| 0.845 \| 0.2481 \| 1.5552 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0558 \| \| 0.0718 \| 92.0 \| 2300 \| 0.4857 \| 0.845 \| 0.2479 \| 1.6085 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0557 \| \| 0.0718 \| 93.0 \| 2325 \| 0.4858 \| 0.845 \| 0.2480 \| 1.5633 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0556 \| \| 0.0718 \| 94.0 \| 2350 \| 0.4856 \| 0.845 \| 0.2480 \| 1.6085 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0556 \| \| 0.0718 \| 95.0 \| 2375 \| 0.4857 \| 0.845 \| 0.2480 \| 1.6082 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0556 \| \| 0.0718 \| 96.0 \| 2400 \| 0.4857 \| 0.845 \| 0.2480 \| 1.6088 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0556 \| \| 0.0718 \| 97.0 \| 2425 \| 0.4858 \| 0.845 \| 0.2480 \| 1.6082 \| 0.845 \| 0.8265 \| 0.1885 \| 0.0556 \| \| 0.0718 \| 98.0 \| 2450 \| 0.4857 \| 0.845 \| 0.2480 \| 1.6084 \| 0.845 \| 0.8265 \| 0.1884 \| 0.0556 \| \| 0.0718 \| 99.0 \| 2475 \| 0.4858 \| 0.845 \| 0.2480 \| 1.6086 \| 0.845 \| 0.8265 \| 0.1885 \| 0.0556 \| \| 0.0718 \| 100.0 \| 2500 \| 0.4858 \| 0.845 \| 0.2480 \| 1.6086 \| 0.845 \| 0.8265 \| 0.1885 \| 0.0556 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5528 - Accuracy: 0.84 - Brier Loss: 0.2493 - Nll: 1.6062 - F1 Micro: 0.8400 - F1 Macro: 0.8256 - Ece: 0.1626 - Aurc: 0.0556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.9962 \| 0.24 \| 0.8634 \| 4.6099 \| 0.24 \| 0.1987 \| 0.2924 \| 0.7628 \| \| No log \| 2.0 \| 50 \| 1.2785 \| 0.545 \| 0.5960 \| 2.6707 \| 0.545 \| 0.4456 \| 0.2808 \| 0.2569 \| \| No log \| 3.0 \| 75 \| 0.9740 \| 0.685 \| 0.4455 \| 2.0688 \| 0.685 \| 0.5937 \| 0.2231 \| 0.1314 \| \| No log \| 4.0 \| 100 \| 0.8052 \| 0.75 \| 0.3628 \| 1.5271 \| 0.75 \| 0.7144 \| 0.2031 \| 0.0915 \| \| No log \| 5.0 \| 125 \| 0.7531 \| 0.77 \| 0.3324 \| 1.5448 \| 0.7700 \| 0.7348 \| 0.1868 \| 0.0829 \| \| No log \| 6.0 \| 150 \| 0.9730 \| 0.735 \| 0.4050 \| 1.5875 \| 0.735 \| 0.7229 \| 0.1899 \| 0.1118 \| \| No log \| 7.0 \| 175 \| 0.6437 \| 0.83 \| 0.2790 \| 1.3915 \| 0.83 \| 0.7996 \| 0.1967 \| 0.0612 \| \| No log \| 8.0 \| 200 \| 0.6670 \| 0.78 \| 0.2984 \| 1.2128 \| 0.78 \| 0.7429 \| 0.1701 \| 0.0716 \| \| No log \| 9.0 \| 225 \| 0.6012 \| 0.845 \| 0.2521 \| 1.4007 \| 0.845 \| 0.8208 \| 0.1573 \| 0.0581 \| \| No log \| 10.0 \| 250 \| 0.6754 \| 0.795 \| 0.3063 \| 1.4194 \| 0.795 \| 0.7638 \| 0.2036 \| 0.0759 \| \| No log \| 11.0 \| 275 \| 0.5950 \| 0.83 \| 0.2554 \| 1.1558 \| 0.83 \| 0.8053 \| 0.1877 \| 0.0529 \| \| No log \| 12.0 \| 300 \| 0.7061 \| 0.79 \| 0.3153 \| 1.6781 \| 0.79 \| 0.7676 \| 0.1879 \| 0.0848 \| \| No log \| 13.0 \| 325 \| 0.6349 \| 0.805 \| 0.2806 \| 1.3102 \| 0.805 \| 0.7767 \| 0.1523 \| 0.0667 \| \| No log \| 14.0 \| 350 \| 0.5973 \| 0.82 \| 0.2677 \| 1.5498 \| 0.82 \| 0.8020 \| 0.1734 \| 0.0567 \| \| No log \| 15.0 \| 375 \| 0.6186 \| 0.825 \| 0.2792 \| 1.3816 \| 0.825 \| 0.8170 \| 0.1558 \| 0.0672 \| \| No log \| 16.0 \| 400 \| 0.5694 \| 0.815 \| 0.2662 \| 1.1759 \| 0.815 \| 0.7962 \| 0.1675 \| 0.0559 \| \| No log \| 17.0 \| 425 \| 0.5993 \| 0.825 \| 0.2793 \| 1.2547 \| 0.825 \| 0.8112 \| 0.1822 \| 0.0647 \| \| No log \| 18.0 \| 450 \| 0.6333 \| 0.815 \| 0.2844 \| 1.6540 \| 0.815 \| 0.8024 \| 0.1562 \| 0.0622 \| \| No log \| 19.0 \| 475 \| 0.5208 \| 0.845 \| 0.2349 \| 1.2879 \| 0.845 \| 0.8155 \| 0.1553 \| 0.0494 \| \| 0.4544 \| 20.0 \| 500 \| 0.5412 \| 0.86 \| 0.2438 \| 1.6726 \| 0.8600 \| 0.8465 \| 0.1531 \| 0.0485 \| \| 0.4544 \| 21.0 \| 525 \| 0.6171 \| 0.825 \| 0.2775 \| 1.9997 \| 0.825 \| 0.8183 \| 0.1464 \| 0.0549 \| \| 0.4544 \| 22.0 \| 550 \| 0.5479 \| 0.84 \| 0.2447 \| 1.5015 \| 0.8400 \| 0.8263 \| 0.1481 \| 0.0680 \| \| 0.4544 \| 23.0 \| 575 \| 0.5508 \| 0.835 \| 0.2491 \| 1.8095 \| 0.835 \| 0.8209 \| 0.1616 \| 0.0469 \| \| 0.4544 \| 24.0 \| 600 \| 0.5597 \| 0.825 \| 0.2577 \| 1.6676 \| 0.825 \| 0.8077 \| 0.1572 \| 0.0486 \| \| 0.4544 \| 25.0 \| 625 \| 0.5505 \| 0.835 \| 0.2535 \| 1.6085 \| 0.835 \| 0.8166 \| 0.1664 \| 0.0524 \| \| 0.4544 \| 26.0 \| 650 \| 0.5347 \| 0.84 \| 0.2442 \| 1.4694 \| 0.8400 \| 0.8288 \| 0.1825 \| 0.0505 \| \| 0.4544 \| 27.0 \| 675 \| 0.5333 \| 0.84 \| 0.2418 \| 1.5809 \| 0.8400 \| 0.8280 \| 0.1634 \| 0.0521 \| \| 0.4544 \| 28.0 \| 700 \| 0.5417 \| 0.84 \| 0.2471 \| 1.5289 \| 0.8400 \| 0.8231 \| 0.1500 \| 0.0503 \| \| 0.4544 \| 29.0 \| 725 \| 0.5369 \| 0.845 \| 0.2434 \| 1.5333 \| 0.845 \| 0.8318 \| 0.1690 \| 0.0523 \| \| 0.4544 \| 30.0 \| 750 \| 0.5396 \| 0.84 \| 0.2448 \| 1.5269 \| 0.8400 \| 0.8260 \| 0.1689 \| 0.0534 \| \| 0.4544 \| 31.0 \| 775 \| 0.5411 \| 0.845 \| 0.2459 \| 1.5325 \| 0.845 \| 0.8289 \| 0.1524 \| 0.0514 \| \| 0.4544 \| 32.0 \| 800 \| 0.5429 \| 0.845 \| 0.2456 \| 1.5239 \| 0.845 \| 0.8318 \| 0.1550 \| 0.0527 \| \| 0.4544 \| 33.0 \| 825 \| 0.5445 \| 0.84 \| 0.2468 \| 1.5275 \| 0.8400 \| 0.8231 \| 0.1626 \| 0.0535 \| \| 0.4544 \| 34.0 \| 850 \| 0.5432 \| 0.845 \| 0.2461 \| 1.5210 \| 0.845 \| 0.8289 \| 0.1557 \| 0.0533 \| \| 0.4544 \| 35.0 \| 875 \| 0.5438 \| 0.845 \| 0.2459 \| 1.5269 \| 0.845 \| 0.8318 \| 0.1564 \| 0.0533 \| \| 0.4544 \| 36.0 \| 900 \| 0.5451 \| 0.845 \| 0.2466 \| 1.5262 \| 0.845 \| 0.8289 \| 0.1610 \| 0.0541 \| \| 0.4544 \| 37.0 \| 925 \| 0.5415 \| 0.85 \| 0.2448 \| 1.5254 \| 0.85 \| 0.8348 \| 0.1667 \| 0.0528 \| \| 0.4544 \| 38.0 \| 950 \| 0.5447 \| 0.845 \| 0.2461 \| 1.5367 \| 0.845 \| 0.8318 \| 0.1519 \| 0.0535 \| \| 0.4544 \| 39.0 \| 975 \| 0.5437 \| 0.85 \| 0.2454 \| 1.5223 \| 0.85 \| 0.8348 \| 0.1605 \| 0.0536 \| \| 0.0607 \| 40.0 \| 1000 \| 0.5445 \| 0.845 \| 0.2460 \| 1.5252 \| 0.845 \| 0.8318 \| 0.1610 \| 0.0539 \| \| 0.0607 \| 41.0 \| 1025 \| 0.5460 \| 0.845 \| 0.2465 \| 1.5925 \| 0.845 \| 0.8318 \| 0.1416 \| 0.0541 \| \| 0.0607 \| 42.0 \| 1050 \| 0.5466 \| 0.84 \| 0.2467 \| 1.5304 \| 0.8400 \| 0.8260 \| 0.1555 \| 0.0542 \| \| 0.0607 \| 43.0 \| 1075 \| 0.5458 \| 0.84 \| 0.2464 \| 1.5272 \| 0.8400 \| 0.8231 \| 0.1633 \| 0.0539 \| \| 0.0607 \| 44.0 \| 1100 \| 0.5460 \| 0.85 \| 0.2464 \| 1.5459 \| 0.85 \| 0.8377 \| 0.1534 \| 0.0550 \| \| 0.0607 \| 45.0 \| 1125 \| 0.5464 \| 0.85 \| 0.2465 \| 1.5390 \| 0.85 \| 0.8377 \| 0.1471 \| 0.0544 \| \| 0.0607 \| 46.0 \| 1150 \| 0.5462 \| 0.85 \| 0.2465 \| 1.5972 \| 0.85 \| 0.8377 \| 0.1549 \| 0.0540 \| \| 0.0607 \| 47.0 \| 1175 \| 0.5475 \| 0.85 \| 0.2472 \| 1.5910 \| 0.85 \| 0.8377 \| 0.1592 \| 0.0546 \| \| 0.0607 \| 48.0 \| 1200 \| 0.5482 \| 0.845 \| 0.2475 \| 1.5943 \| 0.845 \| 0.8294 \| 0.1548 \| 0.0545 \| \| 0.0607 \| 49.0 \| 1225 \| 0.5475 \| 0.845 \| 0.2471 \| 1.5922 \| 0.845 \| 0.8294 \| 0.1534 \| 0.0545 \| \| 0.0607 \| 50.0 \| 1250 \| 0.5476 \| 0.85 \| 0.2470 \| 1.5908 \| 0.85 \| 0.8377 \| 0.1539 \| 0.0545 \| \| 0.0607 \| 51.0 \| 1275 \| 0.5480 \| 0.845 \| 0.2471 \| 1.5990 \| 0.845 \| 0.8322 \| 0.1545 \| 0.0547 \| \| 0.0607 \| 52.0 \| 1300 \| 0.5479 \| 0.85 \| 0.2469 \| 1.5917 \| 0.85 \| 0.8348 \| 0.1688 \| 0.0547 \| \| 0.0607 \| 53.0 \| 1325 \| 0.5479 \| 0.845 \| 0.2472 \| 1.6052 \| 0.845 \| 0.8322 \| 0.1545 \| 0.0543 \| \| 0.0607 \| 54.0 \| 1350 \| 0.5490 \| 0.85 \| 0.2477 \| 1.5948 \| 0.85 \| 0.8348 \| 0.1610 \| 0.0545 \| \| 0.0607 \| 55.0 \| 1375 \| 0.5489 \| 0.85 \| 0.2474 \| 1.5967 \| 0.85 \| 0.8377 \| 0.1543 \| 0.0560 \| \| 0.0607 \| 56.0 \| 1400 \| 0.5499 \| 0.845 \| 0.2480 \| 1.5939 \| 0.845 \| 0.8294 \| 0.1561 \| 0.0549 \| \| 0.0607 \| 57.0 \| 1425 \| 0.5492 \| 0.845 \| 0.2476 \| 1.6048 \| 0.845 \| 0.8322 \| 0.1570 \| 0.0549 \| \| 0.0607 \| 58.0 \| 1450 \| 0.5497 \| 0.845 \| 0.2478 \| 1.6004 \| 0.845 \| 0.8322 \| 0.1724 \| 0.0548 \| \| 0.0607 \| 59.0 \| 1475 \| 0.5496 \| 0.85 \| 0.2477 \| 1.5982 \| 0.85 \| 0.8377 \| 0.1634 \| 0.0546 \| \| 0.0589 \| 60.0 \| 1500 \| 0.5497 \| 0.845 \| 0.2478 \| 1.5969 \| 0.845 \| 0.8322 \| 0.1592 \| 0.0545 \| \| 0.0589 \| 61.0 \| 1525 \| 0.5492 \| 0.85 \| 0.2476 \| 1.6095 \| 0.85 \| 0.8377 \| 0.1630 \| 0.0547 \| \| 0.0589 \| 62.0 \| 1550 \| 0.5507 \| 0.845 \| 0.2483 \| 1.6060 \| 0.845 \| 0.8322 \| 0.1649 \| 0.0554 \| \| 0.0589 \| 63.0 \| 1575 \| 0.5490 \| 0.845 \| 0.2474 \| 1.6021 \| 0.845 \| 0.8322 \| 0.1635 \| 0.0546 \| \| 0.0589 \| 64.0 \| 1600 \| 0.5508 \| 0.845 \| 0.2483 \| 1.5970 \| 0.845 \| 0.8294 \| 0.1697 \| 0.0552 \| \| 0.0589 \| 65.0 \| 1625 \| 0.5505 \| 0.84 \| 0.2483 \| 1.6023 \| 0.8400 \| 0.8256 \| 0.1658 \| 0.0553 \| \| 0.0589 \| 66.0 \| 1650 \| 0.5503 \| 0.845 \| 0.2481 \| 1.6032 \| 0.845 \| 0.8322 \| 0.1637 \| 0.0546 \| \| 0.0589 \| 67.0 \| 1675 \| 0.5514 \| 0.84 \| 0.2486 \| 1.6000 \| 0.8400 \| 0.8227 \| 0.1649 \| 0.0559 \| \| 0.0589 \| 68.0 \| 1700 \| 0.5516 \| 0.84 \| 0.2487 \| 1.5979 \| 0.8400 \| 0.8227 \| 0.1649 \| 0.0550 \| \| 0.0589 \| 69.0 \| 1725 \| 0.5510 \| 0.84 \| 0.2485 \| 1.6005 \| 0.8400 \| 0.8256 \| 0.1639 \| 0.0548 \| \| 0.0589 \| 70.0 \| 1750 \| 0.5510 \| 0.84 \| 0.2484 \| 1.5990 \| 0.8400 \| 0.8256 \| 0.1653 \| 0.0549 \| \| 0.0589 \| 71.0 \| 1775 \| 0.5517 \| 0.84 \| 0.2487 \| 1.6080 \| 0.8400 \| 0.8256 \| 0.1640 \| 0.0558 \| \| 0.0589 \| 72.0 \| 1800 \| 0.5525 \| 0.84 \| 0.2491 \| 1.6069 \| 0.8400 \| 0.8227 \| 0.1669 \| 0.0558 \| \| 0.0589 \| 73.0 \| 1825 \| 0.5519 \| 0.84 \| 0.2488 \| 1.6147 \| 0.8400 \| 0.8256 \| 0.1638 \| 0.0554 \| \| 0.0589 \| 74.0 \| 1850 \| 0.5519 \| 0.84 \| 0.2487 \| 1.6027 \| 0.8400 \| 0.8256 \| 0.1657 \| 0.0558 \| \| 0.0589 \| 75.0 \| 1875 \| 0.5522 \| 0.84 \| 0.2490 \| 1.6082 \| 0.8400 \| 0.8256 \| 0.1717 \| 0.0556 \| \| 0.0589 \| 76.0 \| 1900 \| 0.5523 \| 0.84 \| 0.2489 \| 1.6022 \| 0.8400 \| 0.8256 \| 0.1645 \| 0.0553 \| \| 0.0589 \| 77.0 \| 1925 \| 0.5514 \| 0.84 \| 0.2486 \| 1.6027 \| 0.8400 \| 0.8256 \| 0.1635 \| 0.0551 \| \| 0.0589 \| 78.0 \| 1950 \| 0.5518 \| 0.84 \| 0.2488 \| 1.6007 \| 0.8400 \| 0.8256 \| 0.1641 \| 0.0556 \| \| 0.0589 \| 79.0 \| 1975 \| 0.5522 \| 0.84 \| 0.2490 \| 1.6057 \| 0.8400 \| 0.8256 \| 0.1637 \| 0.0556 \| \| 0.0588 \| 80.0 \| 2000 \| 0.5520 \| 0.84 \| 0.2489 \| 1.6110 \| 0.8400 \| 0.8256 \| 0.1658 \| 0.0552 \| \| 0.0588 \| 81.0 \| 2025 \| 0.5521 \| 0.84 \| 0.2489 \| 1.6047 \| 0.8400 \| 0.8256 \| 0.1659 \| 0.0555 \| \| 0.0588 \| 82.0 \| 2050 \| 0.5521 \| 0.84 \| 0.2490 \| 1.6015 \| 0.8400 \| 0.8256 \| 0.1635 \| 0.0551 \| \| 0.0588 \| 83.0 \| 2075 \| 0.5521 \| 0.84 \| 0.2489 \| 1.6115 \| 0.8400 \| 0.8256 \| 0.1637 \| 0.0553 \| \| 0.0588 \| 84.0 \| 2100 \| 0.5523 \| 0.84 \| 0.2490 \| 1.6033 \| 0.8400 \| 0.8256 \| 0.1738 \| 0.0553 \| \| 0.0588 \| 85.0 \| 2125 \| 0.5525 \| 0.84 \| 0.2491 \| 1.6072 \| 0.8400 \| 0.8256 \| 0.1658 \| 0.0555 \| \| 0.0588 \| 86.0 \| 2150 \| 0.5521 \| 0.84 \| 0.2489 \| 1.6057 \| 0.8400 \| 0.8256 \| 0.1574 \| 0.0553 \| \| 0.0588 \| 87.0 \| 2175 \| 0.5527 \| 0.84 \| 0.2492 \| 1.6605 \| 0.8400 \| 0.8256 \| 0.1610 \| 0.0555 \| \| 0.0588 \| 88.0 \| 2200 \| 0.5526 \| 0.84 \| 0.2491 \| 1.6056 \| 0.8400 \| 0.8256 \| 0.1544 \| 0.0556 \| \| 0.0588 \| 89.0 \| 2225 \| 0.5527 \| 0.84 \| 0.2492 \| 1.6126 \| 0.8400 \| 0.8256 \| 0.1547 \| 0.0556 \| \| 0.0588 \| 90.0 \| 2250 \| 0.5525 \| 0.84 \| 0.2491 \| 1.6059 \| 0.8400 \| 0.8256 \| 0.1525 \| 0.0556 \| \| 0.0588 \| 91.0 \| 2275 \| 0.5528 \| 0.84 \| 0.2492 \| 1.6060 \| 0.8400 \| 0.8256 \| 0.1604 \| 0.0556 \| \| 0.0588 \| 92.0 \| 2300 \| 0.5526 \| 0.84 \| 0.2491 \| 1.6080 \| 0.8400 \| 0.8256 \| 0.1525 \| 0.0555 \| \| 0.0588 \| 93.0 \| 2325 \| 0.5527 \| 0.84 \| 0.2492 \| 1.6034 \| 0.8400 \| 0.8256 \| 0.1547 \| 0.0556 \| \| 0.0588 \| 94.0 \| 2350 \| 0.5526 \| 0.84 \| 0.2492 \| 1.6040 \| 0.8400 \| 0.8256 \| 0.1673 \| 0.0555 \| \| 0.0588 \| 95.0 \| 2375 \| 0.5529 \| 0.84 \| 0.2493 \| 1.6053 \| 0.8400 \| 0.8256 \| 0.1545 \| 0.0556 \| \| 0.0588 \| 96.0 \| 2400 \| 0.5526 \| 0.84 \| 0.2492 \| 1.6050 \| 0.8400 \| 0.8256 \| 0.1626 \| 0.0555 \| \| 0.0588 \| 97.0 \| 2425 \| 0.5528 \| 0.84 \| 0.2492 \| 1.6040 \| 0.8400 \| 0.8256 \| 0.1686 \| 0.0557 \| \| 0.0588 \| 98.0 \| 2450 \| 0.5528 \| 0.84 \| 0.2492 \| 1.6068 \| 0.8400 \| 0.8256 \| 0.1626 \| 0.0555 \| \| 0.0588 \| 99.0 \| 2475 \| 0.5528 \| 0.84 \| 0.2492 \| 1.6065 \| 0.8400 \| 0.8256 \| 0.1626 \| 0.0556 \| \| 0.0588 \| 100.0 \| 2500 \| 0.5528 \| 0.84 \| 0.2493 \| 1.6062 \| 0.8400 \| 0.8256 \| 0.1626 \| 0.0556 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3932 - Accuracy: 0.83 - Brier Loss: 0.2507 - Nll: 1.3117 - F1 Micro: 0.83 - F1 Macro: 0.8164 - Ece: 0.1915 - Aurc: 0.0602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.5453 \| 0.21 \| 0.8721 \| 5.2626 \| 0.2100 \| 0.1784 \| 0.2602 \| 0.7492 \| \| No log \| 2.0 \| 50 \| 0.9799 \| 0.515 \| 0.6132 \| 2.7421 \| 0.515 \| 0.4183 \| 0.2927 \| 0.2698 \| \| No log \| 3.0 \| 75 \| 0.7948 \| 0.655 \| 0.4888 \| 2.6042 \| 0.655 \| 0.5597 \| 0.2331 \| 0.1485 \| \| No log \| 4.0 \| 100 \| 0.6422 \| 0.735 \| 0.3906 \| 1.6168 \| 0.735 \| 0.6662 \| 0.2448 \| 0.1095 \| \| No log \| 5.0 \| 125 \| 0.6261 \| 0.785 \| 0.3475 \| 1.1890 \| 0.785 \| 0.7582 \| 0.2616 \| 0.0792 \| \| No log \| 6.0 \| 150 \| 0.6060 \| 0.775 \| 0.3402 \| 1.3317 \| 0.775 \| 0.7269 \| 0.2750 \| 0.0709 \| \| No log \| 7.0 \| 175 \| 0.5659 \| 0.8 \| 0.3459 \| 1.4773 \| 0.8000 \| 0.7741 \| 0.2628 \| 0.0960 \| \| No log \| 8.0 \| 200 \| 0.5339 \| 0.81 \| 0.3038 \| 1.5029 \| 0.81 \| 0.7882 \| 0.2449 \| 0.0699 \| \| No log \| 9.0 \| 225 \| 0.5429 \| 0.805 \| 0.3117 \| 1.4140 \| 0.805 \| 0.7840 \| 0.2242 \| 0.0771 \| \| No log \| 10.0 \| 250 \| 0.5337 \| 0.815 \| 0.3139 \| 1.4630 \| 0.815 \| 0.8167 \| 0.2253 \| 0.0801 \| \| No log \| 11.0 \| 275 \| 0.5257 \| 0.815 \| 0.3084 \| 1.4325 \| 0.815 \| 0.7943 \| 0.2431 \| 0.0823 \| \| No log \| 12.0 \| 300 \| 0.4704 \| 0.81 \| 0.2879 \| 1.3557 \| 0.81 \| 0.7859 \| 0.2139 \| 0.0770 \| \| No log \| 13.0 \| 325 \| 0.4828 \| 0.81 \| 0.2898 \| 1.5643 \| 0.81 \| 0.7767 \| 0.2115 \| 0.0712 \| \| No log \| 14.0 \| 350 \| 0.4579 \| 0.815 \| 0.2733 \| 1.4403 \| 0.815 \| 0.8061 \| 0.1799 \| 0.0609 \| \| No log \| 15.0 \| 375 \| 0.4642 \| 0.815 \| 0.2892 \| 1.4598 \| 0.815 \| 0.8017 \| 0.1973 \| 0.0537 \| \| No log \| 16.0 \| 400 \| 0.4378 \| 0.84 \| 0.2683 \| 1.1278 \| 0.8400 \| 0.8320 \| 0.2135 \| 0.0545 \| \| No log \| 17.0 \| 425 \| 0.4403 \| 0.825 \| 0.2750 \| 1.3817 \| 0.825 \| 0.8024 \| 0.1898 \| 0.0511 \| \| No log \| 18.0 \| 450 \| 0.4211 \| 0.825 \| 0.2646 \| 1.5604 \| 0.825 \| 0.8123 \| 0.1918 \| 0.0538 \| \| No log \| 19.0 \| 475 \| 0.4280 \| 0.82 \| 0.2700 \| 1.5824 \| 0.82 \| 0.8016 \| 0.1814 \| 0.0587 \| \| 0.4177 \| 20.0 \| 500 \| 0.4223 \| 0.83 \| 0.2649 \| 1.8744 \| 0.83 \| 0.8152 \| 0.1885 \| 0.0681 \| \| 0.4177 \| 21.0 \| 525 \| 0.4219 \| 0.835 \| 0.2682 \| 1.6053 \| 0.835 \| 0.8252 \| 0.1932 \| 0.0600 \| \| 0.4177 \| 22.0 \| 550 \| 0.3935 \| 0.835 \| 0.2534 \| 1.4134 \| 0.835 \| 0.8229 \| 0.2049 \| 0.0680 \| \| 0.4177 \| 23.0 \| 575 \| 0.4231 \| 0.82 \| 0.2651 \| 1.7267 \| 0.82 \| 0.8028 \| 0.1943 \| 0.0636 \| \| 0.4177 \| 24.0 \| 600 \| 0.4135 \| 0.845 \| 0.2576 \| 1.8708 \| 0.845 \| 0.8304 \| 0.1872 \| 0.0559 \| \| 0.4177 \| 25.0 \| 625 \| 0.4027 \| 0.835 \| 0.2526 \| 1.5970 \| 0.835 \| 0.8187 \| 0.1886 \| 0.0645 \| \| 0.4177 \| 26.0 \| 650 \| 0.4000 \| 0.835 \| 0.2513 \| 1.7233 \| 0.835 \| 0.8151 \| 0.1998 \| 0.0613 \| \| 0.4177 \| 27.0 \| 675 \| 0.3956 \| 0.83 \| 0.2478 \| 1.5716 \| 0.83 \| 0.8143 \| 0.1871 \| 0.0533 \| \| 0.4177 \| 28.0 \| 700 \| 0.3952 \| 0.835 \| 0.2493 \| 1.5638 \| 0.835 \| 0.8153 \| 0.1942 \| 0.0593 \| \| 0.4177 \| 29.0 \| 725 \| 0.3965 \| 0.83 \| 0.2509 \| 1.5069 \| 0.83 \| 0.8150 \| 0.1811 \| 0.0594 \| \| 0.4177 \| 30.0 \| 750 \| 0.3935 \| 0.835 \| 0.2486 \| 1.5657 \| 0.835 \| 0.8168 \| 0.1948 \| 0.0573 \| \| 0.4177 \| 31.0 \| 775 \| 0.3951 \| 0.83 \| 0.2498 \| 1.5061 \| 0.83 \| 0.8146 \| 0.1886 \| 0.0591 \| \| 0.4177 \| 32.0 \| 800 \| 0.3940 \| 0.835 \| 0.2506 \| 1.5054 \| 0.835 \| 0.8203 \| 0.1991 \| 0.0598 \| \| 0.4177 \| 33.0 \| 825 \| 0.3935 \| 0.84 \| 0.2493 \| 1.5025 \| 0.8400 \| 0.8230 \| 0.1932 \| 0.0590 \| \| 0.4177 \| 34.0 \| 850 \| 0.3928 \| 0.84 \| 0.2485 \| 1.5679 \| 0.8400 \| 0.8227 \| 0.1981 \| 0.0570 \| \| 0.4177 \| 35.0 \| 875 \| 0.3942 \| 0.835 \| 0.2497 \| 1.5670 \| 0.835 \| 0.8177 \| 0.1940 \| 0.0592 \| \| 0.4177 \| 36.0 \| 900 \| 0.3935 \| 0.835 \| 0.2491 \| 1.5120 \| 0.835 \| 0.8203 \| 0.1931 \| 0.0596 \| \| 0.4177 \| 37.0 \| 925 \| 0.3932 \| 0.835 \| 0.2496 \| 1.5715 \| 0.835 \| 0.8203 \| 0.1989 \| 0.0597 \| \| 0.4177 \| 38.0 \| 950 \| 0.3924 \| 0.835 \| 0.2491 \| 1.5091 \| 0.835 \| 0.8203 \| 0.1973 \| 0.0606 \| \| 0.4177 \| 39.0 \| 975 \| 0.3936 \| 0.835 \| 0.2500 \| 1.5036 \| 0.835 \| 0.8203 \| 0.1954 \| 0.0602 \| \| 0.0597 \| 40.0 \| 1000 \| 0.3936 \| 0.835 \| 0.2497 \| 1.4602 \| 0.835 \| 0.8203 \| 0.2053 \| 0.0597 \| \| 0.0597 \| 41.0 \| 1025 \| 0.3936 \| 0.835 \| 0.2505 \| 1.5040 \| 0.835 \| 0.8203 \| 0.2026 \| 0.0607 \| \| 0.0597 \| 42.0 \| 1050 \| 0.3931 \| 0.83 \| 0.2500 \| 1.4565 \| 0.83 \| 0.8164 \| 0.1961 \| 0.0590 \| \| 0.0597 \| 43.0 \| 1075 \| 0.3931 \| 0.835 \| 0.2497 \| 1.5208 \| 0.835 \| 0.8203 \| 0.1972 \| 0.0591 \| \| 0.0597 \| 44.0 \| 1100 \| 0.3932 \| 0.835 \| 0.2503 \| 1.5040 \| 0.835 \| 0.8203 \| 0.2030 \| 0.0606 \| \| 0.0597 \| 45.0 \| 1125 \| 0.3930 \| 0.835 \| 0.2502 \| 1.4555 \| 0.835 \| 0.8203 \| 0.1992 \| 0.0604 \| \| 0.0597 \| 46.0 \| 1150 \| 0.3927 \| 0.835 \| 0.2500 \| 1.4553 \| 0.835 \| 0.8203 \| 0.1960 \| 0.0616 \| \| 0.0597 \| 47.0 \| 1175 \| 0.3928 \| 0.835 \| 0.2501 \| 1.3970 \| 0.835 \| 0.8203 \| 0.1965 \| 0.0610 \| \| 0.0597 \| 48.0 \| 1200 \| 0.3930 \| 0.835 \| 0.2498 \| 1.3967 \| 0.835 \| 0.8203 \| 0.1989 \| 0.0599 \| \| 0.0597 \| 49.0 \| 1225 \| 0.3931 \| 0.835 \| 0.2502 \| 1.4578 \| 0.835 \| 0.8203 \| 0.1963 \| 0.0606 \| \| 0.0597 \| 50.0 \| 1250 \| 0.3932 \| 0.835 \| 0.2504 \| 1.4475 \| 0.835 \| 0.8203 \| 0.1996 \| 0.0604 \| \| 0.0597 \| 51.0 \| 1275 \| 0.3928 \| 0.835 \| 0.2500 \| 1.3382 \| 0.835 \| 0.8203 \| 0.2002 \| 0.0609 \| \| 0.0597 \| 52.0 \| 1300 \| 0.3933 \| 0.83 \| 0.2502 \| 1.4424 \| 0.83 \| 0.8164 \| 0.1991 \| 0.0597 \| \| 0.0597 \| 53.0 \| 1325 \| 0.3933 \| 0.83 \| 0.2502 \| 1.3390 \| 0.83 \| 0.8164 \| 0.1965 \| 0.0604 \| \| 0.0597 \| 54.0 \| 1350 \| 0.3929 \| 0.83 \| 0.2502 \| 1.3351 \| 0.83 \| 0.8164 \| 0.1914 \| 0.0608 \| \| 0.0597 \| 55.0 \| 1375 \| 0.3932 \| 0.83 \| 0.2503 \| 1.3422 \| 0.83 \| 0.8164 \| 0.1969 \| 0.0608 \| \| 0.0597 \| 56.0 \| 1400 \| 0.3934 \| 0.83 \| 0.2506 \| 1.3369 \| 0.83 \| 0.8164 \| 0.1950 \| 0.0599 \| \| 0.0597 \| 57.0 \| 1425 \| 0.3930 \| 0.83 \| 0.2502 \| 1.3829 \| 0.83 \| 0.8164 \| 0.1966 \| 0.0603 \| \| 0.0597 \| 58.0 \| 1450 \| 0.3930 \| 0.835 \| 0.2503 \| 1.3219 \| 0.835 \| 0.8203 \| 0.1907 \| 0.0607 \| \| 0.0597 \| 59.0 \| 1475 \| 0.3930 \| 0.83 \| 0.2504 \| 1.3268 \| 0.83 \| 0.8164 \| 0.1919 \| 0.0599 \| \| 0.0574 \| 60.0 \| 1500 \| 0.3933 \| 0.835 \| 0.2505 \| 1.3242 \| 0.835 \| 0.8203 \| 0.1913 \| 0.0601 \| \| 0.0574 \| 61.0 \| 1525 \| 0.3930 \| 0.83 \| 0.2504 \| 1.3205 \| 0.83 \| 0.8164 \| 0.1943 \| 0.0607 \| \| 0.0574 \| 62.0 \| 1550 \| 0.3930 \| 0.83 \| 0.2504 \| 1.3189 \| 0.83 \| 0.8164 \| 0.1947 \| 0.0608 \| \| 0.0574 \| 63.0 \| 1575 \| 0.3931 \| 0.83 \| 0.2504 \| 1.3197 \| 0.83 \| 0.8164 \| 0.1917 \| 0.0600 \| \| 0.0574 \| 64.0 \| 1600 \| 0.3931 \| 0.835 \| 0.2505 \| 1.3200 \| 0.835 \| 0.8203 \| 0.1904 \| 0.0597 \| \| 0.0574 \| 65.0 \| 1625 \| 0.3932 \| 0.83 \| 0.2505 \| 1.3175 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0601 \| \| 0.0574 \| 66.0 \| 1650 \| 0.3931 \| 0.83 \| 0.2506 \| 1.3200 \| 0.83 \| 0.8164 \| 0.1917 \| 0.0608 \| \| 0.0574 \| 67.0 \| 1675 \| 0.3929 \| 0.83 \| 0.2503 \| 1.3188 \| 0.83 \| 0.8164 \| 0.1940 \| 0.0598 \| \| 0.0574 \| 68.0 \| 1700 \| 0.3931 \| 0.83 \| 0.2505 \| 1.3160 \| 0.83 \| 0.8164 \| 0.1913 \| 0.0599 \| \| 0.0574 \| 69.0 \| 1725 \| 0.3931 \| 0.83 \| 0.2505 \| 1.3161 \| 0.83 \| 0.8164 \| 0.1941 \| 0.0598 \| \| 0.0574 \| 70.0 \| 1750 \| 0.3932 \| 0.83 \| 0.2506 \| 1.3171 \| 0.83 \| 0.8164 \| 0.1961 \| 0.0608 \| \| 0.0574 \| 71.0 \| 1775 \| 0.3930 \| 0.83 \| 0.2506 \| 1.3161 \| 0.83 \| 0.8164 \| 0.1913 \| 0.0602 \| \| 0.0574 \| 72.0 \| 1800 \| 0.3929 \| 0.83 \| 0.2505 \| 1.3155 \| 0.83 \| 0.8164 \| 0.1960 \| 0.0603 \| \| 0.0574 \| 73.0 \| 1825 \| 0.3930 \| 0.83 \| 0.2506 \| 1.3152 \| 0.83 \| 0.8164 \| 0.1941 \| 0.0601 \| \| 0.0574 \| 74.0 \| 1850 \| 0.3930 \| 0.83 \| 0.2506 \| 1.3167 \| 0.83 \| 0.8164 \| 0.1940 \| 0.0602 \| \| 0.0574 \| 75.0 \| 1875 \| 0.3933 \| 0.83 \| 0.2507 \| 1.3148 \| 0.83 \| 0.8164 \| 0.1918 \| 0.0600 \| \| 0.0574 \| 76.0 \| 1900 \| 0.3930 \| 0.83 \| 0.2505 \| 1.3146 \| 0.83 \| 0.8164 \| 0.1914 \| 0.0602 \| \| 0.0574 \| 77.0 \| 1925 \| 0.3930 \| 0.83 \| 0.2505 \| 1.3147 \| 0.83 \| 0.8164 \| 0.1914 \| 0.0598 \| \| 0.0574 \| 78.0 \| 1950 \| 0.3931 \| 0.83 \| 0.2506 \| 1.3134 \| 0.83 \| 0.8164 \| 0.1942 \| 0.0601 \| \| 0.0574 \| 79.0 \| 1975 \| 0.3931 \| 0.83 \| 0.2505 \| 1.3137 \| 0.83 \| 0.8164 \| 0.1916 \| 0.0598 \| \| 0.0573 \| 80.0 \| 2000 \| 0.3931 \| 0.83 \| 0.2506 \| 1.3136 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0601 \| \| 0.0573 \| 81.0 \| 2025 \| 0.3932 \| 0.83 \| 0.2506 \| 1.3132 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0607 \| \| 0.0573 \| 82.0 \| 2050 \| 0.3933 \| 0.83 \| 0.2507 \| 1.3142 \| 0.83 \| 0.8164 \| 0.1943 \| 0.0603 \| \| 0.0573 \| 83.0 \| 2075 \| 0.3933 \| 0.83 \| 0.2507 \| 1.3135 \| 0.83 \| 0.8164 \| 0.1916 \| 0.0603 \| \| 0.0573 \| 84.0 \| 2100 \| 0.3931 \| 0.83 \| 0.2506 \| 1.3124 \| 0.83 \| 0.8164 \| 0.1914 \| 0.0601 \| \| 0.0573 \| 85.0 \| 2125 \| 0.3931 \| 0.83 \| 0.2507 \| 1.3128 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 86.0 \| 2150 \| 0.3931 \| 0.83 \| 0.2506 \| 1.3128 \| 0.83 \| 0.8164 \| 0.1916 \| 0.0602 \| \| 0.0573 \| 87.0 \| 2175 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3130 \| 0.83 \| 0.8164 \| 0.1943 \| 0.0602 \| \| 0.0573 \| 88.0 \| 2200 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3123 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 89.0 \| 2225 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3123 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0599 \| \| 0.0573 \| 90.0 \| 2250 \| 0.3931 \| 0.83 \| 0.2507 \| 1.3119 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 91.0 \| 2275 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3121 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 92.0 \| 2300 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3117 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0601 \| \| 0.0573 \| 93.0 \| 2325 \| 0.3931 \| 0.83 \| 0.2507 \| 1.3117 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 94.0 \| 2350 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3120 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 95.0 \| 2375 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3120 \| 0.83 \| 0.8164 \| 0.1916 \| 0.0602 \| \| 0.0573 \| 96.0 \| 2400 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3119 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 97.0 \| 2425 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3118 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0601 \| \| 0.0573 \| 98.0 \| 2450 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3117 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 99.0 \| 2475 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3118 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| \| 0.0573 \| 100.0 \| 2500 \| 0.3932 \| 0.83 \| 0.2507 \| 1.3117 \| 0.83 \| 0.8164 \| 0.1915 \| 0.0602 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6810 - Accuracy: 0.795 - Brier Loss: 0.3284 - Nll: 1.4069 - F1 Micro: 0.795 - F1 Macro: 0.7636 - Ece: 0.2215 - Aurc: 0.0726 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 3.5123 \| 0.19 \| 1.2292 \| 9.8836 \| 0.19 \| 0.0557 \| 0.5319 \| 0.8455 \| \| No log \| 2.0 \| 50 \| 3.3061 \| 0.19 \| 1.1746 \| 9.5259 \| 0.19 \| 0.0557 \| 0.4948 \| 0.8502 \| \| No log \| 3.0 \| 75 \| 3.0163 \| 0.18 \| 1.1038 \| 9.0069 \| 0.18 \| 0.0530 \| 0.3959 \| 0.8542 \| \| No log \| 4.0 \| 100 \| 2.7051 \| 0.16 \| 1.0425 \| 8.3332 \| 0.16 \| 0.0685 \| 0.3637 \| 0.8603 \| \| No log \| 5.0 \| 125 \| 2.4101 \| 0.145 \| 0.9955 \| 7.8875 \| 0.145 \| 0.0886 \| 0.3276 \| 0.8594 \| \| No log \| 6.0 \| 150 \| 2.1622 \| 0.16 \| 0.9581 \| 7.4210 \| 0.16 \| 0.1094 \| 0.3098 \| 0.8418 \| \| No log \| 7.0 \| 175 \| 1.9665 \| 0.17 \| 0.9199 \| 6.3502 \| 0.17 \| 0.1141 \| 0.2939 \| 0.8129 \| \| No log \| 8.0 \| 200 \| 1.8218 \| 0.19 \| 0.8852 \| 4.6467 \| 0.19 \| 0.1580 \| 0.2691 \| 0.7908 \| \| No log \| 9.0 \| 225 \| 1.7041 \| 0.205 \| 0.8503 \| 3.9915 \| 0.205 \| 0.1722 \| 0.2746 \| 0.7349 \| \| No log \| 10.0 \| 250 \| 1.6030 \| 0.315 \| 0.8144 \| 3.8156 \| 0.315 \| 0.2716 \| 0.2999 \| 0.6443 \| \| No log \| 11.0 \| 275 \| 1.5078 \| 0.39 \| 0.7749 \| 3.6634 \| 0.39 \| 0.3103 \| 0.2992 \| 0.5291 \| \| No log \| 12.0 \| 300 \| 1.4231 \| 0.46 \| 0.7351 \| 3.5329 \| 0.46 \| 0.3550 \| 0.3143 \| 0.4339 \| \| No log \| 13.0 \| 325 \| 1.3487 \| 0.465 \| 0.6988 \| 3.4534 \| 0.465 \| 0.3851 \| 0.2929 \| 0.3704 \| \| No log \| 14.0 \| 350 \| 1.2823 \| 0.51 \| 0.6657 \| 3.2203 \| 0.51 \| 0.4164 \| 0.2835 \| 0.3215 \| \| No log \| 15.0 \| 375 \| 1.2282 \| 0.54 \| 0.6399 \| 3.0431 \| 0.54 \| 0.4415 \| 0.2943 \| 0.2928 \| \| No log \| 16.0 \| 400 \| 1.1793 \| 0.555 \| 0.6170 \| 2.8562 \| 0.555 \| 0.4502 \| 0.2813 \| 0.2676 \| \| No log \| 17.0 \| 425 \| 1.1466 \| 0.565 \| 0.6019 \| 2.7630 \| 0.565 \| 0.4607 \| 0.2450 \| 0.2524 \| \| No log \| 18.0 \| 450 \| 1.1114 \| 0.59 \| 0.5832 \| 2.7390 \| 0.59 \| 0.4721 \| 0.2850 \| 0.2289 \| \| No log \| 19.0 \| 475 \| 1.0835 \| 0.6 \| 0.5699 \| 2.5928 \| 0.6 \| 0.5073 \| 0.2760 \| 0.2195 \| \| 1.9045 \| 20.0 \| 500 \| 1.0547 \| 0.615 \| 0.5539 \| 2.6261 \| 0.615 \| 0.5273 \| 0.2883 \| 0.2044 \| \| 1.9045 \| 21.0 \| 525 \| 1.0294 \| 0.625 \| 0.5404 \| 2.6118 \| 0.625 \| 0.5343 \| 0.2703 \| 0.1945 \| \| 1.9045 \| 22.0 \| 550 \| 1.0085 \| 0.635 \| 0.5300 \| 2.4378 \| 0.635 \| 0.5381 \| 0.2727 \| 0.1842 \| \| 1.9045 \| 23.0 \| 575 \| 0.9900 \| 0.64 \| 0.5188 \| 2.4290 \| 0.64 \| 0.5435 \| 0.2781 \| 0.1745 \| \| 1.9045 \| 24.0 \| 600 \| 0.9674 \| 0.645 \| 0.5071 \| 2.3513 \| 0.645 \| 0.5527 \| 0.2631 \| 0.1647 \| \| 1.9045 \| 25.0 \| 625 \| 0.9522 \| 0.64 \| 0.4980 \| 2.2815 \| 0.64 \| 0.5494 \| 0.2687 \| 0.1606 \| \| 1.9045 \| 26.0 \| 650 \| 0.9336 \| 0.65 \| 0.4883 \| 2.2513 \| 0.65 \| 0.5603 \| 0.2727 \| 0.1515 \| \| 1.9045 \| 27.0 \| 675 \| 0.9175 \| 0.665 \| 0.4795 \| 2.2466 \| 0.665 \| 0.5707 \| 0.2848 \| 0.1450 \| \| 1.9045 \| 28.0 \| 700 \| 0.9060 \| 0.655 \| 0.4731 \| 2.2223 \| 0.655 \| 0.5655 \| 0.2598 \| 0.1426 \| \| 1.9045 \| 29.0 \| 725 \| 0.8924 \| 0.67 \| 0.4648 \| 2.1571 \| 0.67 \| 0.5748 \| 0.2504 \| 0.1364 \| \| 1.9045 \| 30.0 \| 750 \| 0.8808 \| 0.675 \| 0.4580 \| 2.1970 \| 0.675 \| 0.5804 \| 0.2124 \| 0.1302 \| \| 1.9045 \| 31.0 \| 775 \| 0.8698 \| 0.675 \| 0.4513 \| 1.9818 \| 0.675 \| 0.5784 \| 0.2413 \| 0.1248 \| \| 1.9045 \| 32.0 \| 800 \| 0.8581 \| 0.685 \| 0.4451 \| 2.0653 \| 0.685 \| 0.6062 \| 0.2783 \| 0.1221 \| \| 1.9045 \| 33.0 \| 825 \| 0.8493 \| 0.68 \| 0.4398 \| 1.9229 \| 0.68 \| 0.5964 \| 0.2430 \| 0.1198 \| \| 1.9045 \| 34.0 \| 850 \| 0.8416 \| 0.675 \| 0.4351 \| 1.9147 \| 0.675 \| 0.5901 \| 0.2547 \| 0.1181 \| \| 1.9045 \| 35.0 \| 875 \| 0.8329 \| 0.69 \| 0.4296 \| 1.9727 \| 0.69 \| 0.6098 \| 0.2498 \| 0.1121 \| \| 1.9045 \| 36.0 \| 900 \| 0.8222 \| 0.7 \| 0.4234 \| 1.8988 \| 0.7 \| 0.6185 \| 0.2404 \| 0.1084 \| \| 1.9045 \| 37.0 \| 925 \| 0.8178 \| 0.69 \| 0.4201 \| 1.8900 \| 0.69 \| 0.6103 \| 0.2338 \| 0.1079 \| \| 1.9045 \| 38.0 \| 950 \| 0.8091 \| 0.69 \| 0.4153 \| 1.9396 \| 0.69 \| 0.6100 \| 0.2469 \| 0.1058 \| \| 1.9045 \| 39.0 \| 975 \| 0.7992 \| 0.705 \| 0.4098 \| 1.8177 \| 0.705 \| 0.6453 \| 0.1971 \| 0.1037 \| \| 0.8325 \| 40.0 \| 1000 \| 0.7962 \| 0.715 \| 0.4069 \| 1.7962 \| 0.715 \| 0.6560 \| 0.2474 \| 0.1008 \| \| 0.8325 \| 41.0 \| 1025 \| 0.7890 \| 0.715 \| 0.4032 \| 1.7233 \| 0.715 \| 0.6592 \| 0.2417 \| 0.1009 \| \| 0.8325 \| 42.0 \| 1050 \| 0.7842 \| 0.715 \| 0.3997 \| 1.6669 \| 0.715 \| 0.6573 \| 0.2441 \| 0.1005 \| \| 0.8325 \| 43.0 \| 1075 \| 0.7788 \| 0.71 \| 0.3962 \| 1.6468 \| 0.7100 \| 0.6515 \| 0.2199 \| 0.0998 \| \| 0.8325 \| 44.0 \| 1100 \| 0.7713 \| 0.725 \| 0.3918 \| 1.6398 \| 0.7250 \| 0.6698 \| 0.2363 \| 0.0945 \| \| 0.8325 \| 45.0 \| 1125 \| 0.7687 \| 0.725 \| 0.3895 \| 1.6397 \| 0.7250 \| 0.6677 \| 0.2478 \| 0.0939 \| \| 0.8325 \| 46.0 \| 1150 \| 0.7642 \| 0.73 \| 0.3867 \| 1.6259 \| 0.7300 \| 0.6684 \| 0.2538 \| 0.0941 \| \| 0.8325 \| 47.0 \| 1175 \| 0.7571 \| 0.74 \| 0.3822 \| 1.5033 \| 0.74 \| 0.6907 \| 0.2269 \| 0.0903 \| \| 0.8325 \| 48.0 \| 1200 \| 0.7547 \| 0.74 \| 0.3806 \| 1.5595 \| 0.74 \| 0.6830 \| 0.2448 \| 0.0912 \| \| 0.8325 \| 49.0 \| 1225 \| 0.7516 \| 0.75 \| 0.3779 \| 1.6264 \| 0.75 \| 0.6921 \| 0.2489 \| 0.0887 \| \| 0.8325 \| 50.0 \| 1250 \| 0.7477 \| 0.75 \| 0.3759 \| 1.5568 \| 0.75 \| 0.7029 \| 0.2388 \| 0.0888 \| \| 0.8325 \| 51.0 \| 1275 \| 0.7431 \| 0.755 \| 0.3725 \| 1.5037 \| 0.755 \| 0.6986 \| 0.2254 \| 0.0848 \| \| 0.8325 \| 52.0 \| 1300 \| 0.7418 \| 0.755 \| 0.3713 \| 1.4951 \| 0.755 \| 0.7085 \| 0.2261 \| 0.0862 \| \| 0.8325 \| 53.0 \| 1325 \| 0.7360 \| 0.77 \| 0.3676 \| 1.4881 \| 0.7700 \| 0.7241 \| 0.2474 \| 0.0825 \| \| 0.8325 \| 54.0 \| 1350 \| 0.7339 \| 0.77 \| 0.3665 \| 1.5554 \| 0.7700 \| 0.7241 \| 0.2646 \| 0.0827 \| \| 0.8325 \| 55.0 \| 1375 \| 0.7294 \| 0.775 \| 0.3636 \| 1.4885 \| 0.775 \| 0.7275 \| 0.2283 \| 0.0818 \| \| 0.8325 \| 56.0 \| 1400 \| 0.7265 \| 0.78 \| 0.3617 \| 1.5387 \| 0.78 \| 0.7306 \| 0.2416 \| 0.0799 \| \| 0.8325 \| 57.0 \| 1425 \| 0.7247 \| 0.77 \| 0.3598 \| 1.4382 \| 0.7700 \| 0.7241 \| 0.2553 \| 0.0806 \| \| 0.8325 \| 58.0 \| 1450 \| 0.7234 \| 0.78 \| 0.3589 \| 1.4888 \| 0.78 \| 0.7306 \| 0.2231 \| 0.0796 \| \| 0.8325 \| 59.0 \| 1475 \| 0.7186 \| 0.78 \| 0.3565 \| 1.5400 \| 0.78 \| 0.7306 \| 0.2200 \| 0.0790 \| \| 0.618 \| 60.0 \| 1500 \| 0.7174 \| 0.78 \| 0.3549 \| 1.4823 \| 0.78 \| 0.7306 \| 0.2340 \| 0.0787 \| \| 0.618 \| 61.0 \| 1525 \| 0.7148 \| 0.78 \| 0.3534 \| 1.4804 \| 0.78 \| 0.7306 \| 0.2412 \| 0.0785 \| \| 0.618 \| 62.0 \| 1550 \| 0.7122 \| 0.785 \| 0.3516 \| 1.4334 \| 0.785 \| 0.7445 \| 0.2353 \| 0.0787 \| \| 0.618 \| 63.0 \| 1575 \| 0.7107 \| 0.79 \| 0.3501 \| 1.4153 \| 0.79 \| 0.7516 \| 0.2354 \| 0.0777 \| \| 0.618 \| 64.0 \| 1600 \| 0.7091 \| 0.78 \| 0.3491 \| 1.4698 \| 0.78 \| 0.7306 \| 0.2324 \| 0.0780 \| \| 0.618 \| 65.0 \| 1625 \| 0.7071 \| 0.79 \| 0.3481 \| 1.4097 \| 0.79 \| 0.7516 \| 0.2198 \| 0.0785 \| \| 0.618 \| 66.0 \| 1650 \| 0.7047 \| 0.785 \| 0.3464 \| 1.4088 \| 0.785 \| 0.7458 \| 0.2325 \| 0.0778 \| \| 0.618 \| 67.0 \| 1675 \| 0.7041 \| 0.785 \| 0.3457 \| 1.4108 \| 0.785 \| 0.7458 \| 0.2248 \| 0.0781 \| \| 0.618 \| 68.0 \| 1700 \| 0.7025 \| 0.79 \| 0.3444 \| 1.4145 \| 0.79 \| 0.7516 \| 0.2195 \| 0.0773 \| \| 0.618 \| 69.0 \| 1725 \| 0.7014 \| 0.79 \| 0.3436 \| 1.4120 \| 0.79 \| 0.7516 \| 0.2629 \| 0.0771 \| \| 0.618 \| 70.0 \| 1750 \| 0.6992 \| 0.785 \| 0.3422 \| 1.4046 \| 0.785 \| 0.7458 \| 0.2294 \| 0.0767 \| \| 0.618 \| 71.0 \| 1775 \| 0.6982 \| 0.785 \| 0.3412 \| 1.4142 \| 0.785 \| 0.7458 \| 0.2325 \| 0.0761 \| \| 0.618 \| 72.0 \| 1800 \| 0.6954 \| 0.79 \| 0.3395 \| 1.4009 \| 0.79 \| 0.7516 \| 0.2253 \| 0.0763 \| \| 0.618 \| 73.0 \| 1825 \| 0.6942 \| 0.79 \| 0.3389 \| 1.3994 \| 0.79 \| 0.7559 \| 0.2383 \| 0.0763 \| \| 0.618 \| 74.0 \| 1850 \| 0.6937 \| 0.785 \| 0.3382 \| 1.4061 \| 0.785 \| 0.7458 \| 0.2213 \| 0.0762 \| \| 0.618 \| 75.0 \| 1875 \| 0.6935 \| 0.785 \| 0.3378 \| 1.4082 \| 0.785 \| 0.7458 \| 0.2218 \| 0.0762 \| \| 0.618 \| 76.0 \| 1900 \| 0.6910 \| 0.795 \| 0.3359 \| 1.4098 \| 0.795 \| 0.7599 \| 0.2689 \| 0.0746 \| \| 0.618 \| 77.0 \| 1925 \| 0.6907 \| 0.79 \| 0.3356 \| 1.4072 \| 0.79 \| 0.7541 \| 0.2254 \| 0.0741 \| \| 0.618 \| 78.0 \| 1950 \| 0.6896 \| 0.795 \| 0.3352 \| 1.3996 \| 0.795 \| 0.7636 \| 0.2226 \| 0.0743 \| \| 0.618 \| 79.0 \| 1975 \| 0.6896 \| 0.79 \| 0.3349 \| 1.4073 \| 0.79 \| 0.7541 \| 0.2295 \| 0.0742 \| \| 0.516 \| 80.0 \| 2000 \| 0.6874 \| 0.79 \| 0.3335 \| 1.4089 \| 0.79 \| 0.7541 \| 0.2287 \| 0.0743 \| \| 0.516 \| 81.0 \| 2025 \| 0.6874 \| 0.795 \| 0.3333 \| 1.3983 \| 0.795 \| 0.7636 \| 0.2387 \| 0.0742 \| \| 0.516 \| 82.0 \| 2050 \| 0.6867 \| 0.795 \| 0.3327 \| 1.4098 \| 0.795 \| 0.7636 \| 0.2162 \| 0.0736 \| \| 0.516 \| 83.0 \| 2075 \| 0.6865 \| 0.795 \| 0.3323 \| 1.4656 \| 0.795 \| 0.7636 \| 0.2072 \| 0.0738 \| \| 0.516 \| 84.0 \| 2100 \| 0.6857 \| 0.795 \| 0.3323 \| 1.4107 \| 0.795 \| 0.7636 \| 0.2138 \| 0.0741 \| \| 0.516 \| 85.0 \| 2125 \| 0.6854 \| 0.795 \| 0.3316 \| 1.4223 \| 0.795 \| 0.7636 \| 0.2262 \| 0.0732 \| \| 0.516 \| 86.0 \| 2150 \| 0.6846 \| 0.795 \| 0.3311 \| 1.4138 \| 0.795 \| 0.7636 \| 0.2224 \| 0.0733 \| \| 0.516 \| 87.0 \| 2175 \| 0.6834 \| 0.795 \| 0.3302 \| 1.4113 \| 0.795 \| 0.7636 \| 0.2307 \| 0.0731 \| \| 0.516 \| 88.0 \| 2200 \| 0.6831 \| 0.795 \| 0.3300 \| 1.4088 \| 0.795 \| 0.7636 \| 0.2256 \| 0.0730 \| \| 0.516 \| 89.0 \| 2225 \| 0.6821 \| 0.795 \| 0.3295 \| 1.4126 \| 0.795 \| 0.7636 \| 0.2395 \| 0.0728 \| \| 0.516 \| 90.0 \| 2250 \| 0.6821 \| 0.795 \| 0.3294 \| 1.4123 \| 0.795 \| 0.7636 \| 0.2237 \| 0.0728 \| \| 0.516 \| 91.0 \| 2275 \| 0.6823 \| 0.795 \| 0.3294 \| 1.4085 \| 0.795 \| 0.7636 \| 0.2213 \| 0.0728 \| \| 0.516 \| 92.0 \| 2300 \| 0.6819 \| 0.795 \| 0.3290 \| 1.4105 \| 0.795 \| 0.7636 \| 0.2332 \| 0.0730 \| \| 0.516 \| 93.0 \| 2325 \| 0.6816 \| 0.795 \| 0.3289 \| 1.4094 \| 0.795 \| 0.7636 \| 0.2236 \| 0.0729 \| \| 0.516 \| 94.0 \| 2350 \| 0.6812 \| 0.795 \| 0.3287 \| 1.4092 \| 0.795 \| 0.7636 \| 0.2235 \| 0.0729 \| \| 0.516 \| 95.0 \| 2375 \| 0.6813 \| 0.795 \| 0.3286 \| 1.4065 \| 0.795 \| 0.7636 \| 0.2197 \| 0.0727 \| \| 0.516 \| 96.0 \| 2400 \| 0.6811 \| 0.795 \| 0.3285 \| 1.4079 \| 0.795 \| 0.7636 \| 0.2247 \| 0.0729 \| \| 0.516 \| 97.0 \| 2425 \| 0.6810 \| 0.795 \| 0.3284 \| 1.4072 \| 0.795 \| 0.7636 \| 0.2320 \| 0.0729 \| \| 0.516 \| 98.0 \| 2450 \| 0.6810 \| 0.795 \| 0.3284 \| 1.4062 \| 0.795 \| 0.7636 \| 0.2148 \| 0.0727 \| \| 0.516 \| 99.0 \| 2475 \| 0.6810 \| 0.795 \| 0.3284 \| 1.4068 \| 0.795 \| 0.7636 \| 0.2215 \| 0.0726 \| \| 0.4715 \| 100.0 \| 2500 \| 0.6810 \| 0.795 \| 0.3284 \| 1.4069 \| 0.795 \| 0.7636 \| 0.2215 \| 0.0726 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 - Accuracy: 0.82 - Brier Loss: 0.2563 - Nll: 1.8898 - F1 Micro: 0.82 - F1 Macro: 0.8009 - Ece: 0.1578 - Aurc: 0.0530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.9764 \| 0.23 \| 0.8621 \| 4.6756 \| 0.23 \| 0.1902 \| 0.2733 \| 0.7604 \| \| No log \| 2.0 \| 50 \| 1.2764 \| 0.535 \| 0.5973 \| 2.7212 \| 0.535 \| 0.4337 \| 0.2769 \| 0.2592 \| \| No log \| 3.0 \| 75 \| 0.9774 \| 0.68 \| 0.4478 \| 2.1874 \| 0.68 \| 0.5915 \| 0.2144 \| 0.1334 \| \| No log \| 4.0 \| 100 \| 0.8047 \| 0.755 \| 0.3617 \| 1.4629 \| 0.755 \| 0.7257 \| 0.1850 \| 0.0888 \| \| No log \| 5.0 \| 125 \| 0.7616 \| 0.765 \| 0.3363 \| 1.4885 \| 0.765 \| 0.7391 \| 0.2017 \| 0.0843 \| \| No log \| 6.0 \| 150 \| 1.0029 \| 0.72 \| 0.4200 \| 1.6550 \| 0.72 \| 0.7047 \| 0.2303 \| 0.1169 \| \| No log \| 7.0 \| 175 \| 0.6286 \| 0.825 \| 0.2766 \| 1.2493 \| 0.825 \| 0.7930 \| 0.1954 \| 0.0646 \| \| No log \| 8.0 \| 200 \| 0.6859 \| 0.82 \| 0.2857 \| 1.4847 \| 0.82 \| 0.7971 \| 0.1837 \| 0.0699 \| \| No log \| 9.0 \| 225 \| 0.6365 \| 0.81 \| 0.2765 \| 1.1457 \| 0.81 \| 0.7913 \| 0.1604 \| 0.0669 \| \| No log \| 10.0 \| 250 \| 0.6085 \| 0.81 \| 0.2614 \| 1.5809 \| 0.81 \| 0.7928 \| 0.1874 \| 0.0536 \| \| No log \| 11.0 \| 275 \| 0.5900 \| 0.84 \| 0.2620 \| 1.1457 \| 0.8400 \| 0.8308 \| 0.1695 \| 0.0674 \| \| No log \| 12.0 \| 300 \| 0.8544 \| 0.75 \| 0.3667 \| 1.9577 \| 0.75 \| 0.7330 \| 0.1988 \| 0.1329 \| \| No log \| 13.0 \| 325 \| 0.5265 \| 0.845 \| 0.2278 \| 1.2521 \| 0.845 \| 0.8209 \| 0.1518 \| 0.0399 \| \| No log \| 14.0 \| 350 \| 0.5702 \| 0.815 \| 0.2567 \| 1.5233 \| 0.815 \| 0.8032 \| 0.1551 \| 0.0519 \| \| No log \| 15.0 \| 375 \| 0.5933 \| 0.845 \| 0.2581 \| 1.4776 \| 0.845 \| 0.8341 \| 0.1659 \| 0.0738 \| \| No log \| 16.0 \| 400 \| 0.5697 \| 0.84 \| 0.2496 \| 1.6732 \| 0.8400 \| 0.8235 \| 0.1470 \| 0.0557 \| \| No log \| 17.0 \| 425 \| 0.5471 \| 0.825 \| 0.2428 \| 1.7010 \| 0.825 \| 0.8093 \| 0.1406 \| 0.0461 \| \| No log \| 18.0 \| 450 \| 0.5696 \| 0.825 \| 0.2546 \| 1.4095 \| 0.825 \| 0.7977 \| 0.1461 \| 0.0612 \| \| No log \| 19.0 \| 475 \| 0.6544 \| 0.805 \| 0.2959 \| 1.8251 \| 0.805 \| 0.7970 \| 0.1681 \| 0.0605 \| \| 0.4416 \| 20.0 \| 500 \| 0.5113 \| 0.83 \| 0.2327 \| 1.4103 \| 0.83 \| 0.8093 \| 0.1380 \| 0.0541 \| \| 0.4416 \| 21.0 \| 525 \| 0.5255 \| 0.84 \| 0.2375 \| 1.6750 \| 0.8400 \| 0.8220 \| 0.1320 \| 0.0462 \| \| 0.4416 \| 22.0 \| 550 \| 0.5889 \| 0.835 \| 0.2681 \| 1.7850 \| 0.835 \| 0.8242 \| 0.1507 \| 0.0683 \| \| 0.4416 \| 23.0 \| 575 \| 0.5456 \| 0.835 \| 0.2492 \| 1.8481 \| 0.835 \| 0.8137 \| 0.1716 \| 0.0550 \| \| 0.4416 \| 24.0 \| 600 \| 0.5661 \| 0.83 \| 0.2611 \| 1.8434 \| 0.83 \| 0.8156 \| 0.1618 \| 0.0591 \| \| 0.4416 \| 25.0 \| 625 \| 0.5444 \| 0.83 \| 0.2484 \| 1.7579 \| 0.83 \| 0.8091 \| 0.1478 \| 0.0530 \| \| 0.4416 \| 26.0 \| 650 \| 0.5418 \| 0.83 \| 0.2503 \| 1.7188 \| 0.83 \| 0.8125 \| 0.1564 \| 0.0484 \| \| 0.4416 \| 27.0 \| 675 \| 0.5532 \| 0.835 \| 0.2540 \| 1.8931 \| 0.835 \| 0.8146 \| 0.1694 \| 0.0514 \| \| 0.4416 \| 28.0 \| 700 \| 0.5492 \| 0.835 \| 0.2518 \| 1.8959 \| 0.835 \| 0.8155 \| 0.1505 \| 0.0495 \| \| 0.4416 \| 29.0 \| 725 \| 0.5478 \| 0.825 \| 0.2505 \| 1.8907 \| 0.825 \| 0.8069 \| 0.1548 \| 0.0503 \| \| 0.4416 \| 30.0 \| 750 \| 0.5478 \| 0.835 \| 0.2510 \| 1.8881 \| 0.835 \| 0.8178 \| 0.1467 \| 0.0521 \| \| 0.4416 \| 31.0 \| 775 \| 0.5472 \| 0.825 \| 0.2505 \| 1.8888 \| 0.825 \| 0.8064 \| 0.1527 \| 0.0510 \| \| 0.4416 \| 32.0 \| 800 \| 0.5522 \| 0.83 \| 0.2527 \| 1.8927 \| 0.83 \| 0.8126 \| 0.1449 \| 0.0520 \| \| 0.4416 \| 33.0 \| 825 \| 0.5513 \| 0.825 \| 0.2524 \| 1.8989 \| 0.825 \| 0.8064 \| 0.1625 \| 0.0509 \| \| 0.4416 \| 34.0 \| 850 \| 0.5465 \| 0.835 \| 0.2504 \| 1.8880 \| 0.835 \| 0.8148 \| 0.1519 \| 0.0520 \| \| 0.4416 \| 35.0 \| 875 \| 0.5489 \| 0.825 \| 0.2515 \| 1.8866 \| 0.825 \| 0.8064 \| 0.1538 \| 0.0510 \| \| 0.4416 \| 36.0 \| 900 \| 0.5508 \| 0.825 \| 0.2521 \| 1.8922 \| 0.825 \| 0.8053 \| 0.1356 \| 0.0526 \| \| 0.4416 \| 37.0 \| 925 \| 0.5495 \| 0.825 \| 0.2522 \| 1.8881 \| 0.825 \| 0.8064 \| 0.1517 \| 0.0514 \| \| 0.4416 \| 38.0 \| 950 \| 0.5483 \| 0.825 \| 0.2514 \| 1.8859 \| 0.825 \| 0.8064 \| 0.1749 \| 0.0511 \| \| 0.4416 \| 39.0 \| 975 \| 0.5508 \| 0.825 \| 0.2524 \| 1.8868 \| 0.825 \| 0.8064 \| 0.1459 \| 0.0514 \| \| 0.0519 \| 40.0 \| 1000 \| 0.5519 \| 0.825 \| 0.2529 \| 1.8862 \| 0.825 \| 0.8064 \| 0.1532 \| 0.0513 \| \| 0.0519 \| 41.0 \| 1025 \| 0.5522 \| 0.825 \| 0.2530 \| 1.8882 \| 0.825 \| 0.8064 \| 0.1665 \| 0.0519 \| \| 0.0519 \| 42.0 \| 1050 \| 0.5507 \| 0.825 \| 0.2525 \| 1.8870 \| 0.825 \| 0.8064 \| 0.1613 \| 0.0508 \| \| 0.0519 \| 43.0 \| 1075 \| 0.5528 \| 0.825 \| 0.2536 \| 1.8884 \| 0.825 \| 0.8064 \| 0.1634 \| 0.0517 \| \| 0.0519 \| 44.0 \| 1100 \| 0.5520 \| 0.825 \| 0.2531 \| 1.8879 \| 0.825 \| 0.8064 \| 0.1519 \| 0.0525 \| \| 0.0519 \| 45.0 \| 1125 \| 0.5524 \| 0.825 \| 0.2535 \| 1.8876 \| 0.825 \| 0.8053 \| 0.1582 \| 0.0515 \| \| 0.0519 \| 46.0 \| 1150 \| 0.5525 \| 0.825 \| 0.2534 \| 1.8867 \| 0.825 \| 0.8064 \| 0.1592 \| 0.0519 \| \| 0.0519 \| 47.0 \| 1175 \| 0.5532 \| 0.825 \| 0.2539 \| 1.8875 \| 0.825 \| 0.8064 \| 0.1621 \| 0.0521 \| \| 0.0519 \| 48.0 \| 1200 \| 0.5540 \| 0.825 \| 0.2540 \| 1.8865 \| 0.825 \| 0.8064 \| 0.1502 \| 0.0522 \| \| 0.0519 \| 49.0 \| 1225 \| 0.5523 \| 0.825 \| 0.2538 \| 1.8268 \| 0.825 \| 0.8072 \| 0.1625 \| 0.0514 \| \| 0.0519 \| 50.0 \| 1250 \| 0.5535 \| 0.825 \| 0.2539 \| 1.8871 \| 0.825 \| 0.8064 \| 0.1684 \| 0.0517 \| \| 0.0519 \| 51.0 \| 1275 \| 0.5526 \| 0.825 \| 0.2534 \| 1.8850 \| 0.825 \| 0.8064 \| 0.1621 \| 0.0519 \| \| 0.0519 \| 52.0 \| 1300 \| 0.5543 \| 0.825 \| 0.2543 \| 1.8865 \| 0.825 \| 0.8064 \| 0.1429 \| 0.0521 \| \| 0.0519 \| 53.0 \| 1325 \| 0.5526 \| 0.825 \| 0.2538 \| 1.8866 \| 0.825 \| 0.8064 \| 0.1613 \| 0.0515 \| \| 0.0519 \| 54.0 \| 1350 \| 0.5530 \| 0.82 \| 0.2538 \| 1.8877 \| 0.82 \| 0.8009 \| 0.1620 \| 0.0518 \| \| 0.0519 \| 55.0 \| 1375 \| 0.5550 \| 0.825 \| 0.2547 \| 1.8872 \| 0.825 \| 0.8064 \| 0.1567 \| 0.0522 \| \| 0.0519 \| 56.0 \| 1400 \| 0.5565 \| 0.825 \| 0.2552 \| 1.8859 \| 0.825 \| 0.8064 \| 0.1400 \| 0.0523 \| \| 0.0519 \| 57.0 \| 1425 \| 0.5552 \| 0.825 \| 0.2548 \| 1.8874 \| 0.825 \| 0.8064 \| 0.1543 \| 0.0520 \| \| 0.0519 \| 58.0 \| 1450 \| 0.5537 \| 0.825 \| 0.2542 \| 1.8860 \| 0.825 \| 0.8064 \| 0.1531 \| 0.0516 \| \| 0.0519 \| 59.0 \| 1475 \| 0.5559 \| 0.825 \| 0.2551 \| 1.8879 \| 0.825 \| 0.8064 \| 0.1564 \| 0.0525 \| \| 0.0508 \| 60.0 \| 1500 \| 0.5548 \| 0.825 \| 0.2545 \| 1.8866 \| 0.825 \| 0.8064 \| 0.1526 \| 0.0522 \| \| 0.0508 \| 61.0 \| 1525 \| 0.5557 \| 0.825 \| 0.2550 \| 1.8884 \| 0.825 \| 0.8064 \| 0.1443 \| 0.0524 \| \| 0.0508 \| 62.0 \| 1550 \| 0.5548 \| 0.82 \| 0.2546 \| 1.8874 \| 0.82 \| 0.8009 \| 0.1709 \| 0.0527 \| \| 0.0508 \| 63.0 \| 1575 \| 0.5556 \| 0.825 \| 0.2551 \| 1.8899 \| 0.825 \| 0.8064 \| 0.1606 \| 0.0524 \| \| 0.0508 \| 64.0 \| 1600 \| 0.5562 \| 0.825 \| 0.2553 \| 1.8872 \| 0.825 \| 0.8064 \| 0.1467 \| 0.0527 \| \| 0.0508 \| 65.0 \| 1625 \| 0.5569 \| 0.825 \| 0.2554 \| 1.8879 \| 0.825 \| 0.8064 \| 0.1537 \| 0.0524 \| \| 0.0508 \| 66.0 \| 1650 \| 0.5567 \| 0.825 \| 0.2555 \| 1.8873 \| 0.825 \| 0.8064 \| 0.1601 \| 0.0525 \| \| 0.0508 \| 67.0 \| 1675 \| 0.5556 \| 0.825 \| 0.2550 \| 1.8878 \| 0.825 \| 0.8064 \| 0.1601 \| 0.0527 \| \| 0.0508 \| 68.0 \| 1700 \| 0.5570 \| 0.825 \| 0.2555 \| 1.8879 \| 0.825 \| 0.8064 \| 0.1679 \| 0.0528 \| \| 0.0508 \| 69.0 \| 1725 \| 0.5560 \| 0.825 \| 0.2553 \| 1.8886 \| 0.825 \| 0.8064 \| 0.1525 \| 0.0521 \| \| 0.0508 \| 70.0 \| 1750 \| 0.5562 \| 0.825 \| 0.2553 \| 1.8878 \| 0.825 \| 0.8064 \| 0.1531 \| 0.0528 \| \| 0.0508 \| 71.0 \| 1775 \| 0.5572 \| 0.82 \| 0.2557 \| 1.8883 \| 0.82 \| 0.8009 \| 0.1718 \| 0.0530 \| \| 0.0508 \| 72.0 \| 1800 \| 0.5567 \| 0.82 \| 0.2555 \| 1.8888 \| 0.82 \| 0.8009 \| 0.1630 \| 0.0525 \| \| 0.0508 \| 73.0 \| 1825 \| 0.5571 \| 0.825 \| 0.2556 \| 1.8882 \| 0.825 \| 0.8064 \| 0.1598 \| 0.0528 \| \| 0.0508 \| 74.0 \| 1850 \| 0.5580 \| 0.825 \| 0.2561 \| 1.8901 \| 0.825 \| 0.8064 \| 0.1543 \| 0.0530 \| \| 0.0508 \| 75.0 \| 1875 \| 0.5579 \| 0.82 \| 0.2561 \| 1.8892 \| 0.82 \| 0.8009 \| 0.1721 \| 0.0530 \| \| 0.0508 \| 76.0 \| 1900 \| 0.5574 \| 0.82 \| 0.2559 \| 1.8892 \| 0.82 \| 0.8009 \| 0.1636 \| 0.0528 \| \| 0.0508 \| 77.0 \| 1925 \| 0.5569 \| 0.82 \| 0.2557 \| 1.8393 \| 0.82 \| 0.8009 \| 0.1634 \| 0.0526 \| \| 0.0508 \| 78.0 \| 1950 \| 0.5572 \| 0.82 \| 0.2558 \| 1.8887 \| 0.82 \| 0.8009 \| 0.1637 \| 0.0530 \| \| 0.0508 \| 79.0 \| 1975 \| 0.5578 \| 0.82 \| 0.2560 \| 1.8888 \| 0.82 \| 0.8009 \| 0.1579 \| 0.0530 \| \| 0.0507 \| 80.0 \| 2000 \| 0.5577 \| 0.82 \| 0.2559 \| 1.8889 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0532 \| \| 0.0507 \| 81.0 \| 2025 \| 0.5578 \| 0.82 \| 0.2560 \| 1.8889 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0533 \| \| 0.0507 \| 82.0 \| 2050 \| 0.5579 \| 0.825 \| 0.2561 \| 1.8891 \| 0.825 \| 0.8064 \| 0.1602 \| 0.0528 \| \| 0.0507 \| 83.0 \| 2075 \| 0.5581 \| 0.825 \| 0.2562 \| 1.8894 \| 0.825 \| 0.8064 \| 0.1544 \| 0.0528 \| \| 0.0507 \| 84.0 \| 2100 \| 0.5579 \| 0.82 \| 0.2561 \| 1.8894 \| 0.82 \| 0.8009 \| 0.1581 \| 0.0531 \| \| 0.0507 \| 85.0 \| 2125 \| 0.5580 \| 0.82 \| 0.2561 \| 1.8896 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0528 \| \| 0.0507 \| 86.0 \| 2150 \| 0.5581 \| 0.82 \| 0.2562 \| 1.8891 \| 0.82 \| 0.8009 \| 0.1580 \| 0.0532 \| \| 0.0507 \| 87.0 \| 2175 \| 0.5582 \| 0.82 \| 0.2562 \| 1.8467 \| 0.82 \| 0.8009 \| 0.1581 \| 0.0528 \| \| 0.0507 \| 88.0 \| 2200 \| 0.5583 \| 0.82 \| 0.2562 \| 1.8891 \| 0.82 \| 0.8009 \| 0.1580 \| 0.0531 \| \| 0.0507 \| 89.0 \| 2225 \| 0.5584 \| 0.815 \| 0.2563 \| 1.8894 \| 0.815 \| 0.7976 \| 0.1608 \| 0.0534 \| \| 0.0507 \| 90.0 \| 2250 \| 0.5578 \| 0.82 \| 0.2561 \| 1.8894 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| \| 0.0507 \| 91.0 \| 2275 \| 0.5584 \| 0.815 \| 0.2563 \| 1.8896 \| 0.815 \| 0.7976 \| 0.1607 \| 0.0532 \| \| 0.0507 \| 92.0 \| 2300 \| 0.5583 \| 0.82 \| 0.2562 \| 1.8893 \| 0.82 \| 0.8009 \| 0.1581 \| 0.0531 \| \| 0.0507 \| 93.0 \| 2325 \| 0.5582 \| 0.82 \| 0.2562 \| 1.8898 \| 0.82 \| 0.8009 \| 0.1579 \| 0.0530 \| \| 0.0507 \| 94.0 \| 2350 \| 0.5582 \| 0.82 \| 0.2562 \| 1.8392 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| \| 0.0507 \| 95.0 \| 2375 \| 0.5584 \| 0.82 \| 0.2563 \| 1.8897 \| 0.82 \| 0.8009 \| 0.1582 \| 0.0531 \| \| 0.0507 \| 96.0 \| 2400 \| 0.5582 \| 0.82 \| 0.2562 \| 1.8898 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| \| 0.0507 \| 97.0 \| 2425 \| 0.5583 \| 0.82 \| 0.2563 \| 1.8896 \| 0.82 \| 0.8009 \| 0.1580 \| 0.0530 \| \| 0.0507 \| 98.0 \| 2450 \| 0.5582 \| 0.82 \| 0.2562 \| 1.8898 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| \| 0.0507 \| 99.0 \| 2475 \| 0.5583 \| 0.82 \| 0.2563 \| 1.8898 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| \| 0.0507 \| 100.0 \| 2500 \| 0.5583 \| 0.82 \| 0.2563 \| 1.8898 \| 0.82 \| 0.8009 \| 0.1578 \| 0.0530 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
ayanban011/6_e_200-tiny_tobacco3482	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 6_e_200-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5797 - Accuracy: 0.68 - Brier Loss: 0.4846 - Nll: 2.7977 - F1 Micro: 0.68 - F1 Macro: 0.6624 - Ece: 0.2589 - Aurc: 0.2179 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.6199 \| 0.545 \| 0.5837 \| 2.2367 \| 0.545 \| 0.4488 \| 0.2385 \| 0.2462 \| \| No log \| 2.0 \| 50 \| 0.4278 \| 0.72 \| 0.4443 \| 1.6749 \| 0.72 \| 0.7298 \| 0.3095 \| 0.1339 \| \| No log \| 3.0 \| 75 \| 0.6756 \| 0.625 \| 0.4913 \| 2.4923 \| 0.625 \| 0.5751 \| 0.2278 \| 0.1729 \| \| No log \| 4.0 \| 100 \| 0.6851 \| 0.615 \| 0.5031 \| 2.5374 \| 0.615 \| 0.5988 \| 0.2487 \| 0.1568 \| \| No log \| 5.0 \| 125 \| 0.5550 \| 0.69 \| 0.4473 \| 1.6315 \| 0.69 \| 0.6549 \| 0.2726 \| 0.1697 \| \| No log \| 6.0 \| 150 \| 0.4135 \| 0.79 \| 0.3326 \| 1.3743 \| 0.79 \| 0.7851 \| 0.2019 \| 0.0767 \| \| No log \| 7.0 \| 175 \| 0.4111 \| 0.77 \| 0.3527 \| 1.6472 \| 0.7700 \| 0.7388 \| 0.1917 \| 0.0849 \| \| No log \| 8.0 \| 200 \| 0.4809 \| 0.745 \| 0.3805 \| 2.1093 \| 0.745 \| 0.7260 \| 0.2375 \| 0.0818 \| \| No log \| 9.0 \| 225 \| 0.6527 \| 0.65 \| 0.4651 \| 2.5269 \| 0.65 \| 0.6271 \| 0.2237 \| 0.1333 \| \| No log \| 10.0 \| 250 \| 0.5215 \| 0.715 \| 0.4203 \| 1.7818 \| 0.715 \| 0.6875 \| 0.2155 \| 0.1328 \| \| No log \| 11.0 \| 275 \| 0.5673 \| 0.64 \| 0.4965 \| 2.1840 \| 0.64 \| 0.5458 \| 0.2547 \| 0.1750 \| \| No log \| 12.0 \| 300 \| 0.4713 \| 0.75 \| 0.3879 \| 2.0629 \| 0.75 \| 0.7397 \| 0.2143 \| 0.1262 \| \| No log \| 13.0 \| 325 \| 0.5793 \| 0.655 \| 0.4791 \| 2.6089 \| 0.655 \| 0.6206 \| 0.2461 \| 0.1870 \| \| No log \| 14.0 \| 350 \| 0.6098 \| 0.685 \| 0.4375 \| 2.0773 \| 0.685 \| 0.6802 \| 0.2415 \| 0.1480 \| \| No log \| 15.0 \| 375 \| 0.5338 \| 0.65 \| 0.4777 \| 2.1062 \| 0.65 \| 0.6267 \| 0.2486 \| 0.1577 \| \| No log \| 16.0 \| 400 \| 0.6278 \| 0.675 \| 0.4482 \| 2.3275 \| 0.675 \| 0.6822 \| 0.2425 \| 0.1606 \| \| No log \| 17.0 \| 425 \| 0.5165 \| 0.69 \| 0.4524 \| 2.1670 \| 0.69 \| 0.6661 \| 0.2650 \| 0.1554 \| \| No log \| 18.0 \| 450 \| 0.6064 \| 0.64 \| 0.4978 \| 2.5380 \| 0.64 \| 0.5838 \| 0.2508 \| 0.1850 \| \| No log \| 19.0 \| 475 \| 0.6753 \| 0.645 \| 0.5125 \| 2.4190 \| 0.645 \| 0.5875 \| 0.2664 \| 0.2124 \| \| 0.3785 \| 20.0 \| 500 \| 0.6946 \| 0.65 \| 0.5331 \| 3.1366 \| 0.65 \| 0.6336 \| 0.2715 \| 0.2605 \| \| 0.3785 \| 21.0 \| 525 \| 0.5328 \| 0.695 \| 0.4699 \| 2.8365 \| 0.695 \| 0.6863 \| 0.2500 \| 0.1660 \| \| 0.3785 \| 22.0 \| 550 \| 0.6684 \| 0.62 \| 0.5374 \| 3.2087 \| 0.62 \| 0.6071 \| 0.2363 \| 0.2429 \| \| 0.3785 \| 23.0 \| 575 \| 0.7235 \| 0.615 \| 0.5613 \| 3.4750 \| 0.615 \| 0.5866 \| 0.2748 \| 0.2185 \| \| 0.3785 \| 24.0 \| 600 \| 0.6748 \| 0.67 \| 0.5028 \| 3.0615 \| 0.67 \| 0.6185 \| 0.2697 \| 0.2251 \| \| 0.3785 \| 25.0 \| 625 \| 0.6778 \| 0.645 \| 0.5068 \| 2.7608 \| 0.645 \| 0.6235 \| 0.2442 \| 0.1589 \| \| 0.3785 \| 26.0 \| 650 \| 0.7163 \| 0.6 \| 0.5690 \| 2.7443 \| 0.6 \| 0.5766 \| 0.2655 \| 0.2821 \| \| 0.3785 \| 27.0 \| 675 \| 0.7571 \| 0.635 \| 0.5278 \| 3.3670 \| 0.635 \| 0.6085 \| 0.2635 \| 0.2025 \| \| 0.3785 \| 28.0 \| 700 \| 0.6955 \| 0.605 \| 0.5718 \| 3.1717 \| 0.605 \| 0.5973 \| 0.3092 \| 0.2624 \| \| 0.3785 \| 29.0 \| 725 \| 0.7951 \| 0.585 \| 0.5869 \| 3.2346 \| 0.585 \| 0.5777 \| 0.2849 \| 0.3039 \| \| 0.3785 \| 30.0 \| 750 \| 0.5426 \| 0.655 \| 0.4898 \| 2.6384 \| 0.655 \| 0.6295 \| 0.2758 \| 0.1781 \| \| 0.3785 \| 31.0 \| 775 \| 0.7721 \| 0.6 \| 0.5956 \| 3.5480 \| 0.6 \| 0.5717 \| 0.2908 \| 0.2548 \| \| 0.3785 \| 32.0 \| 800 \| 0.6102 \| 0.65 \| 0.4974 \| 2.6613 \| 0.65 \| 0.6348 \| 0.2529 \| 0.1661 \| \| 0.3785 \| 33.0 \| 825 \| 0.7592 \| 0.62 \| 0.5666 \| 3.5174 \| 0.62 \| 0.5736 \| 0.2821 \| 0.2752 \| \| 0.3785 \| 34.0 \| 850 \| 0.6516 \| 0.655 \| 0.5283 \| 3.1254 \| 0.655 \| 0.6341 \| 0.2596 \| 0.1954 \| \| 0.3785 \| 35.0 \| 875 \| 0.6626 \| 0.65 \| 0.5329 \| 2.9794 \| 0.65 \| 0.6120 \| 0.2793 \| 0.2390 \| \| 0.3785 \| 36.0 \| 900 \| 0.6939 \| 0.66 \| 0.5190 \| 3.4020 \| 0.66 \| 0.6258 \| 0.2473 \| 0.1785 \| \| 0.3785 \| 37.0 \| 925 \| 0.7580 \| 0.605 \| 0.5970 \| 3.1545 \| 0.605 \| 0.5466 \| 0.2996 \| 0.2424 \| \| 0.3785 \| 38.0 \| 950 \| 0.6088 \| 0.655 \| 0.5187 \| 2.7205 \| 0.655 \| 0.6457 \| 0.2636 \| 0.2413 \| \| 0.3785 \| 39.0 \| 975 \| 0.7394 \| 0.605 \| 0.5815 \| 2.8167 \| 0.605 \| 0.5798 \| 0.2975 \| 0.2782 \| \| 0.0886 \| 40.0 \| 1000 \| 0.6910 \| 0.65 \| 0.5015 \| 2.9680 \| 0.65 \| 0.5993 \| 0.2697 \| 0.1652 \| \| 0.0886 \| 41.0 \| 1025 \| 0.6618 \| 0.635 \| 0.5752 \| 3.5088 \| 0.635 \| 0.5937 \| 0.2929 \| 0.2653 \| \| 0.0886 \| 42.0 \| 1050 \| 0.7742 \| 0.6 \| 0.5556 \| 3.5946 \| 0.6 \| 0.5644 \| 0.2556 \| 0.1974 \| \| 0.0886 \| 43.0 \| 1075 \| 0.7379 \| 0.62 \| 0.5589 \| 2.7882 \| 0.62 \| 0.6042 \| 0.3169 \| 0.2143 \| \| 0.0886 \| 44.0 \| 1100 \| 0.6702 \| 0.64 \| 0.5359 \| 2.9335 \| 0.64 \| 0.6088 \| 0.2765 \| 0.2133 \| \| 0.0886 \| 45.0 \| 1125 \| 0.8900 \| 0.585 \| 0.6173 \| 3.8349 \| 0.585 \| 0.5639 \| 0.2934 \| 0.2364 \| \| 0.0886 \| 46.0 \| 1150 \| 0.7800 \| 0.62 \| 0.5707 \| 3.2446 \| 0.62 \| 0.6171 \| 0.3002 \| 0.2156 \| \| 0.0886 \| 47.0 \| 1175 \| 0.8554 \| 0.57 \| 0.6256 \| 3.6828 \| 0.57 \| 0.5583 \| 0.3191 \| 0.2611 \| \| 0.0886 \| 48.0 \| 1200 \| 0.6486 \| 0.67 \| 0.4911 \| 3.4792 \| 0.67 \| 0.6449 \| 0.2741 \| 0.1870 \| \| 0.0886 \| 49.0 \| 1225 \| 0.7315 \| 0.59 \| 0.5829 \| 3.4916 \| 0.59 \| 0.5963 \| 0.2720 \| 0.2101 \| \| 0.0886 \| 50.0 \| 1250 \| 0.6939 \| 0.665 \| 0.5022 \| 2.9091 \| 0.665 \| 0.6362 \| 0.2743 \| 0.1829 \| \| 0.0886 \| 51.0 \| 1275 \| 0.7256 \| 0.625 \| 0.5687 \| 3.4914 \| 0.625 \| 0.5740 \| 0.2943 \| 0.2493 \| \| 0.0886 \| 52.0 \| 1300 \| 0.6374 \| 0.66 \| 0.5144 \| 2.7071 \| 0.66 \| 0.6297 \| 0.2529 \| 0.2006 \| \| 0.0886 \| 53.0 \| 1325 \| 0.7862 \| 0.645 \| 0.5470 \| 3.2902 \| 0.645 \| 0.6385 \| 0.2899 \| 0.2053 \| \| 0.0886 \| 54.0 \| 1350 \| 0.7717 \| 0.63 \| 0.5762 \| 3.8614 \| 0.63 \| 0.6027 \| 0.2954 \| 0.2150 \| \| 0.0886 \| 55.0 \| 1375 \| 0.6664 \| 0.675 \| 0.5120 \| 3.1014 \| 0.675 \| 0.6582 \| 0.2850 \| 0.1842 \| \| 0.0886 \| 56.0 \| 1400 \| 0.6957 \| 0.615 \| 0.5602 \| 3.0253 \| 0.615 \| 0.5977 \| 0.3033 \| 0.2229 \| \| 0.0886 \| 57.0 \| 1425 \| 0.6794 \| 0.64 \| 0.5581 \| 3.0174 \| 0.64 \| 0.6205 \| 0.2802 \| 0.2056 \| \| 0.0886 \| 58.0 \| 1450 \| 0.6345 \| 0.655 \| 0.5162 \| 2.7909 \| 0.655 \| 0.6422 \| 0.2856 \| 0.2789 \| \| 0.0886 \| 59.0 \| 1475 \| 0.6447 \| 0.655 \| 0.5271 \| 2.9860 \| 0.655 \| 0.6432 \| 0.2735 \| 0.1774 \| \| 0.0219 \| 60.0 \| 1500 \| 0.7042 \| 0.665 \| 0.5404 \| 3.1132 \| 0.665 \| 0.6268 \| 0.2871 \| 0.2981 \| \| 0.0219 \| 61.0 \| 1525 \| 0.7288 \| 0.64 \| 0.5486 \| 3.3084 \| 0.64 \| 0.6225 \| 0.2869 \| 0.1861 \| \| 0.0219 \| 62.0 \| 1550 \| 0.6605 \| 0.69 \| 0.5078 \| 2.9123 \| 0.69 \| 0.6642 \| 0.2668 \| 0.2487 \| \| 0.0219 \| 63.0 \| 1575 \| 0.5905 \| 0.715 \| 0.4712 \| 3.4707 \| 0.715 \| 0.7013 \| 0.2548 \| 0.2257 \| \| 0.0219 \| 64.0 \| 1600 \| 0.6209 \| 0.69 \| 0.4940 \| 2.6873 \| 0.69 \| 0.6770 \| 0.2771 \| 0.2263 \| \| 0.0219 \| 65.0 \| 1625 \| 0.6039 \| 0.68 \| 0.4914 \| 2.6448 \| 0.68 \| 0.6620 \| 0.2926 \| 0.2184 \| \| 0.0219 \| 66.0 \| 1650 \| 0.5985 \| 0.69 \| 0.4918 \| 2.7592 \| 0.69 \| 0.6757 \| 0.2844 \| 0.2181 \| \| 0.0219 \| 67.0 \| 1675 \| 0.5955 \| 0.69 \| 0.4903 \| 2.7566 \| 0.69 \| 0.6757 \| 0.2617 \| 0.2227 \| \| 0.0219 \| 68.0 \| 1700 \| 0.5944 \| 0.69 \| 0.4898 \| 2.7730 \| 0.69 \| 0.6757 \| 0.2683 \| 0.2211 \| \| 0.0219 \| 69.0 \| 1725 \| 0.5934 \| 0.695 \| 0.4893 \| 2.7575 \| 0.695 \| 0.6823 \| 0.2666 \| 0.2171 \| \| 0.0219 \| 70.0 \| 1750 \| 0.5913 \| 0.695 \| 0.4890 \| 2.7043 \| 0.695 \| 0.6823 \| 0.2649 \| 0.2160 \| \| 0.0219 \| 71.0 \| 1775 \| 0.5904 \| 0.69 \| 0.4888 \| 2.7476 \| 0.69 \| 0.6742 \| 0.2718 \| 0.2163 \| \| 0.0219 \| 72.0 \| 1800 \| 0.5895 \| 0.69 \| 0.4883 \| 2.7463 \| 0.69 \| 0.6742 \| 0.2714 \| 0.2160 \| \| 0.0219 \| 73.0 \| 1825 \| 0.5882 \| 0.69 \| 0.4877 \| 2.7478 \| 0.69 \| 0.6742 \| 0.2779 \| 0.2171 \| \| 0.0219 \| 74.0 \| 1850 \| 0.5878 \| 0.69 \| 0.4876 \| 2.7489 \| 0.69 \| 0.6742 \| 0.2813 \| 0.2169 \| \| 0.0219 \| 75.0 \| 1875 \| 0.5879 \| 0.69 \| 0.4871 \| 2.7592 \| 0.69 \| 0.6742 \| 0.2765 \| 0.2185 \| \| 0.0219 \| 76.0 \| 1900 \| 0.5868 \| 0.69 \| 0.4870 \| 2.8058 \| 0.69 \| 0.6742 \| 0.2670 \| 0.2183 \| \| 0.0219 \| 77.0 \| 1925 \| 0.5843 \| 0.69 \| 0.4864 \| 2.8037 \| 0.69 \| 0.6745 \| 0.2764 \| 0.2185 \| \| 0.0219 \| 78.0 \| 1950 \| 0.5844 \| 0.69 \| 0.4862 \| 2.8040 \| 0.69 \| 0.6745 \| 0.2788 \| 0.2191 \| \| 0.0219 \| 79.0 \| 1975 \| 0.5831 \| 0.69 \| 0.4857 \| 2.8018 \| 0.69 \| 0.6745 \| 0.2655 \| 0.2178 \| \| 0.0013 \| 80.0 \| 2000 \| 0.5846 \| 0.69 \| 0.4858 \| 2.8022 \| 0.69 \| 0.6745 \| 0.2633 \| 0.2182 \| \| 0.0013 \| 81.0 \| 2025 \| 0.5821 \| 0.69 \| 0.4851 \| 2.8020 \| 0.69 \| 0.6745 \| 0.2750 \| 0.2177 \| \| 0.0013 \| 82.0 \| 2050 \| 0.5826 \| 0.685 \| 0.4852 \| 2.8013 \| 0.685 \| 0.6713 \| 0.2728 \| 0.2180 \| \| 0.0013 \| 83.0 \| 2075 \| 0.5821 \| 0.685 \| 0.4851 \| 2.8005 \| 0.685 \| 0.6713 \| 0.2705 \| 0.2179 \| \| 0.0013 \| 84.0 \| 2100 \| 0.5817 \| 0.685 \| 0.4850 \| 2.8007 \| 0.685 \| 0.6713 \| 0.2773 \| 0.2180 \| \| 0.0013 \| 85.0 \| 2125 \| 0.5814 \| 0.685 \| 0.4849 \| 2.7998 \| 0.685 \| 0.6713 \| 0.2740 \| 0.2176 \| \| 0.0013 \| 86.0 \| 2150 \| 0.5814 \| 0.685 \| 0.4848 \| 2.7997 \| 0.685 \| 0.6713 \| 0.2686 \| 0.2174 \| \| 0.0013 \| 87.0 \| 2175 \| 0.5807 \| 0.68 \| 0.4847 \| 2.7994 \| 0.68 \| 0.6624 \| 0.2658 \| 0.2186 \| \| 0.0013 \| 88.0 \| 2200 \| 0.5803 \| 0.68 \| 0.4845 \| 2.7992 \| 0.68 \| 0.6624 \| 0.2703 \| 0.2180 \| \| 0.0013 \| 89.0 \| 2225 \| 0.5805 \| 0.68 \| 0.4846 \| 2.7990 \| 0.68 \| 0.6624 \| 0.2632 \| 0.2194 \| \| 0.0013 \| 90.0 \| 2250 \| 0.5803 \| 0.685 \| 0.4846 \| 2.7979 \| 0.685 \| 0.6703 \| 0.2596 \| 0.2183 \| \| 0.0013 \| 91.0 \| 2275 \| 0.5805 \| 0.685 \| 0.4847 \| 2.7980 \| 0.685 \| 0.6703 \| 0.2674 \| 0.2183 \| \| 0.0013 \| 92.0 \| 2300 \| 0.5805 \| 0.685 \| 0.4846 \| 2.7993 \| 0.685 \| 0.6703 \| 0.2562 \| 0.2181 \| \| 0.0013 \| 93.0 \| 2325 \| 0.5802 \| 0.68 \| 0.4847 \| 2.7974 \| 0.68 \| 0.6624 \| 0.2598 \| 0.2182 \| \| 0.0013 \| 94.0 \| 2350 \| 0.5800 \| 0.68 \| 0.4846 \| 2.7981 \| 0.68 \| 0.6624 \| 0.2613 \| 0.2175 \| \| 0.0013 \| 95.0 \| 2375 \| 0.5796 \| 0.68 \| 0.4846 \| 2.7985 \| 0.68 \| 0.6624 \| 0.2589 \| 0.2179 \| \| 0.0013 \| 96.0 \| 2400 \| 0.5799 \| 0.68 \| 0.4846 \| 2.7980 \| 0.68 \| 0.6624 \| 0.2560 \| 0.2183 \| \| 0.0013 \| 97.0 \| 2425 \| 0.5796 \| 0.68 \| 0.4846 \| 2.7978 \| 0.68 \| 0.6624 \| 0.2588 \| 0.2175 \| \| 0.0013 \| 98.0 \| 2450 \| 0.5798 \| 0.68 \| 0.4846 \| 2.7977 \| 0.68 \| 0.6624 \| 0.2589 \| 0.2179 \| \| 0.0013 \| 99.0 \| 2475 \| 0.5797 \| 0.68 \| 0.4846 \| 2.7977 \| 0.68 \| 0.6624 \| 0.2589 \| 0.2178 \| \| 0.0003 \| 100.0 \| 2500 \| 0.5797 \| 0.68 \| 0.4846 \| 2.7977 \| 0.68 \| 0.6624 \| 0.2589 \| 0.2179 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
achyudev/vit-base-patch16-224-in21k	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3383 - F1: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 10 \| 0.7369 \| 0.9461 \| \| No log \| 2.0 \| 20 \| 0.6143 \| 0.9526 \| \| No log \| 3.0 \| 30 \| 0.5401 \| 0.9594 \| \| No log \| 4.0 \| 40 \| 0.4809 \| 0.9662 \| \| No log \| 5.0 \| 50 \| 0.4219 \| 0.9595 \| \| No log \| 6.0 \| 60 \| 0.3864 \| 0.9730 \| \| No log \| 7.0 \| 70 \| 0.3628 \| 0.9730 \| \| No log \| 8.0 \| 80 \| 0.3496 \| 0.9732 \| \| No log \| 9.0 \| 90 \| 0.3419 \| 0.9799 \| \| No log \| 10.0 \| 100 \| 0.3383 \| 0.9799 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "viola", "cello", "sax", "flute", "trumpet", "oboe" ]
Akshay-123/vit-base-patch16-224-in21k	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7692 - F1: 0.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 10 \| 1.5877 \| 0.6675 \| \| No log \| 2.0 \| 20 \| 1.4149 \| 0.8402 \| \| No log \| 3.0 \| 30 \| 1.2687 \| 0.8917 \| \| No log \| 4.0 \| 40 \| 1.1382 \| 0.9113 \| \| No log \| 5.0 \| 50 \| 1.0214 \| 0.9523 \| \| No log \| 6.0 \| 60 \| 0.9285 \| 0.9662 \| \| No log \| 7.0 \| 70 \| 0.8601 \| 0.9728 \| \| No log \| 8.0 \| 80 \| 0.8089 \| 0.9797 \| \| No log \| 9.0 \| 90 \| 0.7796 \| 0.9865 \| \| No log \| 10.0 \| 100 \| 0.7692 \| 0.9865 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "cello", "oboe", "sax", "viola", "trumpet", "flute" ]
Schwab/vit-base-patch16-224-finetuned-flower	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
3b3r/vit_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0414 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1498 \| 3.85 \| 500 \| 0.0414 \| 0.9925 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "angular_leaf_spot", "bean_rust", "healthy" ]
jordyvl/vit-base_rvl_tobacco	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_rvl_tobacco This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4152 - Accuracy: 0.905 - Brier Loss: 0.1584 - Nll: 0.7130 - F1 Micro: 0.905 - F1 Macro: 0.9056 - Ece: 0.1601 - Aurc: 0.0196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 0.96 \| 3 \| 2.3234 \| 0.045 \| 0.9050 \| 9.6090 \| 0.045 \| 0.0479 \| 0.1570 \| 0.9674 \| \| No log \| 1.96 \| 6 \| 2.3007 \| 0.05 \| 0.9005 \| 8.5690 \| 0.0500 \| 0.0549 \| 0.1567 \| 0.9599 \| \| No log \| 2.96 \| 9 \| 2.2614 \| 0.095 \| 0.8924 \| 6.9011 \| 0.095 \| 0.0853 \| 0.1807 \| 0.9128 \| \| No log \| 3.96 \| 12 \| 2.2062 \| 0.255 \| 0.8804 \| 5.5442 \| 0.255 \| 0.1609 \| 0.2738 \| 0.7469 \| \| No log \| 4.96 \| 15 \| 2.1348 \| 0.385 \| 0.8636 \| 4.0613 \| 0.3850 \| 0.2330 \| 0.3605 \| 0.4157 \| \| No log \| 5.96 \| 18 \| 2.0473 \| 0.48 \| 0.8410 \| 2.5353 \| 0.48 \| 0.3152 \| 0.4376 \| 0.2329 \| \| No log \| 6.96 \| 21 \| 1.9483 \| 0.64 \| 0.8128 \| 2.0469 \| 0.64 \| 0.5131 \| 0.5355 \| 0.1314 \| \| No log \| 7.96 \| 24 \| 1.8371 \| 0.735 \| 0.7783 \| 1.7309 \| 0.735 \| 0.6333 \| 0.5897 \| 0.0802 \| \| No log \| 8.96 \| 27 \| 1.7227 \| 0.775 \| 0.7393 \| 1.3371 \| 0.775 \| 0.6937 \| 0.6049 \| 0.0560 \| \| No log \| 9.96 \| 30 \| 1.6124 \| 0.805 \| 0.6978 \| 1.1320 \| 0.805 \| 0.7319 \| 0.5981 \| 0.0462 \| \| No log \| 10.96 \| 33 \| 1.4990 \| 0.82 \| 0.6518 \| 0.9973 \| 0.82 \| 0.7658 \| 0.5882 \| 0.0444 \| \| No log \| 11.96 \| 36 \| 1.3922 \| 0.855 \| 0.6064 \| 0.8830 \| 0.855 \| 0.8127 \| 0.5823 \| 0.0397 \| \| No log \| 12.96 \| 39 \| 1.2985 \| 0.865 \| 0.5653 \| 0.8957 \| 0.865 \| 0.8350 \| 0.5604 \| 0.0365 \| \| No log \| 13.96 \| 42 \| 1.2141 \| 0.89 \| 0.5271 \| 0.6892 \| 0.89 \| 0.8733 \| 0.5564 \| 0.0331 \| \| No log \| 14.96 \| 45 \| 1.1402 \| 0.895 \| 0.4926 \| 0.6695 \| 0.895 \| 0.8803 \| 0.5341 \| 0.0321 \| \| No log \| 15.96 \| 48 \| 1.0699 \| 0.91 \| 0.4596 \| 0.6407 \| 0.91 \| 0.8999 \| 0.5185 \| 0.0285 \| \| No log \| 16.96 \| 51 \| 1.0037 \| 0.91 \| 0.4282 \| 0.6163 \| 0.91 \| 0.8979 \| 0.4831 \| 0.0270 \| \| No log \| 17.96 \| 54 \| 0.9457 \| 0.915 \| 0.4004 \| 0.6126 \| 0.915 \| 0.9011 \| 0.4618 \| 0.0247 \| \| No log \| 18.96 \| 57 \| 0.8914 \| 0.915 \| 0.3742 \| 0.6066 \| 0.915 \| 0.9011 \| 0.4426 \| 0.0242 \| \| No log \| 19.96 \| 60 \| 0.8405 \| 0.92 \| 0.3495 \| 0.5898 \| 0.92 \| 0.9102 \| 0.4314 \| 0.0216 \| \| No log \| 20.96 \| 63 \| 0.7995 \| 0.915 \| 0.3291 \| 0.5934 \| 0.915 \| 0.9049 \| 0.4033 \| 0.0204 \| \| No log \| 21.96 \| 66 \| 0.7583 \| 0.915 \| 0.3089 \| 0.5883 \| 0.915 \| 0.9049 \| 0.3818 \| 0.0206 \| \| No log \| 22.96 \| 69 \| 0.7228 \| 0.915 \| 0.2915 \| 0.5835 \| 0.915 \| 0.9049 \| 0.3707 \| 0.0199 \| \| No log \| 23.96 \| 72 \| 0.6889 \| 0.925 \| 0.2747 \| 0.5703 \| 0.925 \| 0.9169 \| 0.3649 \| 0.0191 \| \| No log \| 24.96 \| 75 \| 0.6624 \| 0.925 \| 0.2614 \| 0.5769 \| 0.925 \| 0.9200 \| 0.3375 \| 0.0190 \| \| No log \| 25.96 \| 78 \| 0.6373 \| 0.925 \| 0.2491 \| 0.5764 \| 0.925 \| 0.9218 \| 0.3206 \| 0.0191 \| \| No log \| 26.96 \| 81 \| 0.6106 \| 0.93 \| 0.2363 \| 0.5570 \| 0.93 \| 0.9251 \| 0.3276 \| 0.0186 \| \| No log \| 27.96 \| 84 \| 0.5945 \| 0.93 \| 0.2281 \| 0.5721 \| 0.93 \| 0.9251 \| 0.3201 \| 0.0187 \| \| No log \| 28.96 \| 87 \| 0.5780 \| 0.92 \| 0.2206 \| 0.5668 \| 0.92 \| 0.9190 \| 0.3008 \| 0.0200 \| \| No log \| 29.96 \| 90 \| 0.5613 \| 0.925 \| 0.2125 \| 0.5709 \| 0.925 \| 0.9218 \| 0.2961 \| 0.0191 \| \| No log \| 30.96 \| 93 \| 0.5456 \| 0.925 \| 0.2051 \| 0.6155 \| 0.925 \| 0.9175 \| 0.2764 \| 0.0182 \| \| No log \| 31.96 \| 96 \| 0.5354 \| 0.91 \| 0.2008 \| 0.6139 \| 0.91 \| 0.9104 \| 0.2600 \| 0.0187 \| \| No log \| 32.96 \| 99 \| 0.5248 \| 0.91 \| 0.1961 \| 0.6078 \| 0.91 \| 0.9104 \| 0.2610 \| 0.0194 \| \| No log \| 33.96 \| 102 \| 0.5151 \| 0.91 \| 0.1915 \| 0.6158 \| 0.91 \| 0.9084 \| 0.2529 \| 0.0186 \| \| No log \| 34.96 \| 105 \| 0.5066 \| 0.91 \| 0.1880 \| 0.6121 \| 0.91 \| 0.9084 \| 0.2409 \| 0.0186 \| \| No log \| 35.96 \| 108 \| 0.4986 \| 0.91 \| 0.1846 \| 0.6070 \| 0.91 \| 0.9084 \| 0.2429 \| 0.0186 \| \| No log \| 36.96 \| 111 \| 0.4920 \| 0.91 \| 0.1817 \| 0.6208 \| 0.91 \| 0.9084 \| 0.2380 \| 0.0187 \| \| No log \| 37.96 \| 114 \| 0.4858 \| 0.91 \| 0.1793 \| 0.6081 \| 0.91 \| 0.9084 \| 0.2319 \| 0.0185 \| \| No log \| 38.96 \| 117 \| 0.4792 \| 0.91 \| 0.1766 \| 0.6044 \| 0.91 \| 0.9084 \| 0.2276 \| 0.0184 \| \| No log \| 39.96 \| 120 \| 0.4753 \| 0.91 \| 0.1749 \| 0.6671 \| 0.91 \| 0.9084 \| 0.2245 \| 0.0185 \| \| No log \| 40.96 \| 123 \| 0.4704 \| 0.905 \| 0.1731 \| 0.6137 \| 0.905 \| 0.9056 \| 0.2321 \| 0.0186 \| \| No log \| 41.96 \| 126 \| 0.4656 \| 0.91 \| 0.1714 \| 0.6028 \| 0.91 \| 0.9084 \| 0.2259 \| 0.0187 \| \| No log \| 42.96 \| 129 \| 0.4624 \| 0.91 \| 0.1703 \| 0.6048 \| 0.91 \| 0.9084 \| 0.2080 \| 0.0189 \| \| No log \| 43.96 \| 132 \| 0.4604 \| 0.905 \| 0.1695 \| 0.6674 \| 0.905 \| 0.9056 \| 0.2167 \| 0.0187 \| \| No log \| 44.96 \| 135 \| 0.4553 \| 0.905 \| 0.1678 \| 0.6190 \| 0.905 \| 0.9056 \| 0.2130 \| 0.0185 \| \| No log \| 45.96 \| 138 \| 0.4512 \| 0.905 \| 0.1663 \| 0.6002 \| 0.905 \| 0.9056 \| 0.2182 \| 0.0186 \| \| No log \| 46.96 \| 141 \| 0.4513 \| 0.905 \| 0.1665 \| 0.6681 \| 0.905 \| 0.9056 \| 0.1902 \| 0.0185 \| \| No log \| 47.96 \| 144 \| 0.4480 \| 0.905 \| 0.1656 \| 0.6661 \| 0.905 \| 0.9056 \| 0.1900 \| 0.0186 \| \| No log \| 48.96 \| 147 \| 0.4451 \| 0.905 \| 0.1647 \| 0.6085 \| 0.905 \| 0.9056 \| 0.1969 \| 0.0185 \| \| No log \| 49.96 \| 150 \| 0.4429 \| 0.905 \| 0.1638 \| 0.6729 \| 0.905 \| 0.9056 \| 0.1954 \| 0.0186 \| \| No log \| 50.96 \| 153 \| 0.4416 \| 0.905 \| 0.1637 \| 0.7300 \| 0.905 \| 0.9056 \| 0.1730 \| 0.0188 \| \| No log \| 51.96 \| 156 \| 0.4390 \| 0.905 \| 0.1627 \| 0.6832 \| 0.905 \| 0.9056 \| 0.1881 \| 0.0187 \| \| No log \| 52.96 \| 159 \| 0.4377 \| 0.905 \| 0.1625 \| 0.6708 \| 0.905 \| 0.9056 \| 0.1724 \| 0.0187 \| \| No log \| 53.96 \| 162 \| 0.4360 \| 0.905 \| 0.1620 \| 0.7300 \| 0.905 \| 0.9056 \| 0.1714 \| 0.0189 \| \| No log \| 54.96 \| 165 \| 0.4338 \| 0.905 \| 0.1613 \| 0.6734 \| 0.905 \| 0.9056 \| 0.1923 \| 0.0190 \| \| No log \| 55.96 \| 168 \| 0.4321 \| 0.905 \| 0.1609 \| 0.6635 \| 0.905 \| 0.9056 \| 0.1846 \| 0.0189 \| \| No log \| 56.96 \| 171 \| 0.4326 \| 0.905 \| 0.1614 \| 0.6722 \| 0.905 \| 0.9056 \| 0.1851 \| 0.0190 \| \| No log \| 57.96 \| 174 \| 0.4322 \| 0.905 \| 0.1613 \| 0.7871 \| 0.905 \| 0.9056 \| 0.1850 \| 0.0191 \| \| No log \| 58.96 \| 177 \| 0.4286 \| 0.905 \| 0.1600 \| 0.6660 \| 0.905 \| 0.9056 \| 0.1733 \| 0.0190 \| \| No log \| 59.96 \| 180 \| 0.4267 \| 0.905 \| 0.1596 \| 0.6581 \| 0.905 \| 0.9056 \| 0.1720 \| 0.0190 \| \| No log \| 60.96 \| 183 \| 0.4277 \| 0.905 \| 0.1601 \| 0.7252 \| 0.905 \| 0.9056 \| 0.1772 \| 0.0189 \| \| No log \| 61.96 \| 186 \| 0.4274 \| 0.905 \| 0.1601 \| 0.7841 \| 0.905 \| 0.9056 \| 0.1866 \| 0.0192 \| \| No log \| 62.96 \| 189 \| 0.4264 \| 0.905 \| 0.1598 \| 0.7830 \| 0.905 \| 0.9056 \| 0.1669 \| 0.0191 \| \| No log \| 63.96 \| 192 \| 0.4246 \| 0.905 \| 0.1595 \| 0.7188 \| 0.905 \| 0.9056 \| 0.1671 \| 0.0191 \| \| No log \| 64.96 \| 195 \| 0.4236 \| 0.905 \| 0.1592 \| 0.7170 \| 0.905 \| 0.9056 \| 0.1762 \| 0.0193 \| \| No log \| 65.96 \| 198 \| 0.4238 \| 0.905 \| 0.1594 \| 0.7235 \| 0.905 \| 0.9056 \| 0.1757 \| 0.0192 \| \| No log \| 66.96 \| 201 \| 0.4227 \| 0.905 \| 0.1591 \| 0.7218 \| 0.905 \| 0.9056 \| 0.1724 \| 0.0192 \| \| No log \| 67.96 \| 204 \| 0.4220 \| 0.905 \| 0.1590 \| 0.7195 \| 0.905 \| 0.9056 \| 0.1715 \| 0.0191 \| \| No log \| 68.96 \| 207 \| 0.4214 \| 0.905 \| 0.1589 \| 0.7201 \| 0.905 \| 0.9056 \| 0.1708 \| 0.0191 \| \| No log \| 69.96 \| 210 \| 0.4210 \| 0.905 \| 0.1588 \| 0.7210 \| 0.905 \| 0.9056 \| 0.1703 \| 0.0193 \| \| No log \| 70.96 \| 213 \| 0.4211 \| 0.905 \| 0.1590 \| 0.7226 \| 0.905 \| 0.9056 \| 0.1697 \| 0.0193 \| \| No log \| 71.96 \| 216 \| 0.4201 \| 0.905 \| 0.1587 \| 0.7165 \| 0.905 \| 0.9056 \| 0.1785 \| 0.0193 \| \| No log \| 72.96 \| 219 \| 0.4194 \| 0.905 \| 0.1587 \| 0.7145 \| 0.905 \| 0.9056 \| 0.1780 \| 0.0194 \| \| No log \| 73.96 \| 222 \| 0.4194 \| 0.905 \| 0.1587 \| 0.7189 \| 0.905 \| 0.9056 \| 0.1777 \| 0.0194 \| \| No log \| 74.96 \| 225 \| 0.4192 \| 0.905 \| 0.1587 \| 0.7193 \| 0.905 \| 0.9056 \| 0.1770 \| 0.0194 \| \| No log \| 75.96 \| 228 \| 0.4188 \| 0.905 \| 0.1586 \| 0.7186 \| 0.905 \| 0.9056 \| 0.1764 \| 0.0192 \| \| No log \| 76.96 \| 231 \| 0.4180 \| 0.905 \| 0.1585 \| 0.7148 \| 0.905 \| 0.9056 \| 0.1786 \| 0.0192 \| \| No log \| 77.96 \| 234 \| 0.4174 \| 0.905 \| 0.1584 \| 0.7121 \| 0.905 \| 0.9056 \| 0.1746 \| 0.0193 \| \| No log \| 78.96 \| 237 \| 0.4178 \| 0.905 \| 0.1585 \| 0.7159 \| 0.905 \| 0.9056 \| 0.1720 \| 0.0195 \| \| No log \| 79.96 \| 240 \| 0.4177 \| 0.905 \| 0.1586 \| 0.7161 \| 0.905 \| 0.9056 \| 0.1627 \| 0.0195 \| \| No log \| 80.96 \| 243 \| 0.4173 \| 0.905 \| 0.1585 \| 0.7147 \| 0.905 \| 0.9056 \| 0.1627 \| 0.0195 \| \| No log \| 81.96 \| 246 \| 0.4171 \| 0.905 \| 0.1585 \| 0.7159 \| 0.905 \| 0.9056 \| 0.1650 \| 0.0195 \| \| No log \| 82.96 \| 249 \| 0.4162 \| 0.905 \| 0.1582 \| 0.7135 \| 0.905 \| 0.9056 \| 0.1742 \| 0.0194 \| \| No log \| 83.96 \| 252 \| 0.4163 \| 0.905 \| 0.1584 \| 0.7138 \| 0.905 \| 0.9056 \| 0.1522 \| 0.0196 \| \| No log \| 84.96 \| 255 \| 0.4161 \| 0.905 \| 0.1583 \| 0.7136 \| 0.905 \| 0.9056 \| 0.1616 \| 0.0195 \| \| No log \| 85.96 \| 258 \| 0.4163 \| 0.905 \| 0.1585 \| 0.7143 \| 0.905 \| 0.9056 \| 0.1615 \| 0.0196 \| \| No log \| 86.96 \| 261 \| 0.4161 \| 0.905 \| 0.1585 \| 0.7132 \| 0.905 \| 0.9056 \| 0.1614 \| 0.0195 \| \| No log \| 87.96 \| 264 \| 0.4159 \| 0.905 \| 0.1584 \| 0.7133 \| 0.905 \| 0.9056 \| 0.1514 \| 0.0195 \| \| No log \| 88.96 \| 267 \| 0.4157 \| 0.905 \| 0.1584 \| 0.7132 \| 0.905 \| 0.9056 \| 0.1513 \| 0.0195 \| \| No log \| 89.96 \| 270 \| 0.4156 \| 0.905 \| 0.1584 \| 0.7134 \| 0.905 \| 0.9056 \| 0.1511 \| 0.0195 \| \| No log \| 90.96 \| 273 \| 0.4153 \| 0.905 \| 0.1583 \| 0.7124 \| 0.905 \| 0.9056 \| 0.1605 \| 0.0195 \| \| No log \| 91.96 \| 276 \| 0.4153 \| 0.905 \| 0.1584 \| 0.7121 \| 0.905 \| 0.9056 \| 0.1604 \| 0.0195 \| \| No log \| 92.96 \| 279 \| 0.4154 \| 0.905 \| 0.1584 \| 0.7127 \| 0.905 \| 0.9056 \| 0.1603 \| 0.0195 \| \| No log \| 93.96 \| 282 \| 0.4154 \| 0.905 \| 0.1585 \| 0.7131 \| 0.905 \| 0.9056 \| 0.1603 \| 0.0195 \| \| No log \| 94.96 \| 285 \| 0.4154 \| 0.905 \| 0.1585 \| 0.7132 \| 0.905 \| 0.9056 \| 0.1603 \| 0.0195 \| \| No log \| 95.96 \| 288 \| 0.4154 \| 0.905 \| 0.1585 \| 0.7135 \| 0.905 \| 0.9056 \| 0.1603 \| 0.0196 \| \| No log \| 96.96 \| 291 \| 0.4153 \| 0.905 \| 0.1585 \| 0.7133 \| 0.905 \| 0.9056 \| 0.1602 \| 0.0195 \| \| No log \| 97.96 \| 294 \| 0.4152 \| 0.905 \| 0.1584 \| 0.7132 \| 0.905 \| 0.9056 \| 0.1601 \| 0.0196 \| \| No log \| 98.96 \| 297 \| 0.4152 \| 0.905 \| 0.1584 \| 0.7130 \| 0.905 \| 0.9056 \| 0.1601 \| 0.0196 \| \| No log \| 99.96 \| 300 \| 0.4152 \| 0.905 \| 0.1584 \| 0.7130 \| 0.905 \| 0.9056 \| 0.1601 \| 0.0196 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
XO-Appleton/vit-base-patch16-224-in21k-MR	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # XO-Appleton/vit-base-patch16-224-in21k-MR This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0042 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 0.0126 - Validation Accuracy: 0.9983 - Validation Top-3-accuracy: 1.0 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4485, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 0.1624 \| 0.9544 \| 1.0 \| 0.0380 \| 0.9933 \| 1.0 \| 0 \| \| 0.0178 \| 0.9979 \| 1.0 \| 0.0197 \| 0.9966 \| 1.0 \| 1 \| \| 0.0063 \| 1.0 \| 1.0 \| 0.0139 \| 0.9983 \| 1.0 \| 2 \| \| 0.0042 \| 1.0 \| 1.0 \| 0.0126 \| 0.9983 \| 1.0 \| 3 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "flip", "notflip" ]
sghirardelli/vit-base-patch16-224-in21k-rgbd	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-rgbd This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5496 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 0.3955 - Validation Accuracy: 0.9994 - Validation Top-3-accuracy: 1.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 1.6822 \| 0.9392 \| 0.9664 \| 0.7810 \| 0.9994 \| 1.0 \| 0 \| \| 0.5496 \| 1.0 \| 1.0 \| 0.3955 \| 0.9994 \| 1.0 \| 1 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "apple", "ball", "cereal_box", "coffee_mug", "comb", "dry_battery", "flashlight", "food_bag", "food_box", "food_can", "food_cup", "food_jar", "banana", "garlic", "glue_stick", "greens", "hand_towel", "instant_noodles", "keyboard", "kleenex", "lemon", "lightbulb", "lime", "bell_pepper", "marker", "mushroom", "notebook", "onion", "orange", "peach", "pear", "pitcher", "plate", "pliers", "binder", "potato", "rubber_eraser", "scissors", "shampoo", "soda_can", "sponge", "stapler", "tomato", "toothbrush", "toothpaste", "bowl", "water_bottle", "calculator", "camera", "cap", "cell_phone" ]
ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0275 - Accuracy: 0.9939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.46 \| 1.0 \| 199 \| 0.3950 \| 0.8482 \| \| 0.2048 \| 2.0 \| 398 \| 0.1886 \| 0.9189 \| \| 0.182 \| 3.0 \| 597 \| 0.1382 \| 0.9481 \| \| 0.0826 \| 4.0 \| 796 \| 0.0760 \| 0.9694 \| \| 0.0886 \| 5.0 \| 995 \| 0.0600 \| 0.9788 \| \| 0.0896 \| 6.0 \| 1194 \| 0.0523 \| 0.9802 \| \| 0.0774 \| 7.0 \| 1393 \| 0.0482 \| 0.9826 \| \| 0.0876 \| 8.0 \| 1592 \| 0.0289 \| 0.9877 \| \| 0.1105 \| 9.0 \| 1791 \| 0.0580 \| 0.9821 \| \| 0.0289 \| 10.0 \| 1990 \| 0.0294 \| 0.9925 \| \| 0.0594 \| 11.0 \| 2189 \| 0.0331 \| 0.9906 \| \| 0.0011 \| 12.0 \| 2388 \| 0.0275 \| 0.9939 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "benign", "malignant" ]
ALM-AHME/convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0353 - Accuracy: 0.9901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5207 \| 1.0 \| 199 \| 0.4745 \| 0.8887 \| \| 0.2029 \| 2.0 \| 398 \| 0.2072 \| 0.9401 \| \| 0.1615 \| 3.0 \| 597 \| 0.1489 \| 0.9547 \| \| 0.1662 \| 4.0 \| 796 \| 0.1312 \| 0.9562 \| \| 0.1986 \| 5.0 \| 995 \| 0.1026 \| 0.9698 \| \| 0.0854 \| 6.0 \| 1194 \| 0.0583 \| 0.9802 \| \| 0.0538 \| 7.0 \| 1393 \| 0.0568 \| 0.9835 \| \| 0.0977 \| 8.0 \| 1592 \| 0.0654 \| 0.9793 \| \| 0.6971 \| 9.0 \| 1791 \| 0.6821 \| 0.5450 \| \| 0.211 \| 10.0 \| 1990 \| 0.1654 \| 0.9326 \| \| 0.1775 \| 11.0 \| 2189 \| 0.0859 \| 0.9665 \| \| 0.0042 \| 12.0 \| 2388 \| 0.0353 \| 0.9901 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "benign", "malignant" ]
akar49/mri_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # akar49/mri_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1032 - Validation Loss: 0.1556 - Train Accuracy: 0.9367 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'momentum': 0.0, 'nesterov': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6447 \| 0.6133 \| 0.7004 \| 0 \| \| 0.5405 \| 0.5010 \| 0.8256 \| 1 \| \| 0.4181 \| 0.3917 \| 0.8650 \| 2 \| \| 0.3122 \| 0.3189 \| 0.9058 \| 3 \| \| 0.2474 \| 0.3069 \| 0.8875 \| 4 \| \| 0.2021 \| 0.2733 \| 0.9044 \| 5 \| \| 0.1745 \| 0.2455 \| 0.9100 \| 6 \| \| 0.1591 \| 0.2203 \| 0.9212 \| 7 \| \| 0.1450 \| 0.2350 \| 0.9142 \| 8 \| \| 0.1397 \| 0.2122 \| 0.9198 \| 9 \| \| 0.1227 \| 0.2098 \| 0.9212 \| 10 \| \| 0.1169 \| 0.1754 \| 0.9325 \| 11 \| \| 0.1080 \| 0.1782 \| 0.9339 \| 12 \| \| 0.0971 \| 0.1705 \| 0.9353 \| 13 \| \| 0.1032 \| 0.1556 \| 0.9367 \| 14 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "notumor", "tumor" ]
jvadlamudi2/swin-tiny-patch4-window7-224-jvadlamudi2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-jvadlamudi2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4467 - Accuracy: 0.8304 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 7 \| 0.4580 \| 0.8214 \| \| 0.4488 \| 2.0 \| 14 \| 0.4733 \| 0.8304 \| \| 0.4924 \| 3.0 \| 21 \| 0.4467 \| 0.8304 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3	[ "0", "1" ]
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2053 \| 1.0 \| 199 \| 0.1227 \| 0.9496 \| \| 0.1302 \| 2.0 \| 398 \| 0.0665 \| 0.9736 \| \| 0.0784 \| 3.0 \| 597 \| 0.0600 \| 0.9778 \| \| 0.1181 \| 4.0 \| 796 \| 0.0449 \| 0.9849 \| \| 0.208 \| 5.0 \| 995 \| 0.0393 \| 0.9887 \| \| 0.0057 \| 6.0 \| 1194 \| 0.0229 \| 0.9943 \| \| 0.0017 \| 7.0 \| 1393 \| 0.0263 \| 0.9939 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "benign", "malignant" ]
dyvapandhu/vit-base-molecul-v2-5-epoch	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-molecul-v2-5-epoch This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5290 - Accuracy: 0.77 - F1: 0.7698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.11.0	[ "a", "c" ]
margosabry/food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # margosabry/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3853 - Validation Loss: 0.3150 - Train Accuracy: 0.928 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.8055 \| 1.6705 \| 0.808 \| 0 \| \| 1.2418 \| 0.8233 \| 0.883 \| 1 \| \| 0.7004 \| 0.5248 \| 0.912 \| 2 \| \| 0.5037 \| 0.3802 \| 0.926 \| 3 \| \| 0.3853 \| 0.3150 \| 0.928 \| 4 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
rshrott/vit-base-beans-demo-v5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.8460 - Accuracy: 0.6695 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0616 \| 0.17 \| 100 \| 1.0267 \| 0.5818 \| \| 0.9594 \| 0.34 \| 200 \| 0.9468 \| 0.6073 \| \| 1.1785 \| 0.51 \| 300 \| 0.9976 \| 0.5869 \| \| 0.865 \| 0.68 \| 400 \| 0.9288 \| 0.6388 \| \| 0.8494 \| 0.85 \| 500 \| 0.8573 \| 0.6516 \| \| 0.8151 \| 1.02 \| 600 \| 0.8729 \| 0.6397 \| \| 0.5787 \| 1.19 \| 700 \| 0.9067 \| 0.6448 \| \| 0.7768 \| 1.36 \| 800 \| 0.8996 \| 0.6533 \| \| 0.6098 \| 1.53 \| 900 \| 0.8460 \| 0.6695 \| \| 0.6251 \| 1.7 \| 1000 \| 0.8610 \| 0.6704 \| \| 0.7863 \| 1.87 \| 1100 \| 0.8668 \| 0.6431 \| \| 0.2595 \| 2.04 \| 1200 \| 0.8725 \| 0.6840 \| \| 0.2735 \| 2.21 \| 1300 \| 0.9307 \| 0.6746 \| \| 0.2429 \| 2.39 \| 1400 \| 1.0958 \| 0.6354 \| \| 0.3224 \| 2.56 \| 1500 \| 1.0305 \| 0.6687 \| \| 0.1602 \| 2.73 \| 1600 \| 1.0072 \| 0.6746 \| \| 0.2042 \| 2.9 \| 1700 \| 1.0971 \| 0.6789 \| \| 0.0604 \| 3.07 \| 1800 \| 1.0817 \| 0.6917 \| \| 0.0716 \| 3.24 \| 1900 \| 1.1307 \| 0.6925 \| \| 0.0822 \| 3.41 \| 2000 \| 1.1827 \| 0.6925 \| \| 0.0889 \| 3.58 \| 2100 \| 1.2424 \| 0.6934 \| \| 0.0855 \| 3.75 \| 2200 \| 1.2667 \| 0.6899 \| \| 0.0682 \| 3.92 \| 2300 \| 1.2470 \| 0.6951 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "not applicable", "very poor", "poor", "fair", "good", "great", "excellent" ]
jthetzel/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2326 \| 1.0 \| 190 \| 0.1175 \| 0.9604 \| \| 0.1789 \| 2.0 \| 380 \| 0.0765 \| 0.9763 \| \| 0.1414 \| 3.0 \| 570 \| 0.0604 \| 0.9822 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
hyeongjin99/vit-base-aihub_model-v2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-aihub_model-v2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3076 - Accuracy: 0.9639 - Precision: 0.9610 - Recall: 0.9614 - F1: 0.9604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 3 \| 1.2753 \| 0.8373 \| 0.8563 \| 0.7993 \| 0.8022 \| \| No log \| 2.0 \| 6 \| 1.1252 \| 0.8675 \| 0.8895 \| 0.8300 \| 0.8333 \| \| No log \| 3.0 \| 9 \| 0.9427 \| 0.8976 \| 0.9185 \| 0.8696 \| 0.8760 \| \| 1.1721 \| 4.0 \| 12 \| 0.7995 \| 0.9398 \| 0.9474 \| 0.9195 \| 0.9246 \| \| 1.1721 \| 5.0 \| 15 \| 0.6820 \| 0.9699 \| 0.9704 \| 0.9613 \| 0.9642 \| \| 1.1721 \| 6.0 \| 18 \| 0.5927 \| 0.9639 \| 0.9603 \| 0.9583 \| 0.9587 \| \| 0.7084 \| 7.0 \| 21 \| 0.5239 \| 0.9759 \| 0.9725 \| 0.9729 \| 0.9725 \| \| 0.7084 \| 8.0 \| 24 \| 0.4743 \| 0.9699 \| 0.9665 \| 0.9671 \| 0.9665 \| \| 0.7084 \| 9.0 \| 27 \| 0.4436 \| 0.9578 \| 0.9558 \| 0.9556 \| 0.9544 \| \| 0.4668 \| 10.0 \| 30 \| 0.4070 \| 0.9639 \| 0.9610 \| 0.9614 \| 0.9604 \| \| 0.4668 \| 11.0 \| 33 \| 0.3817 \| 0.9699 \| 0.9665 \| 0.9671 \| 0.9665 \| \| 0.4668 \| 12.0 \| 36 \| 0.3625 \| 0.9699 \| 0.9665 \| 0.9671 \| 0.9665 \| \| 0.4668 \| 13.0 \| 39 \| 0.3536 \| 0.9578 \| 0.9558 \| 0.9556 \| 0.9544 \| \| 0.3611 \| 14.0 \| 42 \| 0.3384 \| 0.9578 \| 0.9558 \| 0.9556 \| 0.9544 \| \| 0.3611 \| 15.0 \| 45 \| 0.3249 \| 0.9699 \| 0.9665 \| 0.9671 \| 0.9665 \| \| 0.3611 \| 16.0 \| 48 \| 0.3164 \| 0.9699 \| 0.9665 \| 0.9671 \| 0.9665 \| \| 0.3063 \| 17.0 \| 51 \| 0.3142 \| 0.9639 \| 0.9610 \| 0.9614 \| 0.9604 \| \| 0.3063 \| 18.0 \| 54 \| 0.3122 \| 0.9639 \| 0.9610 \| 0.9614 \| 0.9604 \| \| 0.3063 \| 19.0 \| 57 \| 0.3093 \| 0.9639 \| 0.9610 \| 0.9614 \| 0.9604 \| \| 0.294 \| 20.0 \| 60 \| 0.3076 \| 0.9639 \| 0.9610 \| 0.9614 \| 0.9604 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3	[ "cloudy", "rainy", "sandy", "shine", "snowy", "sunrise" ]
sezer12138/results	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5248 - Accuracy: 0.6505 ## Model description On ADE20K Dataset, do image classification. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8495 \| 1.0 \| 158 \| 2.3313 \| 0.5525 \| \| 1.8825 \| 2.0 \| 316 \| 1.6815 \| 0.623 \| \| 1.4956 \| 3.0 \| 474 \| 1.5248 \| 0.6505 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	[ "abbey", "access_road", "airport_terminal", "bathroom", "village", "vinery", "vineyard", "volcano", "volleyball_court_indoor", "volleyball_court_outdoor", "voting_booth", "waiting_room", "walk_in_freezer", "walkway", "batters_box", "war_room", "warehouse_indoor", "warehouse_outdoor", "washhouse_indoor", "washhouse_outdoor", "washroom", "watchtower", "water", "water_fountain", "water_gate", "batting_cage_indoor", "water_mill", "water_park", "water_tower", "water_treatment_plant_indoor", "water_treatment_plant_outdoor", "watering_hole", "waterscape", "waterway", "wave", "weighbridge", "batting_cage_outdoor", "western", "wet_bar", "wetland", "wharf", "wheat_field", "whispering_gallery", "widows_walk_indoor", "widows_walk_interior", "wild", "wind_farm", "battlefield", "windmill", "window_seat", "windstorm", "winery", "witness_stand", "woodland", "workroom", "workshop", "wrestling_ring_indoor", "wrestling_ring_outdoor", "battlement", "yard", "youth_hostel", "zen_garden", "ziggurat", "zoo", "bay", "bayou", "bazaar_indoor", "bazaar_outdoor", "airport_ticket_counter", "beach", "beach_house", "beauty_salon", "bedchamber", "bedroom", "beer_garden", "beer_hall", "belfry", "bell_foundry", "berth", "alcove", "berth_deck", "betting_shop", "bicycle_racks", "bindery", "biology_laboratory", "bistro_indoor", "bistro_outdoor", "bleachers_indoor", "bleachers_outdoor", "block", "alley", "boardwalk", "boat", "boat_deck", "boathouse", "bog", "bomb_shelter_indoor", "bookbindery", "bookshelf", "bookstore", "booth", "amphitheater", "booth_indoor", "booth_outdoor", "botanical_garden", "bottle_storage", "bottomland", "bow_window_indoor", "bow_window_outdoor", "bowling_alley", "box_seat", "boxing_ring", "amphitheater_indoor", "breakfast_table", "breakroom", "brewery_indoor", "brewery_outdoor", "bric-a-brac", "brickyard_indoor", "brickyard_outdoor", "bridge", "bridle_path", "broadleaf", "amusement_arcade", "brooklet", "bubble_chamber", "buffet", "building_complex", "building_facade", "bulkhead", "bullpen", "bullring", "bunk_bed", "burial_chamber", "amusement_park", "bus_depot_indoor", "bus_depot_outdoor", "bus_interior", "bus_shelter", "bus_station_indoor", "bus_station_outdoor", "butchers_shop", "butte", "bypass", "byroad", "anechoic_chamber", "cabana", "cabin_cruiser", "cabin_indoor", "cabin_outdoor", "cafeteria", "call_center", "campsite", "campus", "candy_store", "canteen", "apartment_building_outdoor", "canyon", "car_dealership", "caravansary", "cardroom", "cargo_container_interior", "cargo_deck", "cargo_helicopter", "carport_indoor", "carport_outdoor", "carrousel", "acropolis", "apse_indoor", "cascade", "casino_indoor", "casino_outdoor", "castle", "catacomb", "cataract", "cathedral_indoor", "cathedral_outdoor", "catwalk", "cavern_indoor", "apse_outdoor", "cavern_outdoor", "cellar", "cemetery", "chair_lift", "chalet", "chaparral", "chapel", "checkout_counter", "cheese_factory", "chemical_plant", "aquarium", "chemistry_lab", "chicken_coop_indoor", "chicken_coop_outdoor", "chicken_farm_indoor", "chicken_farm_outdoor", "childs_room", "choir_loft_interior", "chuck_wagon", "church_indoor", "church_outdoor", "aquatic_theater", "circus_tent_indoor", "circus_tent_outdoor", "city", "classroom", "clean_room", "cliff", "clock_tower_indoor", "cloister_indoor", "cloister_outdoor", "closet", "aqueduct", "clothing_store", "coast", "coast_road", "cockpit", "cocktail_lounge", "coffee_shop", "computer_room", "conference_center", "conference_hall", "conference_room", "arbor", "confessional", "construction_site", "control_room", "control_tower_indoor", "control_tower_outdoor", "convenience_store_indoor", "convenience_store_outdoor", "coral_reef", "corn_field", "corner", "arcade", "corral", "corridor", "cottage", "cottage_garden", "country_house", "country_road", "courthouse", "courtroom", "courtyard", "covered_bridge_interior", "arch", "crawl_space", "creek", "crevasse", "crosswalk", "cultivated", "customhouse", "cybercafe", "dacha", "dairy_indoor", "dairy_outdoor", "archaelogical_excavation", "dam", "dance_floor", "dance_school", "darkroom", "day_care_center", "deck-house_boat_deck_house", "deck-house_deck_house", "delicatessen", "dentists_office", "department_store", "archipelago", "departure_lounge", "desert_road", "diner_indoor", "diner_outdoor", "dinette_home", "dining_area", "dining_car", "dining_hall", "dining_room", "dirt_track", "air_base", "archive", "discotheque", "distillery", "ditch", "diving_board", "dock", "dolmen", "donjon", "door", "doorway_indoor", "doorway_outdoor", "armory", "dorm_room", "downtown", "drainage_ditch", "dress_shop", "dressing_room", "drill_rig", "driveway", "driving_range_indoor", "driving_range_outdoor", "drugstore", "army_base", "dry", "dry_dock", "dugout", "earth_fissure", "east_asia", "editing_room", "electrical_substation", "elevated_catwalk", "elevator_interior", "elevator_lobby", "arrival_gate_indoor", "elevator_shaft", "embankment", "embassy", "embrasure", "engine_room", "entrance", "entrance_hall", "entranceway_indoor", "entranceway_outdoor", "entryway_outdoor", "arrival_gate_outdoor", "escalator_indoor", "escalator_outdoor", "escarpment", "establishment", "estaminet", "estuary", "excavation", "exhibition_hall", "exterior", "fabric_store", "art_gallery", "factory_indoor", "factory_outdoor", "fairway", "fan", "farm", "farm_building", "farmhouse", "fastfood_restaurant", "feed_bunk", "fence", "art_school", "ferryboat_indoor", "field_house", "field_road", "field_tent_indoor", "field_tent_outdoor", "fire_escape", "fire_station", "fire_trench", "fireplace", "firing_range_indoor", "art_studio", "firing_range_outdoor", "fish_farm", "fishmarket", "fishpond", "fitting_room_interior", "fjord", "flashflood", "flatlet", "flea_market_indoor", "flea_market_outdoor", "artificial", "floating_dock", "floating_dry_dock", "flood", "flood_plain", "florist_shop_indoor", "florist_shop_outdoor", "flowerbed", "flume_indoor", "fly_bridge", "flying_buttress", "artists_loft", "food_court", "football", "football_field", "foothill", "forecourt", "foreshore", "forest_fire", "forest_path", "forest_road", "forklift", "aircraft_carrier_object", "assembly_hall", "formal_garden", "fort", "fortress", "foundry_indoor", "foundry_outdoor", "fountain", "freestanding", "freeway", "freight_elevator", "front_porch", "assembly_line", "frontseat", "funeral_chapel", "funeral_home", "furnace_room", "galley", "game_room", "gangplank", "garage_indoor", "garage_outdoor", "garbage_dump", "assembly_plant", "garden", "gas_station", "gas_well", "gasworks", "gate", "gatehouse", "gazebo_interior", "general_store_indoor", "general_store_outdoor", "geodesic_dome_indoor", "athletic_field_indoor", "geodesic_dome_outdoor", "ghost_town", "gift_shop", "glacier", "glade", "glen", "golf_course", "gorge", "granary", "grape_arbor", "athletic_field_outdoor", "great_hall", "greengrocery", "greenhouse_indoor", "greenhouse_outdoor", "grotto", "grove", "guardhouse", "guardroom", "guesthouse", "gulch", "atrium_home", "gun_deck_indoor", "gun_deck_outdoor", "gun_store", "gymnasium_indoor", "gymnasium_outdoor", "hacienda", "hallway", "handball_court", "hangar_indoor", "hangar_outdoor", "atrium_public", "harbor", "hardware_store", "hat_shop", "hatchery", "hayfield", "hayloft", "head_shop", "hearth", "heath", "hedge_maze", "attic", "hedgerow", "heliport", "hen_yard", "herb_garden", "highway", "hill", "hillock", "hockey", "hollow", "home_office", "auditorium", "home_theater", "hoodoo", "hospital", "hospital_room", "hot_spring", "hot_tub_indoor", "hot_tub_outdoor", "hotel_breakfast_area", "hotel_outdoor", "hotel_room", "auto_factory", "house", "housing_estate", "housing_project", "howdah", "hunting_lodge_indoor", "hunting_lodge_outdoor", "hut", "hutment", "ice_cream_parlor", "ice_floe", "airfield", "auto_mechanics_indoor", "ice_shelf", "ice_skating_rink_indoor", "ice_skating_rink_outdoor", "iceberg", "igloo", "imaret", "incinerator_indoor", "incinerator_outdoor", "indoor_procenium", "indoor_round", "auto_mechanics_outdoor", "indoor_seats", "industrial_area", "industrial_park", "inlet", "inn_indoor", "inn_outdoor", "insane_asylum", "irrigation_ditch", "islet", "jacuzzi_indoor", "auto_racing_paddock", "jacuzzi_outdoor", "jail_cell", "jail_indoor", "jail_outdoor", "japanese_garden", "jetty", "jewelry_shop", "joss_house", "juke_joint", "jungle", "auto_showroom", "junk_pile", "junkyard", "jury_box", "kasbah", "kennel_indoor", "kennel_outdoor", "kindergarden_classroom", "kiosk_indoor", "kiosk_outdoor", "kitchen", "awning_deck", "kitchenette", "kraal", "lab_classroom", "laboratorywet", "labyrinth_indoor", "labyrinth_outdoor", "lagoon", "landfill", "landing", "landing_deck", "back_porch", "landing_strip", "laundromat", "lava_flow", "lavatory", "lawn", "layby", "lean-to", "lean-to_tent", "lecture_room", "legislative_chamber", "backdrop", "levee", "library", "library_indoor", "library_outdoor", "lido_deck_indoor", "lido_deck_outdoor", "lift_bridge", "lighthouse", "limousine_interior", "liquor_store_indoor", "backroom", "liquor_store_outdoor", "living_room", "loading_dock", "lobby", "lock_chamber", "locker_room", "loft", "loge", "loggia_outdoor", "lookout_station_indoor", "backseat", "lookout_station_outdoor", "lower_deck", "luggage_van", "lumberyard_indoor", "lumberyard_outdoor", "lyceum", "machine_shop", "manhole", "mansard", "mansion", "backstage", "manufactured_home", "market_indoor", "market_outdoor", "marsh", "martial_arts_gym", "massage_room", "mastaba", "maternity_ward", "mausoleum", "meadow", "airlock", "backstage_outdoor", "meat_house", "medina", "megalith", "menhir", "mens_store_outdoor", "mental_institution_indoor", "mental_institution_outdoor", "mesa", "mesoamerican", "mess_hall", "backstairs", "mews", "mezzanine", "military_headquarters", "military_hospital", "military_hut", "military_tent", "millpond", "millrace", "mine", "mineral_bath", "backstairs_indoor", "mineshaft", "mini_golf_course_indoor", "mini_golf_course_outdoor", "misc", "mission", "mobile_home", "monastery_indoor", "monastery_outdoor", "moon_bounce", "moor", "backwoods", "morgue", "mosque_indoor", "mosque_outdoor", "motel", "mountain", "mountain_path", "mountain_road", "mountain_snowy", "movie_theater_indoor", "movie_theater_outdoor", "badlands", "mudflat", "museum_indoor", "museum_outdoor", "music_store", "music_studio", "natural", "natural_history_museum", "natural_spring", "naval_base", "needleleaf", "badminton_court_indoor", "newsroom", "newsstand_indoor", "newsstand_outdoor", "nightclub", "nook", "nuclear_power_plant_indoor", "nuclear_power_plant_outdoor", "nunnery", "nursery", "nursing_home", "badminton_court_outdoor", "nursing_home_outdoor", "oasis", "oast_house", "observation_station", "observatory_indoor", "observatory_outdoor", "observatory_post", "ocean", "ocean_deep", "ocean_shallow", "baggage_claim", "office", "office_building", "office_cubicles", "oil_refinery_indoor", "oil_refinery_outdoor", "oilrig", "one-way_street", "open-hearth_furnace", "operating_room", "operating_table", "balcony_interior", "optician", "orchard", "orchestra_pit", "organ_loft_interior", "orlop_deck", "ossuary", "outbuilding", "outcropping", "outhouse_indoor", "outhouse_outdoor", "ball_pit", "outside", "overpass", "oyster_bar", "oyster_farm", "packaging_plant", "pagoda", "palace", "palace_hall", "palestra", "pantry", "airplane", "ballet", "paper_mill", "parade_ground", "park", "parking_garage_indoor", "parking_garage_outdoor", "parking_lot", "parkway", "parlor", "particle_accelerator", "party_tent_indoor", "ballroom", "party_tent_outdoor", "passenger_deck", "pasture", "patio", "patio_indoor", "pavement", "pavilion", "pawnshop", "pawnshop_outdoor", "pedestrian_overpass_indoor", "balustrade", "penalty_box", "performance", "perfume_shop", "pet_shop", "pharmacy", "phone_booth", "physics_laboratory", "piano_store", "picnic_area", "pier", "bamboo_forest", "pig_farm", "pilothouse_indoor", "pilothouse_outdoor", "pinetum", "piste_road", "pitchers_mound", "pizzeria", "pizzeria_outdoor", "planetarium_indoor", "planetarium_outdoor", "bank_indoor", "plantation_house", "platform", "playground", "playroom", "plaza", "plunge", "podium_indoor", "podium_outdoor", "police_station", "pond", "bank_outdoor", "pontoon_bridge", "poolroom_home", "poop_deck", "porch", "portico", "portrait_studio", "postern", "powder_room", "power_plant_outdoor", "preserve", "bank_vault", "print_shop", "priory", "promenade", "promenade_deck", "pub_indoor", "pub_outdoor", "pueblo", "pulpit", "pump_room", "pumping_station", "banquet_hall", "putting_green", "quadrangle", "questionable", "quicksand", "quonset_hut_indoor", "quonset_hut_outdoor", "racecourse", "raceway", "raft", "rail_indoor", "baptistry_indoor", "rail_outdoor", "railroad_track", "railway_yard", "rainforest", "ramp", "ranch", "ranch_house", "reading_room", "reception", "reception_room", "baptistry_outdoor", "recreation_room", "rectory", "recycling_plant_indoor", "recycling_plant_outdoor", "refectory", "repair_shop", "residential_neighborhood", "resort", "rest_area", "rest_stop", "airplane_cabin", "bar", "restaurant", "restaurant_kitchen", "restaurant_patio", "restroom_indoor", "restroom_outdoor", "retaining_wall", "revolving_door", "rice_paddy", "riding_arena", "rift_valley", "barbeque", "river", "road", "road_cut", "road_indoor", "road_outdoor", "rock_arch", "rock_garden", "rodeo", "roller_skating_rink_indoor", "roller_skating_rink_outdoor", "barbershop", "rolling_mill", "roof", "roof_garden", "room", "root_cellar", "rope_bridge", "rotisserie", "roundabout", "roundhouse", "rubble", "barn", "ruin", "runway", "sacristy", "safari_park", "salon", "saloon", "salt_plain", "sanatorium", "sand", "sand_trap", "barndoor", "sandbar", "sandbox", "sauna", "savanna", "sawmill", "schoolhouse", "schoolyard", "science_laboratory", "science_museum", "scriptorium", "barnyard", "scrubland", "scullery", "sea_cliff", "seaside", "seawall", "security_check_point", "semidesert", "server_room", "sewer", "sewing_room", "barrack", "shed", "shelter", "shelter_deck", "shelter_tent", "shipping_room", "shipyard_outdoor", "shoe_shop", "shop", "shopfront", "shopping_mall_indoor", "barrel_storage", "shopping_mall_outdoor", "shore", "shower", "shower_room", "shrine", "shrubbery", "sidewalk", "signal_box", "sinkhole", "ski_jump", "baseball", "ski_lodge", "ski_resort", "ski_slope", "sky", "skyscraper", "skywalk_indoor", "skywalk_outdoor", "slum", "snack_bar", "snowbank", "baseball_field", "snowfield", "soccer", "south_asia", "spillway", "sporting_goods_store", "squash_court", "stable", "stadium_outdoor", "stage_indoor", "stage_outdoor", "airport", "basement", "stage_set", "staircase", "stall", "starting_gate", "stateroom", "station", "steam_plant_outdoor", "steel_mill_indoor", "steel_mill_outdoor", "stone_circle", "basilica", "storage_room", "store", "storm_cellar", "street", "streetcar_track", "strip_mall", "strip_mine", "student_center", "student_residence", "study_hall", "basin_outdoor", "submarine_interior", "subway_interior", "sugar_refinery", "sun_deck", "sunroom", "supermarket", "supply_chamber", "sushi_bar", "swamp", "swimming_hole", "basketball", "swimming_pool_indoor", "swimming_pool_outdoor", "synagogue_indoor", "synagogue_outdoor", "t-bar_lift", "tannery", "taxistand", "taxiway", "tea_garden", "teahouse", "basketball_court_indoor", "tearoom", "teashop", "television_room", "television_studio", "tennis_court_indoor", "tennis_court_outdoor", "tent_outdoor", "terrace_farm", "theater_outdoor", "threshing_floor", "basketball_court_outdoor", "thriftshop", "throne_room", "ticket_booth", "ticket_window_indoor", "tidal_basin", "tidal_river", "tiltyard", "tobacco_shop_indoor", "toll_plaza", "tollbooth", "bath_indoor", "tollgate", "tomb", "topiary_garden", "tower", "town_house", "toyshop", "track_outdoor", "tract_housing", "trading_floor", "traffic_island", "bath_outdoor", "trailer_park", "train_interior", "train_railway", "train_station_outdoor", "tree_farm", "tree_house", "trellis", "trench", "trestle_bridge", "truck_stop", "bathhouse", "tundra", "turkish_bath", "upper_balcony", "urban", "utility_room", "valley", "van_interior", "vat", "vegetable_garden", "vegetation", "bathhouse_outdoor", "vehicle", "velodrome_indoor", "velodrome_outdoor", "ventilation_shaft", "veranda", "vestibule", "vestry", "veterinarians_office", "viaduct", "videostore" ]
adam-bourne/food-classifier	# food-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2136 - Validation Loss: 0.2284 - Train Accuracy: 0.94 - Epoch: 4 ## Model description This is an image classification model fine tuned from the Google Vision Transformer (ViT) to classify images of food. ## Intended uses & limitations For messing around! ## Training and evaluation data The training set contained 101 food classes, over a dataset of 101,000 images. The train/eval split was 80/20 ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.3409 \| 0.2903 \| 0.932 \| 0 \| \| 0.2838 \| 0.2897 \| 0.917 \| 1 \| \| 0.2415 \| 0.2869 \| 0.914 \| 2 \| \| 0.2143 \| 0.2630 \| 0.924 \| 3 \| \| 0.2136 \| 0.2284 \| 0.94 \| 4 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
bgirardot/my_awesome_food_model_v2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model_v2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.9051 - Accuracy: 0.965 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.6221 \| 1.0 \| 25 \| 2.8425 \| 0.875 \| \| 2.195 \| 2.0 \| 50 \| 2.0761 \| 0.955 \| \| 1.9134 \| 3.0 \| 75 \| 1.9051 \| 0.965 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
rshrott/vit-base-renovation	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-renovation This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the renovations dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.382 \| 0.2 \| 25 \| 1.1103 \| 0.6073 \| \| 0.5741 \| 0.4 \| 50 \| 1.0628 \| 0.6210 \| \| 0.5589 \| 0.6 \| 75 \| 1.0025 \| 0.6667 \| \| 0.4074 \| 0.81 \| 100 \| 1.1324 \| 0.6073 \| \| 0.3581 \| 1.01 \| 125 \| 1.1935 \| 0.6438 \| \| 0.2618 \| 1.21 \| 150 \| 1.8300 \| 0.5023 \| \| 0.1299 \| 1.41 \| 175 \| 1.2577 \| 0.6301 \| \| 0.2562 \| 1.61 \| 200 \| 1.0924 \| 0.6895 \| \| 0.2573 \| 1.81 \| 225 \| 1.1285 \| 0.6849 \| \| 0.2471 \| 2.02 \| 250 \| 1.3387 \| 0.6256 \| \| 0.0618 \| 2.22 \| 275 \| 1.2246 \| 0.6667 \| \| 0.0658 \| 2.42 \| 300 \| 1.4132 \| 0.6347 \| \| 0.0592 \| 2.62 \| 325 \| 1.4326 \| 0.6530 \| \| 0.0464 \| 2.82 \| 350 \| 1.2484 \| 0.6849 \| \| 0.0567 \| 3.02 \| 375 \| 1.5350 \| 0.6347 \| \| 0.0269 \| 3.23 \| 400 \| 1.4797 \| 0.6667 \| \| 0.0239 \| 3.43 \| 425 \| 1.4444 \| 0.6530 \| \| 0.0184 \| 3.63 \| 450 \| 1.4474 \| 0.6575 \| \| 0.0286 \| 3.83 \| 475 \| 1.4621 \| 0.6667 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "not applicable", "very poor", "poor", "fair", "good", "excellent", "exceptional" ]
jordyvl/vit-small_tobacco3482_og_simkd_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-small_tobacco3482_og_simkd_ This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 212.9178 - Accuracy: 0.855 - Brier Loss: 0.2563 - Nll: 1.4722 - F1 Micro: 0.855 - F1 Macro: 0.8333 - Ece: 0.1253 - Aurc: 0.0422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 219.2297 \| 0.145 \| 0.8895 \| 6.5562 \| 0.145 \| 0.0567 \| 0.2109 \| 0.7661 \| \| No log \| 2.0 \| 50 \| 217.9786 \| 0.49 \| 0.6839 \| 2.2035 \| 0.49 \| 0.4113 \| 0.3237 \| 0.3097 \| \| No log \| 3.0 \| 75 \| 216.6085 \| 0.565 \| 0.5671 \| 1.7658 \| 0.565 \| 0.4471 \| 0.2471 \| 0.2239 \| \| No log \| 4.0 \| 100 \| 216.0210 \| 0.68 \| 0.4722 \| 1.8682 \| 0.68 \| 0.5586 \| 0.2322 \| 0.1557 \| \| No log \| 5.0 \| 125 \| 215.5695 \| 0.68 \| 0.4668 \| 1.9385 \| 0.68 \| 0.5570 \| 0.2289 \| 0.1440 \| \| No log \| 6.0 \| 150 \| 215.3762 \| 0.745 \| 0.3963 \| 2.1043 \| 0.745 \| 0.6608 \| 0.1949 \| 0.0976 \| \| No log \| 7.0 \| 175 \| 214.8964 \| 0.745 \| 0.3675 \| 1.7226 \| 0.745 \| 0.6693 \| 0.1765 \| 0.0949 \| \| No log \| 8.0 \| 200 \| 215.0440 \| 0.735 \| 0.3838 \| 1.9180 \| 0.735 \| 0.6935 \| 0.2056 \| 0.0958 \| \| No log \| 9.0 \| 225 \| 214.7017 \| 0.775 \| 0.3466 \| 1.5816 \| 0.775 \| 0.6897 \| 0.1756 \| 0.0661 \| \| No log \| 10.0 \| 250 \| 214.6309 \| 0.775 \| 0.3505 \| 1.6245 \| 0.775 \| 0.7604 \| 0.1828 \| 0.0763 \| \| No log \| 11.0 \| 275 \| 214.6275 \| 0.735 \| 0.4314 \| 2.3367 \| 0.735 \| 0.7342 \| 0.2234 \| 0.1203 \| \| No log \| 12.0 \| 300 \| 214.5664 \| 0.75 \| 0.3769 \| 1.7889 \| 0.75 \| 0.7420 \| 0.1873 \| 0.1070 \| \| No log \| 13.0 \| 325 \| 214.6764 \| 0.735 \| 0.4425 \| 2.3533 \| 0.735 \| 0.7404 \| 0.2267 \| 0.1508 \| \| No log \| 14.0 \| 350 \| 214.5261 \| 0.805 \| 0.3093 \| 1.8504 \| 0.805 \| 0.7870 \| 0.1732 \| 0.0580 \| \| No log \| 15.0 \| 375 \| 214.4932 \| 0.79 \| 0.3255 \| 1.4649 \| 0.79 \| 0.7575 \| 0.1796 \| 0.0543 \| \| No log \| 16.0 \| 400 \| 214.3134 \| 0.85 \| 0.2467 \| 1.4769 \| 0.85 \| 0.8388 \| 0.1149 \| 0.0513 \| \| No log \| 17.0 \| 425 \| 214.3825 \| 0.82 \| 0.2845 \| 1.4858 \| 0.82 \| 0.8014 \| 0.1445 \| 0.0540 \| \| No log \| 18.0 \| 450 \| 214.2077 \| 0.85 \| 0.2681 \| 1.4891 \| 0.85 \| 0.8406 \| 0.1462 \| 0.0684 \| \| No log \| 19.0 \| 475 \| 214.1675 \| 0.845 \| 0.2623 \| 1.5311 \| 0.845 \| 0.8329 \| 0.1414 \| 0.0485 \| \| 220.0633 \| 20.0 \| 500 \| 214.1433 \| 0.84 \| 0.2663 \| 1.5269 \| 0.8400 \| 0.8182 \| 0.1302 \| 0.0562 \| \| 220.0633 \| 21.0 \| 525 \| 214.0829 \| 0.805 \| 0.3293 \| 2.0021 \| 0.805 \| 0.8019 \| 0.1710 \| 0.0833 \| \| 220.0633 \| 22.0 \| 550 \| 213.9282 \| 0.84 \| 0.2586 \| 1.5127 \| 0.8400 \| 0.8205 \| 0.1397 \| 0.0453 \| \| 220.0633 \| 23.0 \| 575 \| 213.9303 \| 0.87 \| 0.2260 \| 1.4450 \| 0.87 \| 0.8552 \| 0.1205 \| 0.0365 \| \| 220.0633 \| 24.0 \| 600 \| 213.9140 \| 0.84 \| 0.2620 \| 1.5244 \| 0.8400 \| 0.8161 \| 0.1462 \| 0.0490 \| \| 220.0633 \| 25.0 \| 625 \| 213.7616 \| 0.86 \| 0.2306 \| 1.5288 \| 0.8600 \| 0.8409 \| 0.1215 \| 0.0361 \| \| 220.0633 \| 26.0 \| 650 \| 213.7738 \| 0.845 \| 0.2431 \| 1.5303 \| 0.845 \| 0.8271 \| 0.1335 \| 0.0443 \| \| 220.0633 \| 27.0 \| 675 \| 213.8470 \| 0.85 \| 0.2427 \| 1.3459 \| 0.85 \| 0.8275 \| 0.1296 \| 0.0445 \| \| 220.0633 \| 28.0 \| 700 \| 213.7198 \| 0.85 \| 0.2381 \| 1.3868 \| 0.85 \| 0.8328 \| 0.1267 \| 0.0424 \| \| 220.0633 \| 29.0 \| 725 \| 213.6302 \| 0.855 \| 0.2293 \| 1.4191 \| 0.855 \| 0.8361 \| 0.1157 \| 0.0394 \| \| 220.0633 \| 30.0 \| 750 \| 213.6385 \| 0.85 \| 0.2424 \| 1.5410 \| 0.85 \| 0.8334 \| 0.1339 \| 0.0464 \| \| 220.0633 \| 31.0 \| 775 \| 213.6397 \| 0.865 \| 0.2234 \| 1.4012 \| 0.865 \| 0.8464 \| 0.1226 \| 0.0402 \| \| 220.0633 \| 32.0 \| 800 \| 213.6658 \| 0.86 \| 0.2271 \| 1.3863 \| 0.8600 \| 0.8470 \| 0.1164 \| 0.0354 \| \| 220.0633 \| 33.0 \| 825 \| 213.6526 \| 0.85 \| 0.2448 \| 1.5357 \| 0.85 \| 0.8292 \| 0.1214 \| 0.0397 \| \| 220.0633 \| 34.0 \| 850 \| 213.5407 \| 0.855 \| 0.2282 \| 1.3470 \| 0.855 \| 0.8405 \| 0.1245 \| 0.0393 \| \| 220.0633 \| 35.0 \| 875 \| 213.6166 \| 0.83 \| 0.2624 \| 1.4288 \| 0.83 \| 0.8102 \| 0.1415 \| 0.0458 \| \| 220.0633 \| 36.0 \| 900 \| 213.5887 \| 0.84 \| 0.2613 \| 1.3928 \| 0.8400 \| 0.8135 \| 0.1298 \| 0.0442 \| \| 220.0633 \| 37.0 \| 925 \| 213.4976 \| 0.845 \| 0.2338 \| 1.3784 \| 0.845 \| 0.8244 \| 0.1319 \| 0.0355 \| \| 220.0633 \| 38.0 \| 950 \| 213.4554 \| 0.85 \| 0.2374 \| 1.3680 \| 0.85 \| 0.8323 \| 0.1192 \| 0.0385 \| \| 220.0633 \| 39.0 \| 975 \| 213.4758 \| 0.845 \| 0.2319 \| 1.4895 \| 0.845 \| 0.8274 \| 0.1185 \| 0.0385 \| \| 217.7609 \| 40.0 \| 1000 \| 213.4440 \| 0.845 \| 0.2432 \| 1.3737 \| 0.845 \| 0.8265 \| 0.1310 \| 0.0415 \| \| 217.7609 \| 41.0 \| 1025 \| 213.4492 \| 0.845 \| 0.2385 \| 1.4970 \| 0.845 \| 0.8297 \| 0.1207 \| 0.0373 \| \| 217.7609 \| 42.0 \| 1050 \| 213.4319 \| 0.85 \| 0.2384 \| 1.3580 \| 0.85 \| 0.8276 \| 0.1250 \| 0.0383 \| \| 217.7609 \| 43.0 \| 1075 \| 213.3094 \| 0.855 \| 0.2287 \| 1.4375 \| 0.855 \| 0.8334 \| 0.1188 \| 0.0353 \| \| 217.7609 \| 44.0 \| 1100 \| 213.3809 \| 0.845 \| 0.2514 \| 1.4969 \| 0.845 \| 0.8250 \| 0.1318 \| 0.0435 \| \| 217.7609 \| 45.0 \| 1125 \| 213.3981 \| 0.85 \| 0.2478 \| 1.6052 \| 0.85 \| 0.8287 \| 0.1268 \| 0.0408 \| \| 217.7609 \| 46.0 \| 1150 \| 213.3004 \| 0.86 \| 0.2292 \| 1.3632 \| 0.8600 \| 0.8430 \| 0.1180 \| 0.0355 \| \| 217.7609 \| 47.0 \| 1175 \| 213.3041 \| 0.86 \| 0.2363 \| 1.3407 \| 0.8600 \| 0.8444 \| 0.1235 \| 0.0359 \| \| 217.7609 \| 48.0 \| 1200 \| 213.2955 \| 0.845 \| 0.2462 \| 1.5071 \| 0.845 \| 0.8179 \| 0.1253 \| 0.0396 \| \| 217.7609 \| 49.0 \| 1225 \| 213.2531 \| 0.85 \| 0.2433 \| 1.2946 \| 0.85 \| 0.8277 \| 0.1270 \| 0.0392 \| \| 217.7609 \| 50.0 \| 1250 \| 213.2612 \| 0.845 \| 0.2378 \| 1.2852 \| 0.845 \| 0.8193 \| 0.1281 \| 0.0361 \| \| 217.7609 \| 51.0 \| 1275 \| 213.2246 \| 0.855 \| 0.2370 \| 1.5829 \| 0.855 \| 0.8393 \| 0.1234 \| 0.0357 \| \| 217.7609 \| 52.0 \| 1300 \| 213.1795 \| 0.845 \| 0.2431 \| 1.4923 \| 0.845 \| 0.8300 \| 0.1280 \| 0.0372 \| \| 217.7609 \| 53.0 \| 1325 \| 213.2721 \| 0.855 \| 0.2467 \| 1.5096 \| 0.855 \| 0.8333 \| 0.1248 \| 0.0385 \| \| 217.7609 \| 54.0 \| 1350 \| 213.1976 \| 0.85 \| 0.2453 \| 1.4167 \| 0.85 \| 0.8275 \| 0.1240 \| 0.0384 \| \| 217.7609 \| 55.0 \| 1375 \| 213.2822 \| 0.845 \| 0.2430 \| 1.4438 \| 0.845 \| 0.8193 \| 0.1283 \| 0.0396 \| \| 217.7609 \| 56.0 \| 1400 \| 213.1443 \| 0.85 \| 0.2479 \| 1.5246 \| 0.85 \| 0.8277 \| 0.1304 \| 0.0389 \| \| 217.7609 \| 57.0 \| 1425 \| 213.1679 \| 0.85 \| 0.2455 \| 1.4468 \| 0.85 \| 0.8291 \| 0.1224 \| 0.0387 \| \| 217.7609 \| 58.0 \| 1450 \| 213.1116 \| 0.85 \| 0.2467 \| 1.4372 \| 0.85 \| 0.8287 \| 0.1269 \| 0.0378 \| \| 217.7609 \| 59.0 \| 1475 \| 213.1005 \| 0.85 \| 0.2490 \| 1.4214 \| 0.85 \| 0.8271 \| 0.1316 \| 0.0392 \| \| 217.1217 \| 60.0 \| 1500 \| 213.1516 \| 0.855 \| 0.2425 \| 1.4600 \| 0.855 \| 0.8343 \| 0.1316 \| 0.0369 \| \| 217.1217 \| 61.0 \| 1525 \| 213.1205 \| 0.855 \| 0.2458 \| 1.4436 \| 0.855 \| 0.8303 \| 0.1197 \| 0.0409 \| \| 217.1217 \| 62.0 \| 1550 \| 213.1318 \| 0.85 \| 0.2488 \| 1.4405 \| 0.85 \| 0.8275 \| 0.1304 \| 0.0378 \| \| 217.1217 \| 63.0 \| 1575 \| 213.0243 \| 0.855 \| 0.2521 \| 1.5810 \| 0.855 \| 0.8328 \| 0.1341 \| 0.0447 \| \| 217.1217 \| 64.0 \| 1600 \| 213.1191 \| 0.84 \| 0.2567 \| 1.4478 \| 0.8400 \| 0.8185 \| 0.1292 \| 0.0436 \| \| 217.1217 \| 65.0 \| 1625 \| 213.0329 \| 0.855 \| 0.2528 \| 1.3910 \| 0.855 \| 0.8333 \| 0.1311 \| 0.0404 \| \| 217.1217 \| 66.0 \| 1650 \| 212.9868 \| 0.85 \| 0.2525 \| 1.4652 \| 0.85 \| 0.8275 \| 0.1226 \| 0.0408 \| \| 217.1217 \| 67.0 \| 1675 \| 213.0856 \| 0.84 \| 0.2561 \| 1.4601 \| 0.8400 \| 0.8178 \| 0.1367 \| 0.0419 \| \| 217.1217 \| 68.0 \| 1700 \| 213.0379 \| 0.845 \| 0.2544 \| 1.5222 \| 0.845 \| 0.8216 \| 0.1362 \| 0.0426 \| \| 217.1217 \| 69.0 \| 1725 \| 213.0535 \| 0.835 \| 0.2606 \| 1.5085 \| 0.835 \| 0.8093 \| 0.1346 \| 0.0445 \| \| 217.1217 \| 70.0 \| 1750 \| 213.0247 \| 0.85 \| 0.2530 \| 1.4349 \| 0.85 \| 0.8274 \| 0.1373 \| 0.0427 \| \| 217.1217 \| 71.0 \| 1775 \| 213.0161 \| 0.855 \| 0.2510 \| 1.4529 \| 0.855 \| 0.8333 \| 0.1212 \| 0.0411 \| \| 217.1217 \| 72.0 \| 1800 \| 213.0249 \| 0.845 \| 0.2494 \| 1.4511 \| 0.845 \| 0.8229 \| 0.1358 \| 0.0412 \| \| 217.1217 \| 73.0 \| 1825 \| 213.0014 \| 0.85 \| 0.2548 \| 1.4435 \| 0.85 \| 0.8264 \| 0.1277 \| 0.0390 \| \| 217.1217 \| 74.0 \| 1850 \| 213.0011 \| 0.845 \| 0.2527 \| 1.3719 \| 0.845 \| 0.8206 \| 0.1360 \| 0.0379 \| \| 217.1217 \| 75.0 \| 1875 \| 213.0240 \| 0.845 \| 0.2576 \| 1.4072 \| 0.845 \| 0.8221 \| 0.1284 \| 0.0425 \| \| 217.1217 \| 76.0 \| 1900 \| 212.9793 \| 0.845 \| 0.2534 \| 1.4026 \| 0.845 \| 0.8212 \| 0.1241 \| 0.0404 \| \| 217.1217 \| 77.0 \| 1925 \| 212.9800 \| 0.85 \| 0.2514 \| 1.5023 \| 0.85 \| 0.8271 \| 0.1279 \| 0.0407 \| \| 217.1217 \| 78.0 \| 1950 \| 212.9125 \| 0.845 \| 0.2564 \| 1.4258 \| 0.845 \| 0.8211 \| 0.1298 \| 0.0427 \| \| 217.1217 \| 79.0 \| 1975 \| 212.9454 \| 0.85 \| 0.2527 \| 1.5227 \| 0.85 \| 0.8271 \| 0.1279 \| 0.0423 \| \| 216.765 \| 80.0 \| 2000 \| 212.9475 \| 0.845 \| 0.2551 \| 1.5025 \| 0.845 \| 0.8206 \| 0.1311 \| 0.0423 \| \| 216.765 \| 81.0 \| 2025 \| 212.9739 \| 0.84 \| 0.2567 \| 1.5305 \| 0.8400 \| 0.8162 \| 0.1294 \| 0.0431 \| \| 216.765 \| 82.0 \| 2050 \| 212.9351 \| 0.855 \| 0.2526 \| 1.5373 \| 0.855 \| 0.8339 \| 0.1277 \| 0.0401 \| \| 216.765 \| 83.0 \| 2075 \| 213.0053 \| 0.845 \| 0.2560 \| 1.4724 \| 0.845 \| 0.8228 \| 0.1341 \| 0.0417 \| \| 216.765 \| 84.0 \| 2100 \| 212.9326 \| 0.845 \| 0.2568 \| 1.5217 \| 0.845 \| 0.8206 \| 0.1303 \| 0.0472 \| \| 216.765 \| 85.0 \| 2125 \| 212.9555 \| 0.855 \| 0.2537 \| 1.5265 \| 0.855 \| 0.8339 \| 0.1233 \| 0.0416 \| \| 216.765 \| 86.0 \| 2150 \| 212.9121 \| 0.85 \| 0.2534 \| 1.5224 \| 0.85 \| 0.8280 \| 0.1283 \| 0.0398 \| \| 216.765 \| 87.0 \| 2175 \| 212.8850 \| 0.845 \| 0.2551 \| 1.4480 \| 0.845 \| 0.8221 \| 0.1328 \| 0.0412 \| \| 216.765 \| 88.0 \| 2200 \| 212.9121 \| 0.855 \| 0.2518 \| 1.5069 \| 0.855 \| 0.8339 \| 0.1234 \| 0.0404 \| \| 216.765 \| 89.0 \| 2225 \| 212.9327 \| 0.845 \| 0.2517 \| 1.4532 \| 0.845 \| 0.8206 \| 0.1231 \| 0.0401 \| \| 216.765 \| 90.0 \| 2250 \| 212.9305 \| 0.85 \| 0.2542 \| 1.4506 \| 0.85 \| 0.8271 \| 0.1374 \| 0.0398 \| \| 216.765 \| 91.0 \| 2275 \| 212.9274 \| 0.85 \| 0.2567 \| 1.5045 \| 0.85 \| 0.8280 \| 0.1297 \| 0.0419 \| \| 216.765 \| 92.0 \| 2300 \| 212.8962 \| 0.85 \| 0.2545 \| 1.4956 \| 0.85 \| 0.8280 \| 0.1261 \| 0.0405 \| \| 216.765 \| 93.0 \| 2325 \| 212.9133 \| 0.845 \| 0.2567 \| 1.5274 \| 0.845 \| 0.8212 \| 0.1291 \| 0.0431 \| \| 216.765 \| 94.0 \| 2350 \| 212.8708 \| 0.85 \| 0.2576 \| 1.4410 \| 0.85 \| 0.8280 \| 0.1302 \| 0.0410 \| \| 216.765 \| 95.0 \| 2375 \| 212.9661 \| 0.855 \| 0.2546 \| 1.3988 \| 0.855 \| 0.8339 \| 0.1248 \| 0.0404 \| \| 216.765 \| 96.0 \| 2400 \| 212.9099 \| 0.855 \| 0.2547 \| 1.5096 \| 0.855 \| 0.8333 \| 0.1256 \| 0.0402 \| \| 216.765 \| 97.0 \| 2425 \| 212.9668 \| 0.85 \| 0.2549 \| 1.5337 \| 0.85 \| 0.8271 \| 0.1289 \| 0.0390 \| \| 216.765 \| 98.0 \| 2450 \| 212.9587 \| 0.845 \| 0.2545 \| 1.5161 \| 0.845 \| 0.8215 \| 0.1304 \| 0.0412 \| \| 216.765 \| 99.0 \| 2475 \| 212.9395 \| 0.855 \| 0.2554 \| 1.4606 \| 0.855 \| 0.8333 \| 0.1253 \| 0.0410 \| \| 216.6085 \| 100.0 \| 2500 \| 212.9178 \| 0.855 \| 0.2563 \| 1.4722 \| 0.855 \| 0.8333 \| 0.1253 \| 0.0422 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
hbenitez/AV_classifier_resnet50	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hbenitez/AV_classifier_resnet50 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8261 - Validation Loss: 2.4425 - Train Accuracy: 0.6 - Epoch: 99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 8.2326 \| 8.1060 \| 0.0 \| 0 \| \| 8.3420 \| 7.6394 \| 0.05 \| 1 \| \| 7.9643 \| 7.5706 \| 0.05 \| 2 \| \| 7.9337 \| 7.6265 \| 0.05 \| 3 \| \| 7.8018 \| 7.7736 \| 0.05 \| 4 \| \| 7.8009 \| 7.7905 \| 0.05 \| 5 \| \| 7.6369 \| 7.6354 \| 0.05 \| 6 \| \| 7.4782 \| 7.5608 \| 0.05 \| 7 \| \| 7.3655 \| 7.6271 \| 0.05 \| 8 \| \| 7.2886 \| 7.6028 \| 0.0 \| 9 \| \| 7.1145 \| 7.5211 \| 0.0 \| 10 \| \| 7.1232 \| 7.2993 \| 0.0 \| 11 \| \| 6.8393 \| 7.2079 \| 0.0 \| 12 \| \| 6.8202 \| 7.2143 \| 0.0 \| 13 \| \| 6.7180 \| 7.1236 \| 0.05 \| 14 \| \| 6.7318 \| 7.1061 \| 0.0 \| 15 \| \| 6.4563 \| 6.9758 \| 0.05 \| 16 \| \| 6.3765 \| 6.9413 \| 0.05 \| 17 \| \| 6.1791 \| 6.8315 \| 0.05 \| 18 \| \| 6.1946 \| 6.7703 \| 0.05 \| 19 \| \| 5.8448 \| 6.7431 \| 0.1 \| 20 \| \| 5.8514 \| 6.6876 \| 0.1 \| 21 \| \| 5.8200 \| 6.6353 \| 0.05 \| 22 \| \| 5.8323 \| 6.5814 \| 0.05 \| 23 \| \| 5.5553 \| 6.4306 \| 0.05 \| 24 \| \| 5.4999 \| 6.4455 \| 0.05 \| 25 \| \| 5.4370 \| 6.3026 \| 0.05 \| 26 \| \| 5.2288 \| 6.0093 \| 0.1 \| 27 \| \| 5.2173 \| 6.0593 \| 0.05 \| 28 \| \| 5.2280 \| 6.0598 \| 0.05 \| 29 \| \| 5.0484 \| 5.9769 \| 0.05 \| 30 \| \| 4.8703 \| 5.8336 \| 0.05 \| 31 \| \| 4.9881 \| 5.7711 \| 0.1 \| 32 \| \| 4.5905 \| 5.6685 \| 0.1 \| 33 \| \| 4.7240 \| 5.6156 \| 0.15 \| 34 \| \| 4.5095 \| 5.4680 \| 0.15 \| 35 \| \| 4.2225 \| 5.3962 \| 0.15 \| 36 \| \| 4.3615 \| 5.3290 \| 0.2 \| 37 \| \| 4.1862 \| 5.3602 \| 0.15 \| 38 \| \| 3.9455 \| 5.2635 \| 0.15 \| 39 \| \| 3.9737 \| 5.2337 \| 0.15 \| 40 \| \| 4.0922 \| 5.1268 \| 0.15 \| 41 \| \| 3.6042 \| 4.9972 \| 0.2 \| 42 \| \| 3.7219 \| 4.8787 \| 0.15 \| 43 \| \| 3.5563 \| 4.9075 \| 0.2 \| 44 \| \| 3.5897 \| 4.9157 \| 0.25 \| 45 \| \| 3.5769 \| 4.7936 \| 0.25 \| 46 \| \| 3.6225 \| 4.8689 \| 0.2 \| 47 \| \| 3.4568 \| 4.8767 \| 0.2 \| 48 \| \| 3.1431 \| 4.7520 \| 0.25 \| 49 \| \| 3.0607 \| 4.5815 \| 0.3 \| 50 \| \| 2.8904 \| 4.5007 \| 0.2 \| 51 \| \| 2.8308 \| 4.5054 \| 0.25 \| 52 \| \| 2.8136 \| 4.2745 \| 0.25 \| 53 \| \| 2.6192 \| 4.3300 \| 0.2 \| 54 \| \| 2.5308 \| 4.3180 \| 0.2 \| 55 \| \| 2.5192 \| 4.2706 \| 0.2 \| 56 \| \| 2.5761 \| 4.1395 \| 0.25 \| 57 \| \| 2.3516 \| 3.9031 \| 0.3 \| 58 \| \| 2.3231 \| 3.8172 \| 0.35 \| 59 \| \| 2.2735 \| 3.7651 \| 0.35 \| 60 \| \| 2.1215 \| 3.8034 \| 0.35 \| 61 \| \| 2.3229 \| 3.8096 \| 0.35 \| 62 \| \| 2.2230 \| 3.7000 \| 0.35 \| 63 \| \| 1.9059 \| 3.6666 \| 0.25 \| 64 \| \| 2.0289 \| 3.6743 \| 0.25 \| 65 \| \| 1.9178 \| 3.5819 \| 0.3 \| 66 \| \| 2.0295 \| 3.5087 \| 0.35 \| 67 \| \| 1.6499 \| 3.4962 \| 0.4 \| 68 \| \| 1.6261 \| 3.4146 \| 0.3 \| 69 \| \| 1.7059 \| 3.4097 \| 0.35 \| 70 \| \| 1.4837 \| 3.2702 \| 0.35 \| 71 \| \| 1.3766 \| 3.2214 \| 0.4 \| 72 \| \| 1.5898 \| 3.2674 \| 0.4 \| 73 \| \| 1.5002 \| 3.1907 \| 0.4 \| 74 \| \| 1.2641 \| 3.1176 \| 0.4 \| 75 \| \| 1.3456 \| 3.1562 \| 0.4 \| 76 \| \| 1.2655 \| 2.9548 \| 0.5 \| 77 \| \| 1.5449 \| 2.8738 \| 0.5 \| 78 \| \| 1.2519 \| 2.8336 \| 0.45 \| 79 \| \| 1.0682 \| 2.8478 \| 0.35 \| 80 \| \| 1.1891 \| 2.8408 \| 0.5 \| 81 \| \| 1.2920 \| 2.6254 \| 0.5 \| 82 \| \| 1.1239 \| 2.7507 \| 0.5 \| 83 \| \| 1.0857 \| 2.7772 \| 0.4 \| 84 \| \| 0.9821 \| 2.8372 \| 0.45 \| 85 \| \| 1.0457 \| 2.8636 \| 0.45 \| 86 \| \| 1.1419 \| 2.8426 \| 0.45 \| 87 \| \| 1.0782 \| 2.7856 \| 0.5 \| 88 \| \| 0.9906 \| 2.6826 \| 0.55 \| 89 \| \| 1.0766 \| 2.6707 \| 0.5 \| 90 \| \| 1.1115 \| 2.6457 \| 0.5 \| 91 \| \| 1.2201 \| 2.6838 \| 0.55 \| 92 \| \| 0.8706 \| 2.5262 \| 0.55 \| 93 \| \| 0.7441 \| 2.5422 \| 0.55 \| 94 \| \| 0.9710 \| 2.4211 \| 0.6 \| 95 \| \| 0.9731 \| 2.4090 \| 0.6 \| 96 \| \| 0.8942 \| 2.3773 \| 0.6 \| 97 \| \| 1.0461 \| 2.4159 \| 0.55 \| 98 \| \| 0.8261 \| 2.4425 \| 0.6 \| 99 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.13.0-rc2 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
jordyvl/vit-tiny_tobacco3482_og_simkd_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_tobacco3482_og_simkd_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 212.9255 - Accuracy: 0.825 - Brier Loss: 0.2955 - Nll: 1.3247 - F1 Micro: 0.825 - F1 Macro: 0.8027 - Ece: 0.1660 - Aurc: 0.0554 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 219.5859 \| 0.225 \| 0.8919 \| 5.3905 \| 0.225 \| 0.1403 \| 0.2467 \| 0.7026 \| \| No log \| 2.0 \| 50 \| 218.7605 \| 0.385 \| 0.8149 \| 2.3886 \| 0.3850 \| 0.3040 \| 0.3416 \| 0.4009 \| \| No log \| 3.0 \| 75 \| 217.4839 \| 0.49 \| 0.6311 \| 1.8415 \| 0.49 \| 0.3775 \| 0.2502 \| 0.2789 \| \| No log \| 4.0 \| 100 \| 216.7303 \| 0.605 \| 0.5512 \| 1.9525 \| 0.605 \| 0.4738 \| 0.2423 \| 0.1956 \| \| No log \| 5.0 \| 125 \| 215.7727 \| 0.625 \| 0.4792 \| 1.8491 \| 0.625 \| 0.5148 \| 0.2037 \| 0.1503 \| \| No log \| 6.0 \| 150 \| 215.9141 \| 0.595 \| 0.5723 \| 2.5766 \| 0.595 \| 0.4836 \| 0.2297 \| 0.2039 \| \| No log \| 7.0 \| 175 \| 215.2466 \| 0.715 \| 0.4245 \| 1.8434 \| 0.715 \| 0.5962 \| 0.2236 \| 0.1201 \| \| No log \| 8.0 \| 200 \| 215.0580 \| 0.72 \| 0.3878 \| 1.8760 \| 0.72 \| 0.6108 \| 0.2068 \| 0.0879 \| \| No log \| 9.0 \| 225 \| 214.9401 \| 0.745 \| 0.3919 \| 1.7852 \| 0.745 \| 0.7183 \| 0.1855 \| 0.0936 \| \| No log \| 10.0 \| 250 \| 214.6627 \| 0.66 \| 0.4840 \| 2.1588 \| 0.66 \| 0.6144 \| 0.2371 \| 0.1261 \| \| No log \| 11.0 \| 275 \| 214.9265 \| 0.69 \| 0.5027 \| 1.7910 \| 0.69 \| 0.6599 \| 0.2514 \| 0.1279 \| \| No log \| 12.0 \| 300 \| 214.4700 \| 0.78 \| 0.3372 \| 1.7013 \| 0.78 \| 0.7459 \| 0.1740 \| 0.0741 \| \| No log \| 13.0 \| 325 \| 214.5191 \| 0.805 \| 0.3164 \| 1.5824 \| 0.805 \| 0.7689 \| 0.1662 \| 0.0689 \| \| No log \| 14.0 \| 350 \| 214.3915 \| 0.8 \| 0.3278 \| 1.5290 \| 0.8000 \| 0.7742 \| 0.1716 \| 0.0675 \| \| No log \| 15.0 \| 375 \| 214.2643 \| 0.8 \| 0.3315 \| 1.5964 \| 0.8000 \| 0.7697 \| 0.1491 \| 0.0876 \| \| No log \| 16.0 \| 400 \| 214.2815 \| 0.78 \| 0.3751 \| 1.8388 \| 0.78 \| 0.7670 \| 0.1944 \| 0.0836 \| \| No log \| 17.0 \| 425 \| 214.0954 \| 0.78 \| 0.3505 \| 1.4905 \| 0.78 \| 0.7521 \| 0.1832 \| 0.0662 \| \| No log \| 18.0 \| 450 \| 214.1399 \| 0.785 \| 0.3392 \| 1.2883 \| 0.785 \| 0.7662 \| 0.1760 \| 0.0749 \| \| No log \| 19.0 \| 475 \| 214.1986 \| 0.81 \| 0.3229 \| 1.6305 \| 0.81 \| 0.7984 \| 0.1754 \| 0.0777 \| \| 220.3818 \| 20.0 \| 500 \| 214.1931 \| 0.815 \| 0.2994 \| 1.5637 \| 0.815 \| 0.7940 \| 0.1520 \| 0.0606 \| \| 220.3818 \| 21.0 \| 525 \| 213.9438 \| 0.815 \| 0.3066 \| 1.4756 \| 0.815 \| 0.7983 \| 0.1616 \| 0.0678 \| \| 220.3818 \| 22.0 \| 550 \| 213.9014 \| 0.8 \| 0.3485 \| 1.9629 \| 0.8000 \| 0.7885 \| 0.1838 \| 0.0727 \| \| 220.3818 \| 23.0 \| 575 \| 214.0186 \| 0.83 \| 0.2863 \| 1.6314 \| 0.83 \| 0.8107 \| 0.1516 \| 0.0574 \| \| 220.3818 \| 24.0 \| 600 \| 213.8764 \| 0.805 \| 0.3323 \| 1.4873 \| 0.805 \| 0.7903 \| 0.1716 \| 0.0726 \| \| 220.3818 \| 25.0 \| 625 \| 214.0043 \| 0.81 \| 0.3094 \| 1.5562 \| 0.81 \| 0.7764 \| 0.1625 \| 0.0598 \| \| 220.3818 \| 26.0 \| 650 \| 213.6884 \| 0.825 \| 0.3165 \| 1.6150 \| 0.825 \| 0.8038 \| 0.1667 \| 0.0755 \| \| 220.3818 \| 27.0 \| 675 \| 213.7763 \| 0.81 \| 0.3164 \| 1.5526 \| 0.81 \| 0.7904 \| 0.1767 \| 0.0747 \| \| 220.3818 \| 28.0 \| 700 \| 213.9658 \| 0.825 \| 0.2996 \| 1.7947 \| 0.825 \| 0.8149 \| 0.1686 \| 0.0651 \| \| 220.3818 \| 29.0 \| 725 \| 213.7030 \| 0.815 \| 0.3155 \| 1.3772 \| 0.815 \| 0.8024 \| 0.1616 \| 0.0613 \| \| 220.3818 \| 30.0 \| 750 \| 213.7211 \| 0.805 \| 0.3421 \| 1.5621 \| 0.805 \| 0.7986 \| 0.1794 \| 0.0624 \| \| 220.3818 \| 31.0 \| 775 \| 213.6852 \| 0.815 \| 0.3094 \| 1.5177 \| 0.815 \| 0.7872 \| 0.1500 \| 0.0735 \| \| 220.3818 \| 32.0 \| 800 \| 213.6889 \| 0.785 \| 0.3345 \| 1.4134 \| 0.785 \| 0.7652 \| 0.1669 \| 0.0563 \| \| 220.3818 \| 33.0 \| 825 \| 213.6302 \| 0.805 \| 0.3298 \| 1.8630 \| 0.805 \| 0.7865 \| 0.1689 \| 0.0693 \| \| 220.3818 \| 34.0 \| 850 \| 213.6116 \| 0.83 \| 0.2890 \| 1.5365 \| 0.83 \| 0.8033 \| 0.1555 \| 0.0661 \| \| 220.3818 \| 35.0 \| 875 \| 213.6136 \| 0.805 \| 0.3026 \| 1.1797 \| 0.805 \| 0.7744 \| 0.1551 \| 0.0477 \| \| 220.3818 \| 36.0 \| 900 \| 213.5340 \| 0.815 \| 0.3008 \| 1.6963 \| 0.815 \| 0.7938 \| 0.1531 \| 0.0687 \| \| 220.3818 \| 37.0 \| 925 \| 213.5380 \| 0.84 \| 0.2968 \| 1.3848 \| 0.8400 \| 0.8396 \| 0.1631 \| 0.0577 \| \| 220.3818 \| 38.0 \| 950 \| 213.5322 \| 0.83 \| 0.3016 \| 1.5511 \| 0.83 \| 0.8133 \| 0.1647 \| 0.0599 \| \| 220.3818 \| 39.0 \| 975 \| 213.4971 \| 0.82 \| 0.3155 \| 1.4175 \| 0.82 \| 0.7999 \| 0.1571 \| 0.0535 \| \| 217.7955 \| 40.0 \| 1000 \| 213.4139 \| 0.825 \| 0.3103 \| 1.6359 \| 0.825 \| 0.8043 \| 0.1749 \| 0.0683 \| \| 217.7955 \| 41.0 \| 1025 \| 213.4513 \| 0.83 \| 0.3002 \| 1.5369 \| 0.83 \| 0.8139 \| 0.1580 \| 0.0606 \| \| 217.7955 \| 42.0 \| 1050 \| 213.4196 \| 0.8 \| 0.3251 \| 1.3570 \| 0.8000 \| 0.7779 \| 0.1745 \| 0.0608 \| \| 217.7955 \| 43.0 \| 1075 \| 213.3506 \| 0.815 \| 0.3142 \| 1.3579 \| 0.815 \| 0.7988 \| 0.1724 \| 0.0563 \| \| 217.7955 \| 44.0 \| 1100 \| 213.3151 \| 0.805 \| 0.3217 \| 1.3796 \| 0.805 \| 0.7820 \| 0.1748 \| 0.0580 \| \| 217.7955 \| 45.0 \| 1125 \| 213.3202 \| 0.825 \| 0.3114 \| 1.4198 \| 0.825 \| 0.8090 \| 0.1568 \| 0.0614 \| \| 217.7955 \| 46.0 \| 1150 \| 213.3313 \| 0.805 \| 0.3203 \| 1.3750 \| 0.805 \| 0.7860 \| 0.1667 \| 0.0563 \| \| 217.7955 \| 47.0 \| 1175 \| 213.3293 \| 0.835 \| 0.2910 \| 1.3909 \| 0.835 \| 0.8190 \| 0.1515 \| 0.0576 \| \| 217.7955 \| 48.0 \| 1200 \| 213.2646 \| 0.825 \| 0.2916 \| 1.3674 \| 0.825 \| 0.8022 \| 0.1526 \| 0.0577 \| \| 217.7955 \| 49.0 \| 1225 \| 213.2620 \| 0.83 \| 0.3137 \| 1.4579 \| 0.83 \| 0.8101 \| 0.1634 \| 0.0565 \| \| 217.7955 \| 50.0 \| 1250 \| 213.2164 \| 0.815 \| 0.3087 \| 1.2599 \| 0.815 \| 0.7917 \| 0.1618 \| 0.0573 \| \| 217.7955 \| 51.0 \| 1275 \| 213.2495 \| 0.795 \| 0.3155 \| 1.2060 \| 0.795 \| 0.7648 \| 0.1712 \| 0.0573 \| \| 217.7955 \| 52.0 \| 1300 \| 213.2231 \| 0.82 \| 0.3232 \| 1.4463 \| 0.82 \| 0.8029 \| 0.1658 \| 0.0544 \| \| 217.7955 \| 53.0 \| 1325 \| 213.2242 \| 0.83 \| 0.2891 \| 1.3586 \| 0.83 \| 0.8056 \| 0.1427 \| 0.0520 \| \| 217.7955 \| 54.0 \| 1350 \| 213.2049 \| 0.83 \| 0.2959 \| 1.2968 \| 0.83 \| 0.8063 \| 0.1573 \| 0.0523 \| \| 217.7955 \| 55.0 \| 1375 \| 213.1500 \| 0.84 \| 0.2844 \| 1.4084 \| 0.8400 \| 0.8137 \| 0.1490 \| 0.0570 \| \| 217.7955 \| 56.0 \| 1400 \| 213.1851 \| 0.815 \| 0.3097 \| 1.4005 \| 0.815 \| 0.7892 \| 0.1605 \| 0.0605 \| \| 217.7955 \| 57.0 \| 1425 \| 213.1577 \| 0.805 \| 0.3130 \| 1.2482 \| 0.805 \| 0.7805 \| 0.1635 \| 0.0550 \| \| 217.7955 \| 58.0 \| 1450 \| 213.1812 \| 0.835 \| 0.2943 \| 1.3191 \| 0.835 \| 0.8047 \| 0.1584 \| 0.0550 \| \| 217.7955 \| 59.0 \| 1475 \| 213.0962 \| 0.82 \| 0.3102 \| 1.3163 \| 0.82 \| 0.7906 \| 0.1672 \| 0.0513 \| \| 217.1347 \| 60.0 \| 1500 \| 213.1257 \| 0.835 \| 0.2876 \| 1.3165 \| 0.835 \| 0.8122 \| 0.1433 \| 0.0562 \| \| 217.1347 \| 61.0 \| 1525 \| 213.1092 \| 0.83 \| 0.2896 \| 1.3196 \| 0.83 \| 0.8061 \| 0.1533 \| 0.0549 \| \| 217.1347 \| 62.0 \| 1550 \| 213.0827 \| 0.815 \| 0.3072 \| 1.3053 \| 0.815 \| 0.7925 \| 0.1546 \| 0.0523 \| \| 217.1347 \| 63.0 \| 1575 \| 213.0972 \| 0.825 \| 0.2965 \| 1.2148 \| 0.825 \| 0.8044 \| 0.1472 \| 0.0507 \| \| 217.1347 \| 64.0 \| 1600 \| 213.0849 \| 0.83 \| 0.2923 \| 1.3196 \| 0.83 \| 0.8106 \| 0.1637 \| 0.0551 \| \| 217.1347 \| 65.0 \| 1625 \| 213.0354 \| 0.815 \| 0.3158 \| 1.2749 \| 0.815 \| 0.7867 \| 0.1675 \| 0.0515 \| \| 217.1347 \| 66.0 \| 1650 \| 213.0493 \| 0.825 \| 0.2993 \| 1.3046 \| 0.825 \| 0.7949 \| 0.1636 \| 0.0558 \| \| 217.1347 \| 67.0 \| 1675 \| 212.9869 \| 0.815 \| 0.3081 \| 1.3846 \| 0.815 \| 0.7868 \| 0.1579 \| 0.0583 \| \| 217.1347 \| 68.0 \| 1700 \| 213.0629 \| 0.84 \| 0.2915 \| 1.3902 \| 0.8400 \| 0.8046 \| 0.1590 \| 0.0540 \| \| 217.1347 \| 69.0 \| 1725 \| 213.0391 \| 0.825 \| 0.3068 \| 1.3801 \| 0.825 \| 0.8015 \| 0.1553 \| 0.0531 \| \| 217.1347 \| 70.0 \| 1750 \| 212.9991 \| 0.835 \| 0.2864 \| 1.3331 \| 0.835 \| 0.8097 \| 0.1531 \| 0.0562 \| \| 217.1347 \| 71.0 \| 1775 \| 213.0157 \| 0.83 \| 0.2897 \| 1.2788 \| 0.83 \| 0.8002 \| 0.1584 \| 0.0551 \| \| 217.1347 \| 72.0 \| 1800 \| 212.9134 \| 0.82 \| 0.3051 \| 1.3131 \| 0.82 \| 0.7960 \| 0.1690 \| 0.0522 \| \| 217.1347 \| 73.0 \| 1825 \| 213.0014 \| 0.825 \| 0.2926 \| 1.3111 \| 0.825 \| 0.8040 \| 0.1390 \| 0.0574 \| \| 217.1347 \| 74.0 \| 1850 \| 212.9525 \| 0.82 \| 0.2985 \| 1.3181 \| 0.82 \| 0.7962 \| 0.1579 \| 0.0543 \| \| 217.1347 \| 75.0 \| 1875 \| 212.9581 \| 0.815 \| 0.3024 \| 1.2835 \| 0.815 \| 0.7810 \| 0.1648 \| 0.0504 \| \| 217.1347 \| 76.0 \| 1900 \| 213.0073 \| 0.835 \| 0.2970 \| 1.3745 \| 0.835 \| 0.8095 \| 0.1597 \| 0.0579 \| \| 217.1347 \| 77.0 \| 1925 \| 213.0066 \| 0.805 \| 0.3046 \| 1.3071 \| 0.805 \| 0.7783 \| 0.1502 \| 0.0547 \| \| 217.1347 \| 78.0 \| 1950 \| 212.9872 \| 0.82 \| 0.3018 \| 1.4088 \| 0.82 \| 0.7928 \| 0.1527 \| 0.0527 \| \| 217.1347 \| 79.0 \| 1975 \| 212.9629 \| 0.82 \| 0.3024 \| 1.3665 \| 0.82 \| 0.8012 \| 0.1626 \| 0.0551 \| \| 216.794 \| 80.0 \| 2000 \| 212.9545 \| 0.825 \| 0.3080 \| 1.3609 \| 0.825 \| 0.8062 \| 0.1652 \| 0.0541 \| \| 216.794 \| 81.0 \| 2025 \| 212.9253 \| 0.825 \| 0.3077 \| 1.3779 \| 0.825 \| 0.8044 \| 0.1662 \| 0.0547 \| \| 216.794 \| 82.0 \| 2050 \| 212.9501 \| 0.82 \| 0.3024 \| 1.3636 \| 0.82 \| 0.7928 \| 0.1677 \| 0.0553 \| \| 216.794 \| 83.0 \| 2075 \| 212.9160 \| 0.81 \| 0.3055 \| 1.3686 \| 0.81 \| 0.7786 \| 0.1624 \| 0.0578 \| \| 216.794 \| 84.0 \| 2100 \| 212.9532 \| 0.84 \| 0.2914 \| 1.4589 \| 0.8400 \| 0.8129 \| 0.1482 \| 0.0510 \| \| 216.794 \| 85.0 \| 2125 \| 212.9397 \| 0.825 \| 0.3067 \| 1.3768 \| 0.825 \| 0.7981 \| 0.1653 \| 0.0537 \| \| 216.794 \| 86.0 \| 2150 \| 212.8927 \| 0.83 \| 0.2980 \| 1.3825 \| 0.83 \| 0.8118 \| 0.1662 \| 0.0560 \| \| 216.794 \| 87.0 \| 2175 \| 212.8856 \| 0.825 \| 0.3004 \| 1.4017 \| 0.825 \| 0.8063 \| 0.1595 \| 0.0555 \| \| 216.794 \| 88.0 \| 2200 \| 212.9423 \| 0.82 \| 0.3033 \| 1.3619 \| 0.82 \| 0.8012 \| 0.1539 \| 0.0517 \| \| 216.794 \| 89.0 \| 2225 \| 212.8776 \| 0.84 \| 0.2922 \| 1.3845 \| 0.8400 \| 0.8176 \| 0.1555 \| 0.0537 \| \| 216.794 \| 90.0 \| 2250 \| 212.9439 \| 0.82 \| 0.3011 \| 1.3923 \| 0.82 \| 0.8012 \| 0.1526 \| 0.0535 \| \| 216.794 \| 91.0 \| 2275 \| 212.8640 \| 0.815 \| 0.3006 \| 1.3680 \| 0.815 \| 0.7920 \| 0.1443 \| 0.0548 \| \| 216.794 \| 92.0 \| 2300 \| 212.8850 \| 0.825 \| 0.2940 \| 1.3317 \| 0.825 \| 0.8087 \| 0.1466 \| 0.0548 \| \| 216.794 \| 93.0 \| 2325 \| 212.8843 \| 0.825 \| 0.3024 \| 1.3848 \| 0.825 \| 0.8027 \| 0.1688 \| 0.0529 \| \| 216.794 \| 94.0 \| 2350 \| 212.9464 \| 0.825 \| 0.3013 \| 1.3634 \| 0.825 \| 0.8027 \| 0.1607 \| 0.0515 \| \| 216.794 \| 95.0 \| 2375 \| 212.9154 \| 0.825 \| 0.3001 \| 1.3262 \| 0.825 \| 0.8087 \| 0.1604 \| 0.0539 \| \| 216.794 \| 96.0 \| 2400 \| 212.9131 \| 0.825 \| 0.2984 \| 1.3121 \| 0.825 \| 0.8027 \| 0.1568 \| 0.0538 \| \| 216.794 \| 97.0 \| 2425 \| 212.8888 \| 0.82 \| 0.3003 \| 1.3975 \| 0.82 \| 0.8012 \| 0.1614 \| 0.0527 \| \| 216.794 \| 98.0 \| 2450 \| 212.8581 \| 0.825 \| 0.2989 \| 1.2911 \| 0.825 \| 0.8027 \| 0.1673 \| 0.0542 \| \| 216.794 \| 99.0 \| 2475 \| 212.9360 \| 0.83 \| 0.2983 \| 1.3929 \| 0.83 \| 0.8118 \| 0.1616 \| 0.0534 \| \| 216.6176 \| 100.0 \| 2500 \| 212.9255 \| 0.825 \| 0.2955 \| 1.3247 \| 0.825 \| 0.8027 \| 0.1660 \| 0.0554 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
mvkvc/convnextv2-base-22k-224-finetuned-critique	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-base-22k-224-finetuned-critique This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2878 - Accuracy: 0.8825 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2128 \| 1.0 \| 56 \| 0.3349 \| 0.8588 \| \| 0.2064 \| 1.99 \| 112 \| 0.2999 \| 0.8812 \| \| 0.1788 \| 2.99 \| 168 \| 0.2878 \| 0.8825 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "ai", "real" ]
annazhong/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.1055 - Accuracy: 0.9674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5173 \| 1.0 \| 351 \| 0.1623 \| 0.9444 \| \| 0.4107 \| 2.0 \| 703 \| 0.1182 \| 0.962 \| \| 0.3376 \| 2.99 \| 1053 \| 0.1055 \| 0.9674 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]
mvkvc/convnextv2-base-22k-224-finetuned-critique-100k	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-base-22k-224-finetuned-critique-100k This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1309 - Accuracy: 0.9479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6277 \| 0.07 \| 50 \| 0.5987 \| 0.6767 \| \| 0.5459 \| 0.14 \| 100 \| 0.5187 \| 0.7401 \| \| 0.4397 \| 0.21 \| 150 \| 0.4448 \| 0.7768 \| \| 0.4197 \| 0.28 \| 200 \| 0.3686 \| 0.8401 \| \| 0.3397 \| 0.36 \| 250 \| 0.3153 \| 0.8664 \| \| 0.3345 \| 0.43 \| 300 \| 0.3071 \| 0.8701 \| \| 0.3177 \| 0.5 \| 350 \| 0.2576 \| 0.8938 \| \| 0.3182 \| 0.57 \| 400 \| 0.2546 \| 0.8926 \| \| 0.2596 \| 0.64 \| 450 \| 0.2320 \| 0.9004 \| \| 0.2563 \| 0.71 \| 500 \| 0.2205 \| 0.9082 \| \| 0.2543 \| 0.78 \| 550 \| 0.2142 \| 0.9147 \| \| 0.2768 \| 0.85 \| 600 \| 0.2136 \| 0.9132 \| \| 0.2486 \| 0.92 \| 650 \| 0.2052 \| 0.9175 \| \| 0.2504 \| 1.0 \| 700 \| 0.2314 \| 0.9058 \| \| 0.2437 \| 1.07 \| 750 \| 0.1943 \| 0.9235 \| \| 0.212 \| 1.14 \| 800 \| 0.2019 \| 0.9183 \| \| 0.1891 \| 1.21 \| 850 \| 0.1845 \| 0.9254 \| \| 0.2105 \| 1.28 \| 900 \| 0.1834 \| 0.9288 \| \| 0.2285 \| 1.35 \| 950 \| 0.1994 \| 0.9206 \| \| 0.2214 \| 1.42 \| 1000 \| 0.1804 \| 0.9251 \| \| 0.1848 \| 1.49 \| 1050 \| 0.1975 \| 0.9196 \| \| 0.191 \| 1.56 \| 1100 \| 0.1795 \| 0.9269 \| \| 0.1794 \| 1.64 \| 1150 \| 0.1606 \| 0.9358 \| \| 0.2084 \| 1.71 \| 1200 \| 0.1807 \| 0.9293 \| \| 0.199 \| 1.78 \| 1250 \| 0.1697 \| 0.9307 \| \| 0.1874 \| 1.85 \| 1300 \| 0.1650 \| 0.9372 \| \| 0.1681 \| 1.92 \| 1350 \| 0.1515 \| 0.939 \| \| 0.1696 \| 1.99 \| 1400 \| 0.1473 \| 0.9416 \| \| 0.1651 \| 2.06 \| 1450 \| 0.1489 \| 0.9428 \| \| 0.1627 \| 2.13 \| 1500 \| 0.1529 \| 0.9395 \| \| 0.1754 \| 2.2 \| 1550 \| 0.1540 \| 0.9379 \| \| 0.1302 \| 2.28 \| 1600 \| 0.1579 \| 0.939 \| \| 0.1643 \| 2.35 \| 1650 \| 0.1518 \| 0.9401 \| \| 0.1938 \| 2.42 \| 1700 \| 0.1479 \| 0.941 \| \| 0.1441 \| 2.49 \| 1750 \| 0.1451 \| 0.9436 \| \| 0.1478 \| 2.56 \| 1800 \| 0.1324 \| 0.9472 \| \| 0.1275 \| 2.63 \| 1850 \| 0.1340 \| 0.9466 \| \| 0.1582 \| 2.7 \| 1900 \| 0.1501 \| 0.9391 \| \| 0.1472 \| 2.77 \| 1950 \| 0.1354 \| 0.9451 \| \| 0.1522 \| 2.84 \| 2000 \| 0.1309 \| 0.9479 \| \| 0.1593 \| 2.92 \| 2050 \| 0.1433 \| 0.9452 \| \| 0.1541 \| 2.99 \| 2100 \| 0.1381 \| 0.9466 \| \| 0.1297 \| 3.06 \| 2150 \| 0.1320 \| 0.9479 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "ai", "real" ]
jordyvl/vit-small_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-small_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 56.9670 - Accuracy: 0.835 - Brier Loss: 0.2969 - Nll: 1.1900 - F1 Micro: 0.835 - F1 Macro: 0.8377 - Ece: 0.1545 - Aurc: 0.0499 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 59.9015 \| 0.37 \| 0.7765 \| 4.3056 \| 0.37 \| 0.2185 \| 0.2975 \| 0.4588 \| \| No log \| 2.0 \| 50 \| 58.9173 \| 0.66 \| 0.4866 \| 2.0758 \| 0.66 \| 0.5717 \| 0.2732 \| 0.1578 \| \| No log \| 3.0 \| 75 \| 58.3604 \| 0.745 \| 0.3466 \| 1.5077 \| 0.745 \| 0.7135 \| 0.1846 \| 0.0854 \| \| No log \| 4.0 \| 100 \| 58.0585 \| 0.75 \| 0.3628 \| 1.5044 \| 0.75 \| 0.7674 \| 0.2058 \| 0.1052 \| \| No log \| 5.0 \| 125 \| 57.8363 \| 0.76 \| 0.3782 \| 1.7066 \| 0.76 \| 0.7657 \| 0.2174 \| 0.1039 \| \| No log \| 6.0 \| 150 \| 57.4894 \| 0.75 \| 0.3593 \| 1.5137 \| 0.75 \| 0.7377 \| 0.1724 \| 0.0800 \| \| No log \| 7.0 \| 175 \| 57.5188 \| 0.76 \| 0.3631 \| 1.9770 \| 0.76 \| 0.7514 \| 0.1968 \| 0.0874 \| \| No log \| 8.0 \| 200 \| 57.4349 \| 0.74 \| 0.3947 \| 1.8766 \| 0.74 \| 0.7412 \| 0.1753 \| 0.0777 \| \| No log \| 9.0 \| 225 \| 57.1764 \| 0.765 \| 0.3481 \| 1.1532 \| 0.765 \| 0.7411 \| 0.1956 \| 0.0829 \| \| No log \| 10.0 \| 250 \| 57.6192 \| 0.765 \| 0.3943 \| 1.8998 \| 0.765 \| 0.7755 \| 0.1850 \| 0.0981 \| \| No log \| 11.0 \| 275 \| 57.2121 \| 0.77 \| 0.3531 \| 1.2685 \| 0.7700 \| 0.7643 \| 0.1739 \| 0.0689 \| \| No log \| 12.0 \| 300 \| 57.2250 \| 0.795 \| 0.3279 \| 1.7553 \| 0.795 \| 0.7816 \| 0.1596 \| 0.0660 \| \| No log \| 13.0 \| 325 \| 57.4911 \| 0.785 \| 0.3678 \| 2.0499 \| 0.785 \| 0.7857 \| 0.1788 \| 0.0945 \| \| No log \| 14.0 \| 350 \| 57.1481 \| 0.77 \| 0.3542 \| 1.4834 \| 0.7700 \| 0.7649 \| 0.1892 \| 0.0636 \| \| No log \| 15.0 \| 375 \| 57.1701 \| 0.825 \| 0.3041 \| 1.6075 \| 0.825 \| 0.8223 \| 0.1609 \| 0.0621 \| \| No log \| 16.0 \| 400 \| 57.4059 \| 0.805 \| 0.3343 \| 1.7348 \| 0.805 \| 0.8080 \| 0.1654 \| 0.0822 \| \| No log \| 17.0 \| 425 \| 57.9813 \| 0.72 \| 0.4616 \| 2.6345 \| 0.72 \| 0.7252 \| 0.2263 \| 0.1101 \| \| No log \| 18.0 \| 450 \| 57.2677 \| 0.825 \| 0.2953 \| 1.5836 \| 0.825 \| 0.8171 \| 0.1590 \| 0.0572 \| \| No log \| 19.0 \| 475 \| 57.6052 \| 0.765 \| 0.4023 \| 1.7463 \| 0.765 \| 0.7333 \| 0.2052 \| 0.0822 \| \| 57.2084 \| 20.0 \| 500 \| 57.4249 \| 0.79 \| 0.3653 \| 1.5564 \| 0.79 \| 0.7941 \| 0.1818 \| 0.0845 \| \| 57.2084 \| 21.0 \| 525 \| 57.2631 \| 0.845 \| 0.2704 \| 1.7326 \| 0.845 \| 0.8312 \| 0.1358 \| 0.0628 \| \| 57.2084 \| 22.0 \| 550 \| 57.1520 \| 0.845 \| 0.2723 \| 1.2743 \| 0.845 \| 0.8386 \| 0.1402 \| 0.0551 \| \| 57.2084 \| 23.0 \| 575 \| 57.2977 \| 0.82 \| 0.3137 \| 1.3068 \| 0.82 \| 0.8029 \| 0.1578 \| 0.0621 \| \| 57.2084 \| 24.0 \| 600 \| 57.2030 \| 0.81 \| 0.3107 \| 1.5814 \| 0.81 \| 0.7870 \| 0.1594 \| 0.0688 \| \| 57.2084 \| 25.0 \| 625 \| 57.1500 \| 0.82 \| 0.3027 \| 1.4128 \| 0.82 \| 0.8229 \| 0.1584 \| 0.0436 \| \| 57.2084 \| 26.0 \| 650 \| 57.1619 \| 0.855 \| 0.2735 \| 1.5164 \| 0.855 \| 0.8558 \| 0.1404 \| 0.0530 \| \| 57.2084 \| 27.0 \| 675 \| 57.1504 \| 0.845 \| 0.2832 \| 1.5742 \| 0.845 \| 0.8507 \| 0.1500 \| 0.0516 \| \| 57.2084 \| 28.0 \| 700 \| 57.1829 \| 0.835 \| 0.2932 \| 1.4010 \| 0.835 \| 0.8410 \| 0.1489 \| 0.0496 \| \| 57.2084 \| 29.0 \| 725 \| 57.1899 \| 0.83 \| 0.2953 \| 1.4038 \| 0.83 \| 0.8338 \| 0.1497 \| 0.0511 \| \| 57.2084 \| 30.0 \| 750 \| 57.1644 \| 0.835 \| 0.2948 \| 1.3923 \| 0.835 \| 0.8374 \| 0.1509 \| 0.0507 \| \| 57.2084 \| 31.0 \| 775 \| 57.1720 \| 0.83 \| 0.2958 \| 1.4622 \| 0.83 \| 0.8296 \| 0.1502 \| 0.0509 \| \| 57.2084 \| 32.0 \| 800 \| 57.1365 \| 0.835 \| 0.3024 \| 1.2976 \| 0.835 \| 0.8374 \| 0.1575 \| 0.0509 \| \| 57.2084 \| 33.0 \| 825 \| 57.1499 \| 0.835 \| 0.2995 \| 1.3654 \| 0.835 \| 0.8308 \| 0.1574 \| 0.0523 \| \| 57.2084 \| 34.0 \| 850 \| 57.1064 \| 0.83 \| 0.3022 \| 1.3606 \| 0.83 \| 0.8251 \| 0.1578 \| 0.0526 \| \| 57.2084 \| 35.0 \| 875 \| 57.0901 \| 0.835 \| 0.3003 \| 1.2803 \| 0.835 \| 0.8336 \| 0.1554 \| 0.0516 \| \| 57.2084 \| 36.0 \| 900 \| 57.0922 \| 0.835 \| 0.3047 \| 1.2749 \| 0.835 \| 0.8336 \| 0.1571 \| 0.0517 \| \| 57.2084 \| 37.0 \| 925 \| 57.0673 \| 0.83 \| 0.3034 \| 1.2533 \| 0.83 \| 0.8344 \| 0.1559 \| 0.0509 \| \| 57.2084 \| 38.0 \| 950 \| 57.0810 \| 0.83 \| 0.3024 \| 1.2718 \| 0.83 \| 0.8344 \| 0.1620 \| 0.0526 \| \| 57.2084 \| 39.0 \| 975 \| 57.1040 \| 0.835 \| 0.3041 \| 1.2522 \| 0.835 \| 0.8392 \| 0.1571 \| 0.0506 \| \| 56.1387 \| 40.0 \| 1000 \| 57.0542 \| 0.835 \| 0.3024 \| 1.3210 \| 0.835 \| 0.8392 \| 0.1525 \| 0.0501 \| \| 56.1387 \| 41.0 \| 1025 \| 57.0554 \| 0.83 \| 0.3037 \| 1.3231 \| 0.83 \| 0.8344 \| 0.1534 \| 0.0508 \| \| 56.1387 \| 42.0 \| 1050 \| 57.0724 \| 0.83 \| 0.2989 \| 1.2517 \| 0.83 \| 0.8344 \| 0.1485 \| 0.0495 \| \| 56.1387 \| 43.0 \| 1075 \| 57.0429 \| 0.835 \| 0.3010 \| 1.3082 \| 0.835 \| 0.8401 \| 0.1557 \| 0.0506 \| \| 56.1387 \| 44.0 \| 1100 \| 57.0208 \| 0.835 \| 0.3001 \| 1.2428 \| 0.835 \| 0.8392 \| 0.1583 \| 0.0496 \| \| 56.1387 \| 45.0 \| 1125 \| 57.0700 \| 0.835 \| 0.2996 \| 1.3149 \| 0.835 \| 0.8454 \| 0.1601 \| 0.0509 \| \| 56.1387 \| 46.0 \| 1150 \| 57.0054 \| 0.835 \| 0.2950 \| 1.3019 \| 0.835 \| 0.8407 \| 0.1476 \| 0.0492 \| \| 56.1387 \| 47.0 \| 1175 \| 57.0516 \| 0.825 \| 0.3000 \| 1.2344 \| 0.825 \| 0.8317 \| 0.1485 \| 0.0511 \| \| 56.1387 \| 48.0 \| 1200 \| 57.0373 \| 0.835 \| 0.3008 \| 1.3016 \| 0.835 \| 0.8434 \| 0.1611 \| 0.0498 \| \| 56.1387 \| 49.0 \| 1225 \| 57.0154 \| 0.83 \| 0.2982 \| 1.2376 \| 0.83 \| 0.8329 \| 0.1515 \| 0.0501 \| \| 56.1387 \| 50.0 \| 1250 \| 57.0000 \| 0.835 \| 0.2982 \| 1.2196 \| 0.835 \| 0.8434 \| 0.1535 \| 0.0493 \| \| 56.1387 \| 51.0 \| 1275 \| 57.0054 \| 0.825 \| 0.2987 \| 1.2217 \| 0.825 \| 0.8352 \| 0.1517 \| 0.0505 \| \| 56.1387 \| 52.0 \| 1300 \| 57.0347 \| 0.835 \| 0.2996 \| 1.2239 \| 0.835 \| 0.8407 \| 0.1643 \| 0.0486 \| \| 56.1387 \| 53.0 \| 1325 \| 57.0183 \| 0.835 \| 0.2989 \| 1.2208 \| 0.835 \| 0.8411 \| 0.1604 \| 0.0495 \| \| 56.1387 \| 54.0 \| 1350 \| 57.0094 \| 0.845 \| 0.2925 \| 1.1545 \| 0.845 \| 0.8494 \| 0.1515 \| 0.0486 \| \| 56.1387 \| 55.0 \| 1375 \| 57.0027 \| 0.83 \| 0.2974 \| 1.2161 \| 0.83 \| 0.8380 \| 0.1538 \| 0.0491 \| \| 56.1387 \| 56.0 \| 1400 \| 57.0060 \| 0.835 \| 0.2975 \| 1.2215 \| 0.835 \| 0.8407 \| 0.1546 \| 0.0505 \| \| 56.1387 \| 57.0 \| 1425 \| 56.9898 \| 0.835 \| 0.2959 \| 1.1432 \| 0.835 \| 0.8411 \| 0.1483 \| 0.0501 \| \| 56.1387 \| 58.0 \| 1450 \| 56.9907 \| 0.835 \| 0.2963 \| 1.1437 \| 0.835 \| 0.8406 \| 0.1527 \| 0.0485 \| \| 56.1387 \| 59.0 \| 1475 \| 56.9578 \| 0.84 \| 0.2935 \| 1.1583 \| 0.8400 \| 0.8439 \| 0.1513 \| 0.0488 \| \| 55.9877 \| 60.0 \| 1500 \| 57.0032 \| 0.84 \| 0.2957 \| 1.2160 \| 0.8400 \| 0.8439 \| 0.1460 \| 0.0502 \| \| 55.9877 \| 61.0 \| 1525 \| 56.9880 \| 0.835 \| 0.2990 \| 1.2836 \| 0.835 \| 0.8406 \| 0.1475 \| 0.0489 \| \| 55.9877 \| 62.0 \| 1550 \| 56.9920 \| 0.83 \| 0.2973 \| 1.2071 \| 0.83 \| 0.8349 \| 0.1519 \| 0.0494 \| \| 55.9877 \| 63.0 \| 1575 \| 56.9681 \| 0.835 \| 0.2978 \| 1.2076 \| 0.835 \| 0.8406 \| 0.1465 \| 0.0483 \| \| 55.9877 \| 64.0 \| 1600 \| 56.9772 \| 0.835 \| 0.3003 \| 1.1997 \| 0.835 \| 0.8406 \| 0.1567 \| 0.0489 \| \| 55.9877 \| 65.0 \| 1625 \| 56.9705 \| 0.835 \| 0.2973 \| 1.2038 \| 0.835 \| 0.8406 \| 0.1520 \| 0.0495 \| \| 55.9877 \| 66.0 \| 1650 \| 56.9682 \| 0.835 \| 0.2977 \| 1.2005 \| 0.835 \| 0.8406 \| 0.1576 \| 0.0488 \| \| 55.9877 \| 67.0 \| 1675 \| 56.9775 \| 0.835 \| 0.2981 \| 1.2093 \| 0.835 \| 0.8406 \| 0.1497 \| 0.0501 \| \| 55.9877 \| 68.0 \| 1700 \| 56.9762 \| 0.835 \| 0.2989 \| 1.2061 \| 0.835 \| 0.8406 \| 0.1626 \| 0.0491 \| \| 55.9877 \| 69.0 \| 1725 \| 56.9807 \| 0.84 \| 0.2978 \| 1.2023 \| 0.8400 \| 0.8434 \| 0.1503 \| 0.0481 \| \| 55.9877 \| 70.0 \| 1750 \| 56.9705 \| 0.835 \| 0.2988 \| 1.1987 \| 0.835 \| 0.8406 \| 0.1564 \| 0.0487 \| \| 55.9877 \| 71.0 \| 1775 \| 56.9752 \| 0.83 \| 0.2987 \| 1.2027 \| 0.83 \| 0.8349 \| 0.1593 \| 0.0497 \| \| 55.9877 \| 72.0 \| 1800 \| 56.9957 \| 0.83 \| 0.2996 \| 1.2060 \| 0.83 \| 0.8349 \| 0.1607 \| 0.0496 \| \| 55.9877 \| 73.0 \| 1825 \| 56.9697 \| 0.84 \| 0.2966 \| 1.1977 \| 0.8400 \| 0.8434 \| 0.1510 \| 0.0487 \| \| 55.9877 \| 74.0 \| 1850 \| 56.9644 \| 0.83 \| 0.2997 \| 1.2055 \| 0.83 \| 0.8349 \| 0.1528 \| 0.0506 \| \| 55.9877 \| 75.0 \| 1875 \| 56.9677 \| 0.84 \| 0.2968 \| 1.1969 \| 0.8400 \| 0.8434 \| 0.1536 \| 0.0495 \| \| 55.9877 \| 76.0 \| 1900 \| 56.9609 \| 0.84 \| 0.2958 \| 1.1921 \| 0.8400 \| 0.8434 \| 0.1531 \| 0.0495 \| \| 55.9877 \| 77.0 \| 1925 \| 56.9663 \| 0.835 \| 0.2965 \| 1.1950 \| 0.835 \| 0.8406 \| 0.1576 \| 0.0494 \| \| 55.9877 \| 78.0 \| 1950 \| 56.9796 \| 0.83 \| 0.2968 \| 1.2049 \| 0.83 \| 0.8349 \| 0.1525 \| 0.0496 \| \| 55.9877 \| 79.0 \| 1975 \| 56.9648 \| 0.835 \| 0.2966 \| 1.1944 \| 0.835 \| 0.8406 \| 0.1545 \| 0.0494 \| \| 55.9237 \| 80.0 \| 2000 \| 56.9596 \| 0.845 \| 0.2944 \| 1.1912 \| 0.845 \| 0.8480 \| 0.1543 \| 0.0492 \| \| 55.9237 \| 81.0 \| 2025 \| 56.9596 \| 0.84 \| 0.2951 \| 1.1878 \| 0.8400 \| 0.8434 \| 0.1546 \| 0.0492 \| \| 55.9237 \| 82.0 \| 2050 \| 56.9737 \| 0.84 \| 0.2958 \| 1.1954 \| 0.8400 \| 0.8434 \| 0.1521 \| 0.0498 \| \| 55.9237 \| 83.0 \| 2075 \| 56.9725 \| 0.835 \| 0.2974 \| 1.1963 \| 0.835 \| 0.8377 \| 0.1512 \| 0.0500 \| \| 55.9237 \| 84.0 \| 2100 \| 56.9743 \| 0.835 \| 0.2978 \| 1.1928 \| 0.835 \| 0.8406 \| 0.1554 \| 0.0500 \| \| 55.9237 \| 85.0 \| 2125 \| 56.9788 \| 0.835 \| 0.2971 \| 1.1952 \| 0.835 \| 0.8377 \| 0.1493 \| 0.0500 \| \| 55.9237 \| 86.0 \| 2150 \| 56.9705 \| 0.84 \| 0.2968 \| 1.1933 \| 0.8400 \| 0.8434 \| 0.1541 \| 0.0499 \| \| 55.9237 \| 87.0 \| 2175 \| 56.9684 \| 0.835 \| 0.2966 \| 1.1926 \| 0.835 \| 0.8377 \| 0.1517 \| 0.0497 \| \| 55.9237 \| 88.0 \| 2200 \| 56.9725 \| 0.835 \| 0.2979 \| 1.1934 \| 0.835 \| 0.8377 \| 0.1548 \| 0.0497 \| \| 55.9237 \| 89.0 \| 2225 \| 56.9704 \| 0.84 \| 0.2959 \| 1.1934 \| 0.8400 \| 0.8434 \| 0.1527 \| 0.0495 \| \| 55.9237 \| 90.0 \| 2250 \| 56.9681 \| 0.84 \| 0.2950 \| 1.1907 \| 0.8400 \| 0.8434 \| 0.1503 \| 0.0498 \| \| 55.9237 \| 91.0 \| 2275 \| 56.9763 \| 0.835 \| 0.2979 \| 1.1934 \| 0.835 \| 0.8377 \| 0.1516 \| 0.0501 \| \| 55.9237 \| 92.0 \| 2300 \| 56.9649 \| 0.835 \| 0.2959 \| 1.1889 \| 0.835 \| 0.8377 \| 0.1501 \| 0.0495 \| \| 55.9237 \| 93.0 \| 2325 \| 56.9687 \| 0.835 \| 0.2959 \| 1.1871 \| 0.835 \| 0.8377 \| 0.1519 \| 0.0501 \| \| 55.9237 \| 94.0 \| 2350 \| 56.9663 \| 0.835 \| 0.2963 \| 1.1901 \| 0.835 \| 0.8377 \| 0.1533 \| 0.0496 \| \| 55.9237 \| 95.0 \| 2375 \| 56.9674 \| 0.84 \| 0.2955 \| 1.1895 \| 0.8400 \| 0.8434 \| 0.1534 \| 0.0498 \| \| 55.9237 \| 96.0 \| 2400 \| 56.9661 \| 0.835 \| 0.2966 \| 1.1907 \| 0.835 \| 0.8377 \| 0.1520 \| 0.0496 \| \| 55.9237 \| 97.0 \| 2425 \| 56.9623 \| 0.84 \| 0.2958 \| 1.1871 \| 0.8400 \| 0.8434 \| 0.1532 \| 0.0499 \| \| 55.9237 \| 98.0 \| 2450 \| 56.9694 \| 0.835 \| 0.2969 \| 1.1897 \| 0.835 \| 0.8377 \| 0.1543 \| 0.0499 \| \| 55.9237 \| 99.0 \| 2475 \| 56.9698 \| 0.835 \| 0.2967 \| 1.1906 \| 0.835 \| 0.8377 \| 0.1543 \| 0.0499 \| \| 55.8955 \| 100.0 \| 2500 \| 56.9670 \| 0.835 \| 0.2969 \| 1.1900 \| 0.835 \| 0.8377 \| 0.1545 \| 0.0499 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
dyvapandhu/vit-base-molecul	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-molecul This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6052 - Accuracy: 0.69 - F1: 0.6900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4633 \| 3.85 \| 50 \| 0.6052 \| 0.69 \| 0.6900 \| \| 0.0341 \| 7.69 \| 100 \| 0.8705 \| 0.71 \| 0.7100 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.11.0	[ "a", "c" ]
jordyvl/vit-tiny_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 56.8340 - Accuracy: 0.85 - Brier Loss: 0.2391 - Nll: 1.2700 - F1 Micro: 0.85 - F1 Macro: 0.8414 - Ece: 0.1228 - Aurc: 0.0436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 59.9929 \| 0.25 \| 0.8706 \| 4.9000 \| 0.25 \| 0.1856 \| 0.3000 \| 0.7819 \| \| No log \| 2.0 \| 50 \| 59.2119 \| 0.54 \| 0.5951 \| 2.8481 \| 0.54 \| 0.4540 \| 0.2868 \| 0.2585 \| \| No log \| 3.0 \| 75 \| 58.7608 \| 0.675 \| 0.4417 \| 1.6161 \| 0.675 \| 0.6086 \| 0.2356 \| 0.1335 \| \| No log \| 4.0 \| 100 \| 58.5280 \| 0.75 \| 0.3897 \| 1.7733 \| 0.75 \| 0.7378 \| 0.2211 \| 0.1173 \| \| No log \| 5.0 \| 125 \| 58.2236 \| 0.81 \| 0.3119 \| 1.5196 \| 0.81 \| 0.7653 \| 0.1573 \| 0.0769 \| \| No log \| 6.0 \| 150 \| 58.0804 \| 0.805 \| 0.3048 \| 1.6467 \| 0.805 \| 0.7766 \| 0.1355 \| 0.0599 \| \| No log \| 7.0 \| 175 \| 58.3912 \| 0.645 \| 0.5320 \| 2.1424 \| 0.645 \| 0.6017 \| 0.2645 \| 0.1497 \| \| No log \| 8.0 \| 200 \| 57.7380 \| 0.755 \| 0.3634 \| 1.4581 \| 0.755 \| 0.7173 \| 0.1913 \| 0.0920 \| \| No log \| 9.0 \| 225 \| 57.5060 \| 0.805 \| 0.2939 \| 1.4346 \| 0.805 \| 0.7860 \| 0.1603 \| 0.0636 \| \| No log \| 10.0 \| 250 \| 57.5367 \| 0.805 \| 0.3345 \| 1.7441 \| 0.805 \| 0.7872 \| 0.1748 \| 0.0727 \| \| No log \| 11.0 \| 275 \| 57.7193 \| 0.725 \| 0.4195 \| 1.7223 \| 0.7250 \| 0.6706 \| 0.2121 \| 0.0984 \| \| No log \| 12.0 \| 300 \| 57.5447 \| 0.785 \| 0.3435 \| 1.7574 \| 0.785 \| 0.7772 \| 0.1667 \| 0.0678 \| \| No log \| 13.0 \| 325 \| 57.4991 \| 0.795 \| 0.3335 \| 1.5337 \| 0.795 \| 0.7724 \| 0.1685 \| 0.0895 \| \| No log \| 14.0 \| 350 \| 57.3536 \| 0.835 \| 0.2942 \| 1.5660 \| 0.835 \| 0.8089 \| 0.1451 \| 0.0594 \| \| No log \| 15.0 \| 375 \| 57.5013 \| 0.815 \| 0.3096 \| 1.7294 \| 0.815 \| 0.7978 \| 0.1579 \| 0.0696 \| \| No log \| 16.0 \| 400 \| 57.3610 \| 0.835 \| 0.2763 \| 1.7078 \| 0.835 \| 0.8182 \| 0.1354 \| 0.0526 \| \| No log \| 17.0 \| 425 \| 57.3415 \| 0.825 \| 0.3002 \| 1.3895 \| 0.825 \| 0.8249 \| 0.1455 \| 0.0651 \| \| No log \| 18.0 \| 450 \| 57.4573 \| 0.79 \| 0.3649 \| 1.6263 \| 0.79 \| 0.7688 \| 0.1936 \| 0.0943 \| \| No log \| 19.0 \| 475 \| 57.5728 \| 0.8 \| 0.3261 \| 1.7616 \| 0.8000 \| 0.7461 \| 0.1669 \| 0.0659 \| \| 57.5035 \| 20.0 \| 500 \| 57.3014 \| 0.835 \| 0.2982 \| 1.3501 \| 0.835 \| 0.8175 \| 0.1442 \| 0.0549 \| \| 57.5035 \| 21.0 \| 525 \| 57.3792 \| 0.82 \| 0.3206 \| 1.4779 \| 0.82 \| 0.7957 \| 0.1698 \| 0.0568 \| \| 57.5035 \| 22.0 \| 550 \| 57.6968 \| 0.795 \| 0.3627 \| 1.8539 \| 0.795 \| 0.7468 \| 0.1809 \| 0.0698 \| \| 57.5035 \| 23.0 \| 575 \| 57.4539 \| 0.8 \| 0.3465 \| 1.4210 \| 0.8000 \| 0.7787 \| 0.1736 \| 0.0776 \| \| 57.5035 \| 24.0 \| 600 \| 57.1479 \| 0.815 \| 0.2998 \| 1.3434 \| 0.815 \| 0.7989 \| 0.1513 \| 0.0607 \| \| 57.5035 \| 25.0 \| 625 \| 57.3390 \| 0.79 \| 0.3482 \| 1.2595 \| 0.79 \| 0.7803 \| 0.1635 \| 0.0652 \| \| 57.5035 \| 26.0 \| 650 \| 57.4046 \| 0.82 \| 0.3130 \| 1.5825 \| 0.82 \| 0.8060 \| 0.1495 \| 0.0692 \| \| 57.5035 \| 27.0 \| 675 \| 57.1398 \| 0.835 \| 0.2643 \| 1.5441 \| 0.835 \| 0.8185 \| 0.1317 \| 0.0485 \| \| 57.5035 \| 28.0 \| 700 \| 57.4322 \| 0.82 \| 0.3217 \| 1.5260 \| 0.82 \| 0.7912 \| 0.1576 \| 0.0643 \| \| 57.5035 \| 29.0 \| 725 \| 57.1362 \| 0.87 \| 0.2291 \| 1.3903 \| 0.87 \| 0.8621 \| 0.1104 \| 0.0539 \| \| 57.5035 \| 30.0 \| 750 \| 57.1131 \| 0.855 \| 0.2511 \| 1.1423 \| 0.855 \| 0.8451 \| 0.1292 \| 0.0492 \| \| 57.5035 \| 31.0 \| 775 \| 56.8690 \| 0.845 \| 0.2454 \| 1.2822 \| 0.845 \| 0.8265 \| 0.1254 \| 0.0473 \| \| 57.5035 \| 32.0 \| 800 \| 56.8384 \| 0.855 \| 0.2220 \| 1.2243 \| 0.855 \| 0.8453 \| 0.1119 \| 0.0382 \| \| 57.5035 \| 33.0 \| 825 \| 56.9461 \| 0.855 \| 0.2450 \| 1.2192 \| 0.855 \| 0.8537 \| 0.1276 \| 0.0395 \| \| 57.5035 \| 34.0 \| 850 \| 56.9061 \| 0.85 \| 0.2450 \| 1.2097 \| 0.85 \| 0.8337 \| 0.1216 \| 0.0408 \| \| 57.5035 \| 35.0 \| 875 \| 56.9100 \| 0.86 \| 0.2413 \| 1.2197 \| 0.8600 \| 0.8546 \| 0.1189 \| 0.0387 \| \| 57.5035 \| 36.0 \| 900 \| 56.9087 \| 0.855 \| 0.2444 \| 1.2098 \| 0.855 \| 0.8460 \| 0.1281 \| 0.0379 \| \| 57.5035 \| 37.0 \| 925 \| 56.8923 \| 0.86 \| 0.2438 \| 1.2156 \| 0.8600 \| 0.8509 \| 0.1259 \| 0.0378 \| \| 57.5035 \| 38.0 \| 950 \| 56.8908 \| 0.85 \| 0.2450 \| 1.2187 \| 0.85 \| 0.8397 \| 0.1236 \| 0.0390 \| \| 57.5035 \| 39.0 \| 975 \| 56.8591 \| 0.855 \| 0.2404 \| 1.2063 \| 0.855 \| 0.8411 \| 0.1326 \| 0.0383 \| \| 56.2493 \| 40.0 \| 1000 \| 56.8479 \| 0.86 \| 0.2352 \| 1.2142 \| 0.8600 \| 0.8539 \| 0.1257 \| 0.0386 \| \| 56.2493 \| 41.0 \| 1025 \| 56.8762 \| 0.86 \| 0.2365 \| 1.2166 \| 0.8600 \| 0.8492 \| 0.1171 \| 0.0383 \| \| 56.2493 \| 42.0 \| 1050 \| 56.8551 \| 0.865 \| 0.2321 \| 1.2078 \| 0.865 \| 0.8547 \| 0.1183 \| 0.0383 \| \| 56.2493 \| 43.0 \| 1075 \| 56.8913 \| 0.87 \| 0.2360 \| 1.2013 \| 0.87 \| 0.8617 \| 0.1187 \| 0.0410 \| \| 56.2493 \| 44.0 \| 1100 \| 56.8523 \| 0.86 \| 0.2359 \| 1.2094 \| 0.8600 \| 0.8506 \| 0.1214 \| 0.0399 \| \| 56.2493 \| 45.0 \| 1125 \| 56.8546 \| 0.87 \| 0.2330 \| 1.2028 \| 0.87 \| 0.8650 \| 0.1233 \| 0.0384 \| \| 56.2493 \| 46.0 \| 1150 \| 56.8536 \| 0.865 \| 0.2323 \| 1.2099 \| 0.865 \| 0.8579 \| 0.1230 \| 0.0389 \| \| 56.2493 \| 47.0 \| 1175 \| 56.8490 \| 0.865 \| 0.2346 \| 1.2095 \| 0.865 \| 0.8580 \| 0.1139 \| 0.0411 \| \| 56.2493 \| 48.0 \| 1200 \| 56.8569 \| 0.88 \| 0.2295 \| 1.1277 \| 0.88 \| 0.8693 \| 0.1238 \| 0.0381 \| \| 56.2493 \| 49.0 \| 1225 \| 56.8528 \| 0.875 \| 0.2292 \| 1.1966 \| 0.875 \| 0.8681 \| 0.1264 \| 0.0394 \| \| 56.2493 \| 50.0 \| 1250 \| 56.8462 \| 0.875 \| 0.2309 \| 1.1215 \| 0.875 \| 0.8690 \| 0.1178 \| 0.0424 \| \| 56.2493 \| 51.0 \| 1275 \| 56.8438 \| 0.87 \| 0.2285 \| 1.1259 \| 0.87 \| 0.8631 \| 0.1304 \| 0.0404 \| \| 56.2493 \| 52.0 \| 1300 \| 56.8660 \| 0.865 \| 0.2334 \| 1.1231 \| 0.865 \| 0.8623 \| 0.1343 \| 0.0416 \| \| 56.2493 \| 53.0 \| 1325 \| 56.8802 \| 0.885 \| 0.2273 \| 1.1220 \| 0.885 \| 0.8784 \| 0.1202 \| 0.0415 \| \| 56.2493 \| 54.0 \| 1350 \| 56.8581 \| 0.885 \| 0.2261 \| 1.1938 \| 0.885 \| 0.8772 \| 0.1286 \| 0.0408 \| \| 56.2493 \| 55.0 \| 1375 \| 56.8348 \| 0.875 \| 0.2301 \| 1.1953 \| 0.875 \| 0.8675 \| 0.1267 \| 0.0404 \| \| 56.2493 \| 56.0 \| 1400 \| 56.8489 \| 0.87 \| 0.2336 \| 1.1974 \| 0.87 \| 0.8589 \| 0.1079 \| 0.0393 \| \| 56.2493 \| 57.0 \| 1425 \| 56.8508 \| 0.87 \| 0.2307 \| 1.1928 \| 0.87 \| 0.8648 \| 0.1143 \| 0.0399 \| \| 56.2493 \| 58.0 \| 1450 \| 56.8172 \| 0.875 \| 0.2198 \| 1.2030 \| 0.875 \| 0.8675 \| 0.1062 \| 0.0378 \| \| 56.2493 \| 59.0 \| 1475 \| 56.8662 \| 0.87 \| 0.2287 \| 1.1354 \| 0.87 \| 0.8659 \| 0.1198 \| 0.0410 \| \| 56.0552 \| 60.0 \| 1500 \| 56.8437 \| 0.88 \| 0.2241 \| 1.1968 \| 0.88 \| 0.8735 \| 0.1127 \| 0.0407 \| \| 56.0552 \| 61.0 \| 1525 \| 56.8468 \| 0.87 \| 0.2303 \| 1.1998 \| 0.87 \| 0.8648 \| 0.1170 \| 0.0428 \| \| 56.0552 \| 62.0 \| 1550 \| 56.8575 \| 0.865 \| 0.2299 \| 1.1940 \| 0.865 \| 0.8599 \| 0.1171 \| 0.0411 \| \| 56.0552 \| 63.0 \| 1575 \| 56.8451 \| 0.875 \| 0.2291 \| 1.1981 \| 0.875 \| 0.8697 \| 0.1225 \| 0.0395 \| \| 56.0552 \| 64.0 \| 1600 \| 56.8355 \| 0.875 \| 0.2255 \| 1.2009 \| 0.875 \| 0.8675 \| 0.1137 \| 0.0394 \| \| 56.0552 \| 65.0 \| 1625 \| 56.8490 \| 0.875 \| 0.2338 \| 1.2653 \| 0.875 \| 0.8697 \| 0.1259 \| 0.0413 \| \| 56.0552 \| 66.0 \| 1650 \| 56.8206 \| 0.87 \| 0.2271 \| 1.2860 \| 0.87 \| 0.8603 \| 0.1197 \| 0.0379 \| \| 56.0552 \| 67.0 \| 1675 \| 56.8592 \| 0.87 \| 0.2307 \| 1.1992 \| 0.87 \| 0.8636 \| 0.1210 \| 0.0408 \| \| 56.0552 \| 68.0 \| 1700 \| 56.8339 \| 0.865 \| 0.2290 \| 1.1968 \| 0.865 \| 0.8599 \| 0.1101 \| 0.0386 \| \| 56.0552 \| 69.0 \| 1725 \| 56.8533 \| 0.87 \| 0.2336 \| 1.2625 \| 0.87 \| 0.8636 \| 0.1154 \| 0.0406 \| \| 56.0552 \| 70.0 \| 1750 \| 56.8281 \| 0.87 \| 0.2328 \| 1.2012 \| 0.87 \| 0.8659 \| 0.1229 \| 0.0406 \| \| 56.0552 \| 71.0 \| 1775 \| 56.8431 \| 0.875 \| 0.2335 \| 1.2557 \| 0.875 \| 0.8697 \| 0.1318 \| 0.0398 \| \| 56.0552 \| 72.0 \| 1800 \| 56.8396 \| 0.865 \| 0.2321 \| 1.2659 \| 0.865 \| 0.8599 \| 0.1165 \| 0.0397 \| \| 56.0552 \| 73.0 \| 1825 \| 56.8229 \| 0.86 \| 0.2302 \| 1.2615 \| 0.8600 \| 0.8524 \| 0.1258 \| 0.0402 \| \| 56.0552 \| 74.0 \| 1850 \| 56.8445 \| 0.87 \| 0.2344 \| 1.2371 \| 0.87 \| 0.8659 \| 0.1202 \| 0.0393 \| \| 56.0552 \| 75.0 \| 1875 \| 56.8475 \| 0.865 \| 0.2341 \| 1.2660 \| 0.865 \| 0.8599 \| 0.1202 \| 0.0423 \| \| 56.0552 \| 76.0 \| 1900 \| 56.8338 \| 0.86 \| 0.2320 \| 1.2643 \| 0.8600 \| 0.8524 \| 0.1296 \| 0.0422 \| \| 56.0552 \| 77.0 \| 1925 \| 56.8481 \| 0.87 \| 0.2353 \| 1.2665 \| 0.87 \| 0.8659 \| 0.1266 \| 0.0426 \| \| 56.0552 \| 78.0 \| 1950 \| 56.8328 \| 0.865 \| 0.2323 \| 1.2584 \| 0.865 \| 0.8599 \| 0.1128 \| 0.0424 \| \| 56.0552 \| 79.0 \| 1975 \| 56.8382 \| 0.86 \| 0.2363 \| 1.2658 \| 0.8600 \| 0.8553 \| 0.1273 \| 0.0425 \| \| 55.9822 \| 80.0 \| 2000 \| 56.8260 \| 0.86 \| 0.2354 \| 1.2710 \| 0.8600 \| 0.8553 \| 0.1129 \| 0.0430 \| \| 55.9822 \| 81.0 \| 2025 \| 56.8474 \| 0.86 \| 0.2398 \| 1.2679 \| 0.8600 \| 0.8553 \| 0.1212 \| 0.0433 \| \| 55.9822 \| 82.0 \| 2050 \| 56.8105 \| 0.855 \| 0.2320 \| 1.2655 \| 0.855 \| 0.8478 \| 0.1269 \| 0.0423 \| \| 55.9822 \| 83.0 \| 2075 \| 56.8240 \| 0.86 \| 0.2347 \| 1.2651 \| 0.8600 \| 0.8524 \| 0.1312 \| 0.0425 \| \| 55.9822 \| 84.0 \| 2100 \| 56.8350 \| 0.86 \| 0.2353 \| 1.2690 \| 0.8600 \| 0.8553 \| 0.1225 \| 0.0435 \| \| 55.9822 \| 85.0 \| 2125 \| 56.8317 \| 0.855 \| 0.2371 \| 1.2674 \| 0.855 \| 0.8478 \| 0.1211 \| 0.0433 \| \| 55.9822 \| 86.0 \| 2150 \| 56.8270 \| 0.855 \| 0.2364 \| 1.2646 \| 0.855 \| 0.8478 \| 0.1270 \| 0.0433 \| \| 55.9822 \| 87.0 \| 2175 \| 56.8275 \| 0.855 \| 0.2359 \| 1.2660 \| 0.855 \| 0.8478 \| 0.1167 \| 0.0423 \| \| 55.9822 \| 88.0 \| 2200 \| 56.8426 \| 0.855 \| 0.2385 \| 1.2683 \| 0.855 \| 0.8478 \| 0.1239 \| 0.0428 \| \| 55.9822 \| 89.0 \| 2225 \| 56.8376 \| 0.855 \| 0.2368 \| 1.2676 \| 0.855 \| 0.8478 \| 0.1239 \| 0.0426 \| \| 55.9822 \| 90.0 \| 2250 \| 56.8358 \| 0.855 \| 0.2382 \| 1.2670 \| 0.855 \| 0.8451 \| 0.1213 \| 0.0435 \| \| 55.9822 \| 91.0 \| 2275 \| 56.8254 \| 0.86 \| 0.2374 \| 1.2687 \| 0.8600 \| 0.8536 \| 0.1308 \| 0.0432 \| \| 55.9822 \| 92.0 \| 2300 \| 56.8269 \| 0.855 \| 0.2359 \| 1.2684 \| 0.855 \| 0.8476 \| 0.1223 \| 0.0425 \| \| 55.9822 \| 93.0 \| 2325 \| 56.8324 \| 0.85 \| 0.2381 \| 1.2708 \| 0.85 \| 0.8414 \| 0.1224 \| 0.0432 \| \| 55.9822 \| 94.0 \| 2350 \| 56.8344 \| 0.85 \| 0.2384 \| 1.2682 \| 0.85 \| 0.8414 \| 0.1222 \| 0.0433 \| \| 55.9822 \| 95.0 \| 2375 \| 56.8344 \| 0.85 \| 0.2387 \| 1.2708 \| 0.85 \| 0.8414 \| 0.1228 \| 0.0434 \| \| 55.9822 \| 96.0 \| 2400 \| 56.8342 \| 0.85 \| 0.2389 \| 1.2687 \| 0.85 \| 0.8414 \| 0.1222 \| 0.0432 \| \| 55.9822 \| 97.0 \| 2425 \| 56.8339 \| 0.85 \| 0.2387 \| 1.2713 \| 0.85 \| 0.8414 \| 0.1222 \| 0.0433 \| \| 55.9822 \| 98.0 \| 2450 \| 56.8342 \| 0.85 \| 0.2393 \| 1.2708 \| 0.85 \| 0.8414 \| 0.1228 \| 0.0437 \| \| 55.9822 \| 99.0 \| 2475 \| 56.8325 \| 0.85 \| 0.2390 \| 1.2703 \| 0.85 \| 0.8414 \| 0.1228 \| 0.0436 \| \| 55.9517 \| 100.0 \| 2500 \| 56.8340 \| 0.85 \| 0.2391 \| 1.2700 \| 0.85 \| 0.8414 \| 0.1228 \| 0.0436 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/81-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 81-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4383 - Accuracy: 0.815 - Brier Loss: 0.3137 - Nll: 0.9180 - F1 Micro: 0.815 - F1 Macro: 0.7935 - Ece: 0.2739 - Aurc: 0.0546 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.1414 \| 0.1 \| 1.0055 \| 8.0967 \| 0.1000 \| 0.0761 \| 0.3253 \| 0.8939 \| \| No log \| 2.0 \| 14 \| 1.4539 \| 0.215 \| 0.8614 \| 5.0189 \| 0.2150 \| 0.1812 \| 0.2795 \| 0.7550 \| \| No log \| 3.0 \| 21 \| 1.1486 \| 0.39 \| 0.7467 \| 4.3499 \| 0.39 \| 0.3077 \| 0.2755 \| 0.4066 \| \| No log \| 4.0 \| 28 \| 0.9182 \| 0.575 \| 0.5990 \| 2.9424 \| 0.575 \| 0.4619 \| 0.2988 \| 0.2367 \| \| No log \| 5.0 \| 35 \| 0.8134 \| 0.645 \| 0.5398 \| 2.1261 \| 0.645 \| 0.5549 \| 0.2689 \| 0.1878 \| \| No log \| 6.0 \| 42 \| 0.7149 \| 0.7 \| 0.4612 \| 2.2189 \| 0.7 \| 0.6006 \| 0.2518 \| 0.1316 \| \| No log \| 7.0 \| 49 \| 0.6607 \| 0.73 \| 0.4303 \| 1.7076 \| 0.7300 \| 0.6689 \| 0.2976 \| 0.1200 \| \| No log \| 8.0 \| 56 \| 0.6109 \| 0.755 \| 0.3922 \| 1.7703 \| 0.755 \| 0.6743 \| 0.2638 \| 0.0861 \| \| No log \| 9.0 \| 63 \| 0.6119 \| 0.715 \| 0.3943 \| 1.4194 \| 0.715 \| 0.6534 \| 0.2276 \| 0.0840 \| \| No log \| 10.0 \| 70 \| 0.5462 \| 0.8 \| 0.3535 \| 1.4026 \| 0.8000 \| 0.7760 \| 0.2888 \| 0.0660 \| \| No log \| 11.0 \| 77 \| 0.5376 \| 0.785 \| 0.3481 \| 1.2329 \| 0.785 \| 0.7600 \| 0.2660 \| 0.0757 \| \| No log \| 12.0 \| 84 \| 0.5250 \| 0.785 \| 0.3442 \| 1.1226 \| 0.785 \| 0.7669 \| 0.2476 \| 0.0802 \| \| No log \| 13.0 \| 91 \| 0.5053 \| 0.81 \| 0.3313 \| 1.1038 \| 0.81 \| 0.7824 \| 0.2543 \| 0.0628 \| \| No log \| 14.0 \| 98 \| 0.5188 \| 0.79 \| 0.3497 \| 1.0872 \| 0.79 \| 0.7678 \| 0.2495 \| 0.0759 \| \| No log \| 15.0 \| 105 \| 0.5020 \| 0.805 \| 0.3412 \| 1.4342 \| 0.805 \| 0.7868 \| 0.2669 \| 0.0652 \| \| No log \| 16.0 \| 112 \| 0.5221 \| 0.795 \| 0.3496 \| 1.3609 \| 0.795 \| 0.7473 \| 0.2682 \| 0.0621 \| \| No log \| 17.0 \| 119 \| 0.5046 \| 0.8 \| 0.3372 \| 1.1543 \| 0.8000 \| 0.7766 \| 0.2604 \| 0.0689 \| \| No log \| 18.0 \| 126 \| 0.4733 \| 0.805 \| 0.3248 \| 1.1335 \| 0.805 \| 0.7800 \| 0.2599 \| 0.0582 \| \| No log \| 19.0 \| 133 \| 0.4725 \| 0.815 \| 0.3242 \| 1.1607 \| 0.815 \| 0.7855 \| 0.2733 \| 0.0573 \| \| No log \| 20.0 \| 140 \| 0.4887 \| 0.82 \| 0.3325 \| 1.0316 \| 0.82 \| 0.7998 \| 0.2684 \| 0.0568 \| \| No log \| 21.0 \| 147 \| 0.4708 \| 0.815 \| 0.3205 \| 1.1275 \| 0.815 \| 0.8033 \| 0.2412 \| 0.0663 \| \| No log \| 22.0 \| 154 \| 0.4773 \| 0.83 \| 0.3309 \| 1.1147 \| 0.83 \| 0.8101 \| 0.2739 \| 0.0518 \| \| No log \| 23.0 \| 161 \| 0.4957 \| 0.815 \| 0.3402 \| 1.0884 \| 0.815 \| 0.8012 \| 0.2582 \| 0.0726 \| \| No log \| 24.0 \| 168 \| 0.4666 \| 0.805 \| 0.3305 \| 1.0784 \| 0.805 \| 0.7858 \| 0.2792 \| 0.0560 \| \| No log \| 25.0 \| 175 \| 0.4830 \| 0.795 \| 0.3324 \| 1.1757 \| 0.795 \| 0.7595 \| 0.2505 \| 0.0715 \| \| No log \| 26.0 \| 182 \| 0.4622 \| 0.8 \| 0.3272 \| 1.0698 \| 0.8000 \| 0.7873 \| 0.2795 \| 0.0590 \| \| No log \| 27.0 \| 189 \| 0.4604 \| 0.8 \| 0.3200 \| 1.1104 \| 0.8000 \| 0.7717 \| 0.2561 \| 0.0630 \| \| No log \| 28.0 \| 196 \| 0.4635 \| 0.82 \| 0.3253 \| 1.1271 \| 0.82 \| 0.7903 \| 0.2756 \| 0.0571 \| \| No log \| 29.0 \| 203 \| 0.4590 \| 0.815 \| 0.3211 \| 1.1048 \| 0.815 \| 0.7952 \| 0.2881 \| 0.0567 \| \| No log \| 30.0 \| 210 \| 0.4575 \| 0.795 \| 0.3210 \| 0.9174 \| 0.795 \| 0.7786 \| 0.2833 \| 0.0625 \| \| No log \| 31.0 \| 217 \| 0.4684 \| 0.83 \| 0.3337 \| 0.9485 \| 0.83 \| 0.8093 \| 0.2892 \| 0.0557 \| \| No log \| 32.0 \| 224 \| 0.4520 \| 0.81 \| 0.3208 \| 1.0186 \| 0.81 \| 0.7955 \| 0.2438 \| 0.0577 \| \| No log \| 33.0 \| 231 \| 0.4567 \| 0.825 \| 0.3233 \| 0.9246 \| 0.825 \| 0.7928 \| 0.2665 \| 0.0592 \| \| No log \| 34.0 \| 238 \| 0.4468 \| 0.82 \| 0.3152 \| 1.0065 \| 0.82 \| 0.8000 \| 0.2710 \| 0.0563 \| \| No log \| 35.0 \| 245 \| 0.4562 \| 0.78 \| 0.3244 \| 1.0626 \| 0.78 \| 0.7614 \| 0.2602 \| 0.0624 \| \| No log \| 36.0 \| 252 \| 0.4542 \| 0.815 \| 0.3223 \| 1.1362 \| 0.815 \| 0.7852 \| 0.2584 \| 0.0579 \| \| No log \| 37.0 \| 259 \| 0.4441 \| 0.82 \| 0.3136 \| 1.0419 \| 0.82 \| 0.7901 \| 0.2790 \| 0.0529 \| \| No log \| 38.0 \| 266 \| 0.4408 \| 0.825 \| 0.3125 \| 0.9860 \| 0.825 \| 0.8023 \| 0.2766 \| 0.0553 \| \| No log \| 39.0 \| 273 \| 0.4354 \| 0.83 \| 0.3082 \| 0.8958 \| 0.83 \| 0.8116 \| 0.2713 \| 0.0504 \| \| No log \| 40.0 \| 280 \| 0.4465 \| 0.79 \| 0.3164 \| 1.1111 \| 0.79 \| 0.7715 \| 0.2668 \| 0.0628 \| \| No log \| 41.0 \| 287 \| 0.4416 \| 0.845 \| 0.3128 \| 1.0103 \| 0.845 \| 0.8162 \| 0.3044 \| 0.0527 \| \| No log \| 42.0 \| 294 \| 0.4463 \| 0.83 \| 0.3165 \| 1.0849 \| 0.83 \| 0.8106 \| 0.2683 \| 0.0580 \| \| No log \| 43.0 \| 301 \| 0.4405 \| 0.845 \| 0.3132 \| 1.0312 \| 0.845 \| 0.8247 \| 0.2792 \| 0.0509 \| \| No log \| 44.0 \| 308 \| 0.4443 \| 0.83 \| 0.3174 \| 0.9196 \| 0.83 \| 0.8094 \| 0.2687 \| 0.0524 \| \| No log \| 45.0 \| 315 \| 0.4445 \| 0.82 \| 0.3194 \| 1.0665 \| 0.82 \| 0.7897 \| 0.2651 \| 0.0560 \| \| No log \| 46.0 \| 322 \| 0.4405 \| 0.81 \| 0.3133 \| 1.1805 \| 0.81 \| 0.7770 \| 0.2771 \| 0.0550 \| \| No log \| 47.0 \| 329 \| 0.4380 \| 0.84 \| 0.3132 \| 0.9508 \| 0.8400 \| 0.8104 \| 0.2916 \| 0.0535 \| \| No log \| 48.0 \| 336 \| 0.4407 \| 0.825 \| 0.3139 \| 0.9044 \| 0.825 \| 0.7978 \| 0.2702 \| 0.0542 \| \| No log \| 49.0 \| 343 \| 0.4418 \| 0.835 \| 0.3154 \| 0.8965 \| 0.835 \| 0.8178 \| 0.2877 \| 0.0569 \| \| No log \| 50.0 \| 350 \| 0.4368 \| 0.825 \| 0.3123 \| 0.9774 \| 0.825 \| 0.8073 \| 0.2607 \| 0.0531 \| \| No log \| 51.0 \| 357 \| 0.4402 \| 0.825 \| 0.3140 \| 0.9170 \| 0.825 \| 0.8052 \| 0.2810 \| 0.0550 \| \| No log \| 52.0 \| 364 \| 0.4374 \| 0.82 \| 0.3107 \| 0.9873 \| 0.82 \| 0.7952 \| 0.2602 \| 0.0542 \| \| No log \| 53.0 \| 371 \| 0.4368 \| 0.83 \| 0.3120 \| 0.9832 \| 0.83 \| 0.8084 \| 0.2709 \| 0.0541 \| \| No log \| 54.0 \| 378 \| 0.4375 \| 0.82 \| 0.3131 \| 0.9094 \| 0.82 \| 0.7943 \| 0.2633 \| 0.0538 \| \| No log \| 55.0 \| 385 \| 0.4379 \| 0.815 \| 0.3134 \| 0.9927 \| 0.815 \| 0.7856 \| 0.2960 \| 0.0552 \| \| No log \| 56.0 \| 392 \| 0.4370 \| 0.83 \| 0.3125 \| 0.9746 \| 0.83 \| 0.8100 \| 0.2744 \| 0.0535 \| \| No log \| 57.0 \| 399 \| 0.4366 \| 0.825 \| 0.3123 \| 1.0392 \| 0.825 \| 0.8021 \| 0.2730 \| 0.0536 \| \| No log \| 58.0 \| 406 \| 0.4372 \| 0.825 \| 0.3129 \| 0.9174 \| 0.825 \| 0.8026 \| 0.2800 \| 0.0542 \| \| No log \| 59.0 \| 413 \| 0.4380 \| 0.81 \| 0.3134 \| 0.9770 \| 0.81 \| 0.7831 \| 0.2612 \| 0.0557 \| \| No log \| 60.0 \| 420 \| 0.4374 \| 0.82 \| 0.3130 \| 0.9124 \| 0.82 \| 0.7961 \| 0.2589 \| 0.0541 \| \| No log \| 61.0 \| 427 \| 0.4366 \| 0.825 \| 0.3121 \| 0.9038 \| 0.825 \| 0.8061 \| 0.2641 \| 0.0538 \| \| No log \| 62.0 \| 434 \| 0.4372 \| 0.825 \| 0.3126 \| 0.9105 \| 0.825 \| 0.8042 \| 0.2684 \| 0.0547 \| \| No log \| 63.0 \| 441 \| 0.4381 \| 0.82 \| 0.3135 \| 0.9160 \| 0.82 \| 0.7961 \| 0.2810 \| 0.0545 \| \| No log \| 64.0 \| 448 \| 0.4376 \| 0.83 \| 0.3133 \| 0.9134 \| 0.83 \| 0.8100 \| 0.2757 \| 0.0539 \| \| No log \| 65.0 \| 455 \| 0.4376 \| 0.825 \| 0.3130 \| 0.9133 \| 0.825 \| 0.8061 \| 0.2977 \| 0.0541 \| \| No log \| 66.0 \| 462 \| 0.4378 \| 0.825 \| 0.3133 \| 0.9153 \| 0.825 \| 0.8061 \| 0.2767 \| 0.0543 \| \| No log \| 67.0 \| 469 \| 0.4373 \| 0.825 \| 0.3129 \| 0.9139 \| 0.825 \| 0.8042 \| 0.2905 \| 0.0541 \| \| No log \| 68.0 \| 476 \| 0.4375 \| 0.82 \| 0.3129 \| 0.9128 \| 0.82 \| 0.7961 \| 0.2739 \| 0.0543 \| \| No log \| 69.0 \| 483 \| 0.4376 \| 0.82 \| 0.3131 \| 0.9125 \| 0.82 \| 0.7961 \| 0.2757 \| 0.0542 \| \| No log \| 70.0 \| 490 \| 0.4377 \| 0.825 \| 0.3133 \| 0.9174 \| 0.825 \| 0.8061 \| 0.2924 \| 0.0538 \| \| No log \| 71.0 \| 497 \| 0.4380 \| 0.82 \| 0.3134 \| 0.9179 \| 0.82 \| 0.7961 \| 0.2896 \| 0.0541 \| \| 0.2684 \| 72.0 \| 504 \| 0.4378 \| 0.82 \| 0.3133 \| 0.9148 \| 0.82 \| 0.8035 \| 0.2912 \| 0.0543 \| \| 0.2684 \| 73.0 \| 511 \| 0.4375 \| 0.82 \| 0.3131 \| 0.9169 \| 0.82 \| 0.7961 \| 0.2731 \| 0.0542 \| \| 0.2684 \| 74.0 \| 518 \| 0.4379 \| 0.82 \| 0.3133 \| 0.9177 \| 0.82 \| 0.7961 \| 0.2732 \| 0.0540 \| \| 0.2684 \| 75.0 \| 525 \| 0.4383 \| 0.82 \| 0.3138 \| 0.9194 \| 0.82 \| 0.8035 \| 0.2835 \| 0.0545 \| \| 0.2684 \| 76.0 \| 532 \| 0.4378 \| 0.815 \| 0.3133 \| 0.9133 \| 0.815 \| 0.7935 \| 0.2824 \| 0.0543 \| \| 0.2684 \| 77.0 \| 539 \| 0.4378 \| 0.815 \| 0.3133 \| 0.9146 \| 0.815 \| 0.7935 \| 0.2735 \| 0.0542 \| \| 0.2684 \| 78.0 \| 546 \| 0.4379 \| 0.815 \| 0.3134 \| 0.9139 \| 0.815 \| 0.7935 \| 0.2828 \| 0.0547 \| \| 0.2684 \| 79.0 \| 553 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9179 \| 0.815 \| 0.7935 \| 0.2817 \| 0.0547 \| \| 0.2684 \| 80.0 \| 560 \| 0.4380 \| 0.815 \| 0.3134 \| 0.9168 \| 0.815 \| 0.7935 \| 0.2818 \| 0.0545 \| \| 0.2684 \| 81.0 \| 567 \| 0.4381 \| 0.815 \| 0.3135 \| 0.9183 \| 0.815 \| 0.7935 \| 0.2736 \| 0.0544 \| \| 0.2684 \| 82.0 \| 574 \| 0.4379 \| 0.815 \| 0.3134 \| 0.9164 \| 0.815 \| 0.7935 \| 0.2736 \| 0.0544 \| \| 0.2684 \| 83.0 \| 581 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9168 \| 0.815 \| 0.7935 \| 0.2736 \| 0.0541 \| \| 0.2684 \| 84.0 \| 588 \| 0.4381 \| 0.815 \| 0.3136 \| 0.9199 \| 0.815 \| 0.7935 \| 0.2737 \| 0.0541 \| \| 0.2684 \| 85.0 \| 595 \| 0.4380 \| 0.815 \| 0.3134 \| 0.9175 \| 0.815 \| 0.7935 \| 0.2735 \| 0.0543 \| \| 0.2684 \| 86.0 \| 602 \| 0.4383 \| 0.815 \| 0.3137 \| 0.9197 \| 0.815 \| 0.7935 \| 0.2674 \| 0.0545 \| \| 0.2684 \| 87.0 \| 609 \| 0.4381 \| 0.815 \| 0.3135 \| 0.9176 \| 0.815 \| 0.7935 \| 0.2738 \| 0.0547 \| \| 0.2684 \| 88.0 \| 616 \| 0.4381 \| 0.815 \| 0.3135 \| 0.9179 \| 0.815 \| 0.7935 \| 0.2736 \| 0.0541 \| \| 0.2684 \| 89.0 \| 623 \| 0.4381 \| 0.815 \| 0.3135 \| 0.9177 \| 0.815 \| 0.7935 \| 0.2737 \| 0.0543 \| \| 0.2684 \| 90.0 \| 630 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9173 \| 0.815 \| 0.7935 \| 0.2736 \| 0.0544 \| \| 0.2684 \| 91.0 \| 637 \| 0.4383 \| 0.815 \| 0.3136 \| 0.9187 \| 0.815 \| 0.7935 \| 0.2738 \| 0.0546 \| \| 0.2684 \| 92.0 \| 644 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9171 \| 0.815 \| 0.7935 \| 0.2738 \| 0.0544 \| \| 0.2684 \| 93.0 \| 651 \| 0.4383 \| 0.815 \| 0.3137 \| 0.9190 \| 0.815 \| 0.7935 \| 0.2738 \| 0.0546 \| \| 0.2684 \| 94.0 \| 658 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9187 \| 0.815 \| 0.7935 \| 0.2737 \| 0.0543 \| \| 0.2684 \| 95.0 \| 665 \| 0.4383 \| 0.815 \| 0.3136 \| 0.9184 \| 0.815 \| 0.7935 \| 0.2739 \| 0.0545 \| \| 0.2684 \| 96.0 \| 672 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9184 \| 0.815 \| 0.7935 \| 0.2737 \| 0.0545 \| \| 0.2684 \| 97.0 \| 679 \| 0.4382 \| 0.815 \| 0.3136 \| 0.9179 \| 0.815 \| 0.7935 \| 0.2739 \| 0.0545 \| \| 0.2684 \| 98.0 \| 686 \| 0.4383 \| 0.815 \| 0.3137 \| 0.9185 \| 0.815 \| 0.7935 \| 0.2738 \| 0.0544 \| \| 0.2684 \| 99.0 \| 693 \| 0.4383 \| 0.815 \| 0.3137 \| 0.9182 \| 0.815 \| 0.7935 \| 0.2739 \| 0.0546 \| \| 0.2684 \| 100.0 \| 700 \| 0.4383 \| 0.815 \| 0.3137 \| 0.9180 \| 0.815 \| 0.7935 \| 0.2739 \| 0.0546 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/81-tiny_tobacco3482	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 81-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1635 - Accuracy: 0.815 - Brier Loss: 0.3719 - Nll: 0.7360 - F1 Micro: 0.815 - F1 Macro: 0.7973 - Ece: 0.3345 - Aurc: 0.0542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.2942 \| 0.16 \| 1.0273 \| 8.4554 \| 0.16 \| 0.0543 \| 0.3538 \| 0.8479 \| \| No log \| 2.0 \| 14 \| 1.1952 \| 0.095 \| 0.9063 \| 7.1126 \| 0.095 \| 0.0819 \| 0.2327 \| 0.8615 \| \| No log \| 3.0 \| 21 \| 0.7862 \| 0.305 \| 0.8434 \| 5.1628 \| 0.305 \| 0.1452 \| 0.2965 \| 0.6092 \| \| No log \| 4.0 \| 28 \| 0.6144 \| 0.385 \| 0.7594 \| 3.3452 \| 0.3850 \| 0.2942 \| 0.2978 \| 0.4140 \| \| No log \| 5.0 \| 35 \| 0.5040 \| 0.54 \| 0.6858 \| 2.6433 \| 0.54 \| 0.4419 \| 0.3200 \| 0.2853 \| \| No log \| 6.0 \| 42 \| 0.4291 \| 0.625 \| 0.6353 \| 2.2564 \| 0.625 \| 0.5273 \| 0.3772 \| 0.1975 \| \| No log \| 7.0 \| 49 \| 0.3839 \| 0.63 \| 0.5815 \| 2.0215 \| 0.63 \| 0.4970 \| 0.3469 \| 0.1795 \| \| No log \| 8.0 \| 56 \| 0.3390 \| 0.705 \| 0.5512 \| 1.5239 \| 0.705 \| 0.5983 \| 0.3704 \| 0.1337 \| \| No log \| 9.0 \| 63 \| 0.3128 \| 0.705 \| 0.5015 \| 1.7837 \| 0.705 \| 0.6172 \| 0.3403 \| 0.1138 \| \| No log \| 10.0 \| 70 \| 0.2868 \| 0.705 \| 0.4532 \| 1.1243 \| 0.705 \| 0.6108 \| 0.3221 \| 0.1015 \| \| No log \| 11.0 \| 77 \| 0.2866 \| 0.74 \| 0.4532 \| 1.0967 \| 0.74 \| 0.6909 \| 0.3422 \| 0.0932 \| \| No log \| 12.0 \| 84 \| 0.2625 \| 0.765 \| 0.4235 \| 1.4646 \| 0.765 \| 0.6788 \| 0.3240 \| 0.0805 \| \| No log \| 13.0 \| 91 \| 0.2516 \| 0.73 \| 0.4141 \| 1.4855 \| 0.7300 \| 0.6495 \| 0.2770 \| 0.0869 \| \| No log \| 14.0 \| 98 \| 0.2487 \| 0.805 \| 0.4440 \| 1.2873 \| 0.805 \| 0.7319 \| 0.3699 \| 0.0670 \| \| No log \| 15.0 \| 105 \| 0.2103 \| 0.77 \| 0.4029 \| 1.0834 \| 0.7700 \| 0.6891 \| 0.3007 \| 0.0677 \| \| No log \| 16.0 \| 112 \| 0.2216 \| 0.8 \| 0.4029 \| 1.0621 \| 0.8000 \| 0.7247 \| 0.3513 \| 0.0611 \| \| No log \| 17.0 \| 119 \| 0.2230 \| 0.745 \| 0.3999 \| 1.0367 \| 0.745 \| 0.6710 \| 0.2992 \| 0.0809 \| \| No log \| 18.0 \| 126 \| 0.2301 \| 0.805 \| 0.4044 \| 0.9650 \| 0.805 \| 0.7473 \| 0.3406 \| 0.0538 \| \| No log \| 19.0 \| 133 \| 0.2024 \| 0.795 \| 0.3935 \| 0.9429 \| 0.795 \| 0.7562 \| 0.3327 \| 0.0669 \| \| No log \| 20.0 \| 140 \| 0.1959 \| 0.82 \| 0.3935 \| 0.8984 \| 0.82 \| 0.8000 \| 0.3453 \| 0.0571 \| \| No log \| 21.0 \| 147 \| 0.2020 \| 0.815 \| 0.3946 \| 0.8507 \| 0.815 \| 0.7949 \| 0.3669 \| 0.0588 \| \| No log \| 22.0 \| 154 \| 0.2100 \| 0.805 \| 0.3758 \| 0.8083 \| 0.805 \| 0.7743 \| 0.3087 \| 0.0625 \| \| No log \| 23.0 \| 161 \| 0.2002 \| 0.81 \| 0.3881 \| 1.0046 \| 0.81 \| 0.7953 \| 0.3276 \| 0.0617 \| \| No log \| 24.0 \| 168 \| 0.2004 \| 0.83 \| 0.3987 \| 0.8922 \| 0.83 \| 0.8034 \| 0.3565 \| 0.0531 \| \| No log \| 25.0 \| 175 \| 0.1914 \| 0.785 \| 0.3699 \| 0.8571 \| 0.785 \| 0.7484 \| 0.3117 \| 0.0601 \| \| No log \| 26.0 \| 182 \| 0.1845 \| 0.815 \| 0.3764 \| 0.8153 \| 0.815 \| 0.7970 \| 0.3215 \| 0.0539 \| \| No log \| 27.0 \| 189 \| 0.1821 \| 0.835 \| 0.3815 \| 0.8488 \| 0.835 \| 0.8175 \| 0.3441 \| 0.0497 \| \| No log \| 28.0 \| 196 \| 0.1869 \| 0.84 \| 0.3808 \| 0.8654 \| 0.8400 \| 0.8236 \| 0.3639 \| 0.0504 \| \| No log \| 29.0 \| 203 \| 0.1859 \| 0.79 \| 0.3752 \| 0.7067 \| 0.79 \| 0.7661 \| 0.3019 \| 0.0618 \| \| No log \| 30.0 \| 210 \| 0.1842 \| 0.795 \| 0.3826 \| 0.9031 \| 0.795 \| 0.7646 \| 0.3170 \| 0.0599 \| \| No log \| 31.0 \| 217 \| 0.1797 \| 0.815 \| 0.3714 \| 0.8572 \| 0.815 \| 0.8038 \| 0.3214 \| 0.0588 \| \| No log \| 32.0 \| 224 \| 0.1754 \| 0.805 \| 0.3679 \| 0.7412 \| 0.805 \| 0.7883 \| 0.3063 \| 0.0563 \| \| No log \| 33.0 \| 231 \| 0.1790 \| 0.835 \| 0.3736 \| 0.8357 \| 0.835 \| 0.8128 \| 0.3431 \| 0.0497 \| \| No log \| 34.0 \| 238 \| 0.1761 \| 0.81 \| 0.3744 \| 0.7360 \| 0.81 \| 0.7907 \| 0.3292 \| 0.0526 \| \| No log \| 35.0 \| 245 \| 0.1731 \| 0.83 \| 0.3744 \| 0.7494 \| 0.83 \| 0.8202 \| 0.3351 \| 0.0540 \| \| No log \| 36.0 \| 252 \| 0.1758 \| 0.79 \| 0.3738 \| 0.9441 \| 0.79 \| 0.7710 \| 0.3099 \| 0.0625 \| \| No log \| 37.0 \| 259 \| 0.1730 \| 0.81 \| 0.3785 \| 0.9418 \| 0.81 \| 0.7939 \| 0.3425 \| 0.0577 \| \| No log \| 38.0 \| 266 \| 0.1727 \| 0.815 \| 0.3752 \| 0.7539 \| 0.815 \| 0.8001 \| 0.3290 \| 0.0540 \| \| No log \| 39.0 \| 273 \| 0.1769 \| 0.81 \| 0.3754 \| 0.9013 \| 0.81 \| 0.7888 \| 0.3296 \| 0.0563 \| \| No log \| 40.0 \| 280 \| 0.1770 \| 0.805 \| 0.3809 \| 0.7637 \| 0.805 \| 0.7809 \| 0.3291 \| 0.0519 \| \| No log \| 41.0 \| 287 \| 0.1771 \| 0.815 \| 0.3814 \| 0.7495 \| 0.815 \| 0.7993 \| 0.3352 \| 0.0565 \| \| No log \| 42.0 \| 294 \| 0.1745 \| 0.8 \| 0.3807 \| 0.8386 \| 0.8000 \| 0.7865 \| 0.3212 \| 0.0601 \| \| No log \| 43.0 \| 301 \| 0.1700 \| 0.825 \| 0.3762 \| 0.8216 \| 0.825 \| 0.8067 \| 0.3420 \| 0.0517 \| \| No log \| 44.0 \| 308 \| 0.1686 \| 0.825 \| 0.3706 \| 0.7310 \| 0.825 \| 0.8024 \| 0.3448 \| 0.0509 \| \| No log \| 45.0 \| 315 \| 0.1692 \| 0.81 \| 0.3739 \| 0.6805 \| 0.81 \| 0.7914 \| 0.3397 \| 0.0508 \| \| No log \| 46.0 \| 322 \| 0.1697 \| 0.825 \| 0.3709 \| 0.7482 \| 0.825 \| 0.7995 \| 0.3364 \| 0.0531 \| \| No log \| 47.0 \| 329 \| 0.1681 \| 0.805 \| 0.3735 \| 0.8810 \| 0.805 \| 0.7872 \| 0.3388 \| 0.0585 \| \| No log \| 48.0 \| 336 \| 0.1667 \| 0.815 \| 0.3731 \| 0.6788 \| 0.815 \| 0.7968 \| 0.3427 \| 0.0567 \| \| No log \| 49.0 \| 343 \| 0.1690 \| 0.82 \| 0.3786 \| 0.8693 \| 0.82 \| 0.7967 \| 0.3507 \| 0.0502 \| \| No log \| 50.0 \| 350 \| 0.1668 \| 0.82 \| 0.3761 \| 0.7854 \| 0.82 \| 0.8032 \| 0.3509 \| 0.0526 \| \| No log \| 51.0 \| 357 \| 0.1659 \| 0.82 \| 0.3760 \| 0.7437 \| 0.82 \| 0.8045 \| 0.3374 \| 0.0557 \| \| No log \| 52.0 \| 364 \| 0.1663 \| 0.805 \| 0.3682 \| 0.7932 \| 0.805 \| 0.7863 \| 0.3171 \| 0.0560 \| \| No log \| 53.0 \| 371 \| 0.1649 \| 0.805 \| 0.3649 \| 0.7249 \| 0.805 \| 0.7863 \| 0.3139 \| 0.0562 \| \| No log \| 54.0 \| 378 \| 0.1654 \| 0.815 \| 0.3725 \| 0.7390 \| 0.815 \| 0.7935 \| 0.3273 \| 0.0560 \| \| No log \| 55.0 \| 385 \| 0.1667 \| 0.84 \| 0.3741 \| 0.7324 \| 0.8400 \| 0.8279 \| 0.3501 \| 0.0513 \| \| No log \| 56.0 \| 392 \| 0.1659 \| 0.825 \| 0.3718 \| 0.7278 \| 0.825 \| 0.8058 \| 0.3288 \| 0.0522 \| \| No log \| 57.0 \| 399 \| 0.1659 \| 0.825 \| 0.3731 \| 0.7351 \| 0.825 \| 0.8084 \| 0.3204 \| 0.0537 \| \| No log \| 58.0 \| 406 \| 0.1645 \| 0.825 \| 0.3730 \| 0.7356 \| 0.825 \| 0.8068 \| 0.3468 \| 0.0524 \| \| No log \| 59.0 \| 413 \| 0.1626 \| 0.825 \| 0.3715 \| 0.7282 \| 0.825 \| 0.8040 \| 0.3391 \| 0.0527 \| \| No log \| 60.0 \| 420 \| 0.1628 \| 0.825 \| 0.3698 \| 0.7297 \| 0.825 \| 0.8056 \| 0.3212 \| 0.0534 \| \| No log \| 61.0 \| 427 \| 0.1630 \| 0.825 \| 0.3719 \| 0.7312 \| 0.825 \| 0.8065 \| 0.3260 \| 0.0533 \| \| No log \| 62.0 \| 434 \| 0.1642 \| 0.82 \| 0.3714 \| 0.7335 \| 0.82 \| 0.8040 \| 0.3388 \| 0.0549 \| \| No log \| 63.0 \| 441 \| 0.1634 \| 0.82 \| 0.3733 \| 0.7349 \| 0.82 \| 0.8011 \| 0.3399 \| 0.0553 \| \| No log \| 64.0 \| 448 \| 0.1628 \| 0.815 \| 0.3710 \| 0.7301 \| 0.815 \| 0.7974 \| 0.3249 \| 0.0532 \| \| No log \| 65.0 \| 455 \| 0.1629 \| 0.815 \| 0.3704 \| 0.7362 \| 0.815 \| 0.7981 \| 0.3362 \| 0.0541 \| \| No log \| 66.0 \| 462 \| 0.1630 \| 0.82 \| 0.3727 \| 0.7354 \| 0.82 \| 0.8024 \| 0.3559 \| 0.0526 \| \| No log \| 67.0 \| 469 \| 0.1633 \| 0.825 \| 0.3733 \| 0.7355 \| 0.825 \| 0.8038 \| 0.3492 \| 0.0526 \| \| No log \| 68.0 \| 476 \| 0.1638 \| 0.82 \| 0.3724 \| 0.7369 \| 0.82 \| 0.8040 \| 0.3468 \| 0.0550 \| \| No log \| 69.0 \| 483 \| 0.1629 \| 0.82 \| 0.3717 \| 0.7329 \| 0.82 \| 0.8040 \| 0.3251 \| 0.0536 \| \| No log \| 70.0 \| 490 \| 0.1629 \| 0.815 \| 0.3709 \| 0.7312 \| 0.815 \| 0.7947 \| 0.3221 \| 0.0541 \| \| No log \| 71.0 \| 497 \| 0.1630 \| 0.815 \| 0.3718 \| 0.7371 \| 0.815 \| 0.7973 \| 0.3436 \| 0.0545 \| \| 0.1743 \| 72.0 \| 504 \| 0.1631 \| 0.815 \| 0.3712 \| 0.7350 \| 0.815 \| 0.7973 \| 0.3264 \| 0.0536 \| \| 0.1743 \| 73.0 \| 511 \| 0.1634 \| 0.815 \| 0.3721 \| 0.7348 \| 0.815 \| 0.7981 \| 0.3340 \| 0.0543 \| \| 0.1743 \| 74.0 \| 518 \| 0.1631 \| 0.815 \| 0.3716 \| 0.7332 \| 0.815 \| 0.7973 \| 0.3279 \| 0.0541 \| \| 0.1743 \| 75.0 \| 525 \| 0.1633 \| 0.82 \| 0.3720 \| 0.7346 \| 0.82 \| 0.8008 \| 0.3186 \| 0.0542 \| \| 0.1743 \| 76.0 \| 532 \| 0.1631 \| 0.815 \| 0.3716 \| 0.7342 \| 0.815 \| 0.7973 \| 0.3189 \| 0.0542 \| \| 0.1743 \| 77.0 \| 539 \| 0.1633 \| 0.815 \| 0.3718 \| 0.7358 \| 0.815 \| 0.7973 \| 0.3344 \| 0.0542 \| \| 0.1743 \| 78.0 \| 546 \| 0.1633 \| 0.815 \| 0.3718 \| 0.7363 \| 0.815 \| 0.7991 \| 0.3342 \| 0.0542 \| \| 0.1743 \| 79.0 \| 553 \| 0.1633 \| 0.815 \| 0.3718 \| 0.7350 \| 0.815 \| 0.7973 \| 0.3344 \| 0.0543 \| \| 0.1743 \| 80.0 \| 560 \| 0.1634 \| 0.815 \| 0.3718 \| 0.7354 \| 0.815 \| 0.7973 \| 0.3136 \| 0.0542 \| \| 0.1743 \| 81.0 \| 567 \| 0.1633 \| 0.815 \| 0.3721 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3279 \| 0.0543 \| \| 0.1743 \| 82.0 \| 574 \| 0.1634 \| 0.815 \| 0.3719 \| 0.7358 \| 0.815 \| 0.7973 \| 0.3276 \| 0.0542 \| \| 0.1743 \| 83.0 \| 581 \| 0.1634 \| 0.815 \| 0.3720 \| 0.7358 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 84.0 \| 588 \| 0.1633 \| 0.815 \| 0.3717 \| 0.7352 \| 0.815 \| 0.7973 \| 0.3275 \| 0.0542 \| \| 0.1743 \| 85.0 \| 595 \| 0.1633 \| 0.815 \| 0.3720 \| 0.7357 \| 0.815 \| 0.7973 \| 0.3194 \| 0.0541 \| \| 0.1743 \| 86.0 \| 602 \| 0.1634 \| 0.815 \| 0.3717 \| 0.7361 \| 0.815 \| 0.7973 \| 0.3276 \| 0.0542 \| \| 0.1743 \| 87.0 \| 609 \| 0.1634 \| 0.815 \| 0.3719 \| 0.7359 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 88.0 \| 616 \| 0.1634 \| 0.815 \| 0.3720 \| 0.7359 \| 0.815 \| 0.7973 \| 0.3278 \| 0.0542 \| \| 0.1743 \| 89.0 \| 623 \| 0.1634 \| 0.815 \| 0.3718 \| 0.7359 \| 0.815 \| 0.7973 \| 0.3192 \| 0.0543 \| \| 0.1743 \| 90.0 \| 630 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7359 \| 0.815 \| 0.7973 \| 0.3278 \| 0.0543 \| \| 0.1743 \| 91.0 \| 637 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7357 \| 0.815 \| 0.7973 \| 0.3344 \| 0.0541 \| \| 0.1743 \| 92.0 \| 644 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7361 \| 0.815 \| 0.7973 \| 0.3278 \| 0.0542 \| \| 0.1743 \| 93.0 \| 651 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7357 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 94.0 \| 658 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7356 \| 0.815 \| 0.7973 \| 0.3261 \| 0.0543 \| \| 0.1743 \| 95.0 \| 665 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 96.0 \| 672 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7357 \| 0.815 \| 0.7973 \| 0.3278 \| 0.0542 \| \| 0.1743 \| 97.0 \| 679 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 98.0 \| 686 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3278 \| 0.0543 \| \| 0.1743 \| 99.0 \| 693 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| \| 0.1743 \| 100.0 \| 700 \| 0.1635 \| 0.815 \| 0.3719 \| 0.7360 \| 0.815 \| 0.7973 \| 0.3345 \| 0.0542 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/81-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 81-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0317 - Accuracy: 0.86 - Brier Loss: 0.2316 - Nll: 0.9681 - F1 Micro: 0.8600 - F1 Macro: 0.8444 - Ece: 0.1162 - Aurc: 0.0505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 6.0999 \| 0.095 \| 1.0191 \| 8.7249 \| 0.095 \| 0.0767 \| 0.3174 \| 0.8988 \| \| No log \| 2.0 \| 14 \| 5.0037 \| 0.145 \| 0.9023 \| 8.1615 \| 0.145 \| 0.1212 \| 0.2431 \| 0.8041 \| \| No log \| 3.0 \| 21 \| 4.5740 \| 0.265 \| 0.8363 \| 5.9714 \| 0.265 \| 0.2003 \| 0.2732 \| 0.5711 \| \| No log \| 4.0 \| 28 \| 4.3460 \| 0.435 \| 0.7292 \| 3.9720 \| 0.435 \| 0.3780 \| 0.3029 \| 0.3535 \| \| No log \| 5.0 \| 35 \| 4.1953 \| 0.565 \| 0.6344 \| 2.7772 \| 0.565 \| 0.4465 \| 0.3056 \| 0.2433 \| \| No log \| 6.0 \| 42 \| 4.0769 \| 0.665 \| 0.5852 \| 2.3975 \| 0.665 \| 0.5345 \| 0.3469 \| 0.1662 \| \| No log \| 7.0 \| 49 \| 3.9670 \| 0.685 \| 0.5103 \| 2.1879 \| 0.685 \| 0.5645 \| 0.3335 \| 0.1339 \| \| No log \| 8.0 \| 56 \| 3.9085 \| 0.74 \| 0.4625 \| 1.7988 \| 0.74 \| 0.6444 \| 0.3276 \| 0.1028 \| \| No log \| 9.0 \| 63 \| 3.8671 \| 0.765 \| 0.4073 \| 1.5650 \| 0.765 \| 0.6567 \| 0.2800 \| 0.0878 \| \| No log \| 10.0 \| 70 \| 3.8008 \| 0.77 \| 0.3617 \| 1.4468 \| 0.7700 \| 0.6730 \| 0.2443 \| 0.0692 \| \| No log \| 11.0 \| 77 \| 3.8924 \| 0.76 \| 0.3685 \| 1.4401 \| 0.76 \| 0.7045 \| 0.2129 \| 0.0859 \| \| No log \| 12.0 \| 84 \| 3.8523 \| 0.78 \| 0.3275 \| 1.3333 \| 0.78 \| 0.7165 \| 0.2191 \| 0.0691 \| \| No log \| 13.0 \| 91 \| 3.7745 \| 0.78 \| 0.3229 \| 1.4554 \| 0.78 \| 0.6820 \| 0.1980 \| 0.0653 \| \| No log \| 14.0 \| 98 \| 3.8155 \| 0.805 \| 0.3070 \| 1.1638 \| 0.805 \| 0.7564 \| 0.1993 \| 0.0621 \| \| No log \| 15.0 \| 105 \| 3.8060 \| 0.815 \| 0.3038 \| 1.2009 \| 0.815 \| 0.7624 \| 0.2051 \| 0.0607 \| \| No log \| 16.0 \| 112 \| 3.8269 \| 0.81 \| 0.3044 \| 1.2937 \| 0.81 \| 0.7751 \| 0.1978 \| 0.0643 \| \| No log \| 17.0 \| 119 \| 3.8191 \| 0.83 \| 0.2841 \| 0.9571 \| 0.83 \| 0.7978 \| 0.1773 \| 0.0593 \| \| No log \| 18.0 \| 126 \| 3.8986 \| 0.81 \| 0.3109 \| 1.1933 \| 0.81 \| 0.7858 \| 0.1728 \| 0.0692 \| \| No log \| 19.0 \| 133 \| 3.8134 \| 0.845 \| 0.2694 \| 0.9314 \| 0.845 \| 0.8175 \| 0.1791 \| 0.0549 \| \| No log \| 20.0 \| 140 \| 3.8148 \| 0.825 \| 0.2768 \| 0.8896 \| 0.825 \| 0.8108 \| 0.1495 \| 0.0619 \| \| No log \| 21.0 \| 147 \| 3.7769 \| 0.83 \| 0.2639 \| 0.9579 \| 0.83 \| 0.8064 \| 0.1521 \| 0.0521 \| \| No log \| 22.0 \| 154 \| 3.7941 \| 0.84 \| 0.2596 \| 0.9457 \| 0.8400 \| 0.8176 \| 0.1644 \| 0.0534 \| \| No log \| 23.0 \| 161 \| 3.9296 \| 0.79 \| 0.2938 \| 1.0757 \| 0.79 \| 0.7732 \| 0.1612 \| 0.0706 \| \| No log \| 24.0 \| 168 \| 3.7899 \| 0.815 \| 0.2734 \| 1.0937 \| 0.815 \| 0.7910 \| 0.1570 \| 0.0531 \| \| No log \| 25.0 \| 175 \| 3.8261 \| 0.855 \| 0.2554 \| 0.9640 \| 0.855 \| 0.8370 \| 0.1378 \| 0.0559 \| \| No log \| 26.0 \| 182 \| 3.8596 \| 0.805 \| 0.2626 \| 1.0429 \| 0.805 \| 0.7814 \| 0.1548 \| 0.0597 \| \| No log \| 27.0 \| 189 \| 3.7974 \| 0.825 \| 0.2637 \| 1.0256 \| 0.825 \| 0.8107 \| 0.1251 \| 0.0522 \| \| No log \| 28.0 \| 196 \| 3.8304 \| 0.83 \| 0.2555 \| 1.1640 \| 0.83 \| 0.8081 \| 0.1337 \| 0.0561 \| \| No log \| 29.0 \| 203 \| 3.8346 \| 0.835 \| 0.2631 \| 0.8774 \| 0.835 \| 0.8110 \| 0.1473 \| 0.0564 \| \| No log \| 30.0 \| 210 \| 3.8251 \| 0.825 \| 0.2549 \| 0.9254 \| 0.825 \| 0.7979 \| 0.1298 \| 0.0541 \| \| No log \| 31.0 \| 217 \| 3.8759 \| 0.825 \| 0.2671 \| 0.9357 \| 0.825 \| 0.8014 \| 0.1672 \| 0.0609 \| \| No log \| 32.0 \| 224 \| 3.8466 \| 0.835 \| 0.2567 \| 1.0822 \| 0.835 \| 0.8168 \| 0.1454 \| 0.0553 \| \| No log \| 33.0 \| 231 \| 3.8600 \| 0.835 \| 0.2578 \| 1.0196 \| 0.835 \| 0.8103 \| 0.1363 \| 0.0577 \| \| No log \| 34.0 \| 238 \| 3.8364 \| 0.84 \| 0.2615 \| 0.9060 \| 0.8400 \| 0.8153 \| 0.1556 \| 0.0550 \| \| No log \| 35.0 \| 245 \| 3.8615 \| 0.84 \| 0.2741 \| 0.9777 \| 0.8400 \| 0.8175 \| 0.1553 \| 0.0609 \| \| No log \| 36.0 \| 252 \| 3.8354 \| 0.815 \| 0.2672 \| 1.0578 \| 0.815 \| 0.7829 \| 0.1412 \| 0.0571 \| \| No log \| 37.0 \| 259 \| 3.8214 \| 0.825 \| 0.2586 \| 1.1244 \| 0.825 \| 0.8005 \| 0.1567 \| 0.0555 \| \| No log \| 38.0 \| 266 \| 3.8379 \| 0.84 \| 0.2557 \| 0.9827 \| 0.8400 \| 0.8159 \| 0.1623 \| 0.0569 \| \| No log \| 39.0 \| 273 \| 3.8269 \| 0.82 \| 0.2590 \| 1.0025 \| 0.82 \| 0.7983 \| 0.1300 \| 0.0552 \| \| No log \| 40.0 \| 280 \| 3.8326 \| 0.835 \| 0.2576 \| 0.9914 \| 0.835 \| 0.8145 \| 0.1339 \| 0.0549 \| \| No log \| 41.0 \| 287 \| 3.8171 \| 0.845 \| 0.2446 \| 0.9369 \| 0.845 \| 0.8234 \| 0.1345 \| 0.0555 \| \| No log \| 42.0 \| 294 \| 3.8197 \| 0.835 \| 0.2421 \| 0.9189 \| 0.835 \| 0.8127 \| 0.1263 \| 0.0551 \| \| No log \| 43.0 \| 301 \| 3.8182 \| 0.835 \| 0.2421 \| 1.0651 \| 0.835 \| 0.8181 \| 0.1348 \| 0.0528 \| \| No log \| 44.0 \| 308 \| 3.8461 \| 0.85 \| 0.2482 \| 0.9057 \| 0.85 \| 0.8353 \| 0.1580 \| 0.0530 \| \| No log \| 45.0 \| 315 \| 3.8203 \| 0.845 \| 0.2405 \| 0.8504 \| 0.845 \| 0.8241 \| 0.1461 \| 0.0512 \| \| No log \| 46.0 \| 322 \| 3.8468 \| 0.845 \| 0.2431 \| 0.7909 \| 0.845 \| 0.8252 \| 0.1273 \| 0.0534 \| \| No log \| 47.0 \| 329 \| 3.8798 \| 0.84 \| 0.2486 \| 0.9810 \| 0.8400 \| 0.8219 \| 0.1412 \| 0.0565 \| \| No log \| 48.0 \| 336 \| 3.8650 \| 0.855 \| 0.2447 \| 0.9117 \| 0.855 \| 0.8372 \| 0.1465 \| 0.0525 \| \| No log \| 49.0 \| 343 \| 3.8425 \| 0.845 \| 0.2414 \| 1.0040 \| 0.845 \| 0.8231 \| 0.1251 \| 0.0536 \| \| No log \| 50.0 \| 350 \| 3.8350 \| 0.86 \| 0.2351 \| 0.8541 \| 0.8600 \| 0.8454 \| 0.1403 \| 0.0511 \| \| No log \| 51.0 \| 357 \| 3.8572 \| 0.84 \| 0.2427 \| 0.9366 \| 0.8400 \| 0.8197 \| 0.1249 \| 0.0527 \| \| No log \| 52.0 \| 364 \| 3.8461 \| 0.85 \| 0.2374 \| 0.8631 \| 0.85 \| 0.8352 \| 0.1269 \| 0.0506 \| \| No log \| 53.0 \| 371 \| 3.8692 \| 0.835 \| 0.2431 \| 0.9839 \| 0.835 \| 0.8110 \| 0.1356 \| 0.0546 \| \| No log \| 54.0 \| 378 \| 3.8806 \| 0.855 \| 0.2403 \| 1.0493 \| 0.855 \| 0.8343 \| 0.1287 \| 0.0530 \| \| No log \| 55.0 \| 385 \| 3.8875 \| 0.86 \| 0.2389 \| 0.9404 \| 0.8600 \| 0.8435 \| 0.1416 \| 0.0532 \| \| No log \| 56.0 \| 392 \| 3.8893 \| 0.84 \| 0.2407 \| 0.9410 \| 0.8400 \| 0.8189 \| 0.1256 \| 0.0539 \| \| No log \| 57.0 \| 399 \| 3.8923 \| 0.84 \| 0.2408 \| 0.9917 \| 0.8400 \| 0.8176 \| 0.1274 \| 0.0540 \| \| No log \| 58.0 \| 406 \| 3.8865 \| 0.86 \| 0.2323 \| 0.9745 \| 0.8600 \| 0.8444 \| 0.1372 \| 0.0517 \| \| No log \| 59.0 \| 413 \| 3.9037 \| 0.845 \| 0.2363 \| 0.9451 \| 0.845 \| 0.8230 \| 0.1422 \| 0.0533 \| \| No log \| 60.0 \| 420 \| 3.9040 \| 0.845 \| 0.2396 \| 1.0699 \| 0.845 \| 0.8233 \| 0.1272 \| 0.0522 \| \| No log \| 61.0 \| 427 \| 3.9136 \| 0.85 \| 0.2378 \| 1.0664 \| 0.85 \| 0.8289 \| 0.1283 \| 0.0527 \| \| No log \| 62.0 \| 434 \| 3.8877 \| 0.85 \| 0.2355 \| 0.8483 \| 0.85 \| 0.8315 \| 0.1445 \| 0.0519 \| \| No log \| 63.0 \| 441 \| 3.8991 \| 0.855 \| 0.2342 \| 0.9246 \| 0.855 \| 0.8374 \| 0.1124 \| 0.0509 \| \| No log \| 64.0 \| 448 \| 3.9207 \| 0.85 \| 0.2383 \| 1.0477 \| 0.85 \| 0.8330 \| 0.1235 \| 0.0514 \| \| No log \| 65.0 \| 455 \| 3.9000 \| 0.855 \| 0.2362 \| 1.0504 \| 0.855 \| 0.8384 \| 0.1375 \| 0.0529 \| \| No log \| 66.0 \| 462 \| 3.9542 \| 0.84 \| 0.2463 \| 0.9763 \| 0.8400 \| 0.8255 \| 0.1405 \| 0.0540 \| \| No log \| 67.0 \| 469 \| 3.9153 \| 0.855 \| 0.2374 \| 0.9954 \| 0.855 \| 0.8376 \| 0.1353 \| 0.0523 \| \| No log \| 68.0 \| 476 \| 3.9264 \| 0.845 \| 0.2410 \| 0.9917 \| 0.845 \| 0.8295 \| 0.1081 \| 0.0515 \| \| No log \| 69.0 \| 483 \| 3.8989 \| 0.86 \| 0.2272 \| 0.9322 \| 0.8600 \| 0.8438 \| 0.1260 \| 0.0492 \| \| No log \| 70.0 \| 490 \| 3.9224 \| 0.86 \| 0.2329 \| 0.9317 \| 0.8600 \| 0.8443 \| 0.1091 \| 0.0515 \| \| No log \| 71.0 \| 497 \| 3.9313 \| 0.85 \| 0.2360 \| 1.0424 \| 0.85 \| 0.8310 \| 0.1259 \| 0.0511 \| \| 3.5118 \| 72.0 \| 504 \| 3.9407 \| 0.85 \| 0.2343 \| 0.9333 \| 0.85 \| 0.8331 \| 0.1156 \| 0.0529 \| \| 3.5118 \| 73.0 \| 511 \| 3.9407 \| 0.865 \| 0.2318 \| 0.9791 \| 0.865 \| 0.8523 \| 0.1245 \| 0.0497 \| \| 3.5118 \| 74.0 \| 518 \| 3.9461 \| 0.855 \| 0.2347 \| 1.0488 \| 0.855 \| 0.8385 \| 0.1298 \| 0.0508 \| \| 3.5118 \| 75.0 \| 525 \| 3.9560 \| 0.86 \| 0.2319 \| 0.9924 \| 0.8600 \| 0.8444 \| 0.1410 \| 0.0504 \| \| 3.5118 \| 76.0 \| 532 \| 3.9608 \| 0.855 \| 0.2317 \| 0.9253 \| 0.855 \| 0.8390 \| 0.1380 \| 0.0517 \| \| 3.5118 \| 77.0 \| 539 \| 3.9638 \| 0.865 \| 0.2319 \| 0.9210 \| 0.865 \| 0.8528 \| 0.1202 \| 0.0504 \| \| 3.5118 \| 78.0 \| 546 \| 3.9718 \| 0.86 \| 0.2323 \| 0.9413 \| 0.8600 \| 0.8444 \| 0.1255 \| 0.0505 \| \| 3.5118 \| 79.0 \| 553 \| 3.9778 \| 0.86 \| 0.2324 \| 0.9916 \| 0.8600 \| 0.8444 \| 0.1270 \| 0.0506 \| \| 3.5118 \| 80.0 \| 560 \| 3.9813 \| 0.855 \| 0.2323 \| 0.9919 \| 0.855 \| 0.8390 \| 0.1246 \| 0.0509 \| \| 3.5118 \| 81.0 \| 567 \| 3.9876 \| 0.86 \| 0.2319 \| 0.9330 \| 0.8600 \| 0.8444 \| 0.1318 \| 0.0506 \| \| 3.5118 \| 82.0 \| 574 \| 3.9939 \| 0.855 \| 0.2324 \| 0.9328 \| 0.855 \| 0.8390 \| 0.1280 \| 0.0510 \| \| 3.5118 \| 83.0 \| 581 \| 3.9971 \| 0.86 \| 0.2319 \| 0.9321 \| 0.8600 \| 0.8444 \| 0.1303 \| 0.0503 \| \| 3.5118 \| 84.0 \| 588 \| 4.0003 \| 0.855 \| 0.2316 \| 0.9348 \| 0.855 \| 0.8390 \| 0.1284 \| 0.0508 \| \| 3.5118 \| 85.0 \| 595 \| 4.0054 \| 0.86 \| 0.2319 \| 0.9909 \| 0.8600 \| 0.8444 \| 0.1348 \| 0.0503 \| \| 3.5118 \| 86.0 \| 602 \| 4.0086 \| 0.86 \| 0.2315 \| 0.9338 \| 0.8600 \| 0.8444 \| 0.1340 \| 0.0504 \| \| 3.5118 \| 87.0 \| 609 \| 4.0125 \| 0.86 \| 0.2318 \| 0.9522 \| 0.8600 \| 0.8444 \| 0.1348 \| 0.0504 \| \| 3.5118 \| 88.0 \| 616 \| 4.0148 \| 0.86 \| 0.2316 \| 0.9396 \| 0.8600 \| 0.8444 \| 0.1323 \| 0.0504 \| \| 3.5118 \| 89.0 \| 623 \| 4.0185 \| 0.86 \| 0.2318 \| 0.9378 \| 0.8600 \| 0.8444 \| 0.1326 \| 0.0505 \| \| 3.5118 \| 90.0 \| 630 \| 4.0197 \| 0.86 \| 0.2316 \| 0.9412 \| 0.8600 \| 0.8444 \| 0.1253 \| 0.0506 \| \| 3.5118 \| 91.0 \| 637 \| 4.0231 \| 0.86 \| 0.2318 \| 0.9395 \| 0.8600 \| 0.8444 \| 0.1165 \| 0.0506 \| \| 3.5118 \| 92.0 \| 644 \| 4.0249 \| 0.86 \| 0.2316 \| 0.9921 \| 0.8600 \| 0.8444 \| 0.1159 \| 0.0504 \| \| 3.5118 \| 93.0 \| 651 \| 4.0266 \| 0.86 \| 0.2316 \| 0.9441 \| 0.8600 \| 0.8444 \| 0.1161 \| 0.0505 \| \| 3.5118 \| 94.0 \| 658 \| 4.0275 \| 0.86 \| 0.2317 \| 0.9934 \| 0.8600 \| 0.8444 \| 0.1159 \| 0.0504 \| \| 3.5118 \| 95.0 \| 665 \| 4.0289 \| 0.86 \| 0.2315 \| 0.9429 \| 0.8600 \| 0.8444 \| 0.1160 \| 0.0505 \| \| 3.5118 \| 96.0 \| 672 \| 4.0301 \| 0.86 \| 0.2316 \| 0.9932 \| 0.8600 \| 0.8444 \| 0.1163 \| 0.0505 \| \| 3.5118 \| 97.0 \| 679 \| 4.0304 \| 0.86 \| 0.2315 \| 0.9936 \| 0.8600 \| 0.8444 \| 0.1163 \| 0.0505 \| \| 3.5118 \| 98.0 \| 686 \| 4.0313 \| 0.86 \| 0.2316 \| 0.9935 \| 0.8600 \| 0.8444 \| 0.1163 \| 0.0504 \| \| 3.5118 \| 99.0 \| 693 \| 4.0317 \| 0.86 \| 0.2316 \| 0.9601 \| 0.8600 \| 0.8444 \| 0.1162 \| 0.0505 \| \| 3.5118 \| 100.0 \| 700 \| 4.0317 \| 0.86 \| 0.2316 \| 0.9681 \| 0.8600 \| 0.8444 \| 0.1162 \| 0.0505 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/81-tiny_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 81-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 63.0326 - Accuracy: 0.85 - Brier Loss: 0.2647 - Nll: 1.1178 - F1 Micro: 0.85 - F1 Macro: 0.8409 - Ece: 0.1296 - Aurc: 0.0380 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 66.3150 \| 0.26 \| 0.8706 \| 4.9002 \| 0.26 \| 0.1920 \| 0.2904 \| 0.7808 \| \| No log \| 2.0 \| 50 \| 65.5312 \| 0.54 \| 0.5948 \| 2.8544 \| 0.54 \| 0.4535 \| 0.2938 \| 0.2564 \| \| No log \| 3.0 \| 75 \| 65.0613 \| 0.675 \| 0.4403 \| 1.6101 \| 0.675 \| 0.6134 \| 0.2276 \| 0.1328 \| \| No log \| 4.0 \| 100 \| 64.7775 \| 0.765 \| 0.3800 \| 1.5512 \| 0.765 \| 0.7486 \| 0.2394 \| 0.1111 \| \| No log \| 5.0 \| 125 \| 64.4695 \| 0.8 \| 0.3065 \| 1.7477 \| 0.8000 \| 0.7658 \| 0.1814 \| 0.0713 \| \| No log \| 6.0 \| 150 \| 64.3179 \| 0.78 \| 0.3286 \| 1.7786 \| 0.78 \| 0.7409 \| 0.1555 \| 0.0636 \| \| No log \| 7.0 \| 175 \| 64.0707 \| 0.77 \| 0.3516 \| 1.6165 \| 0.7700 \| 0.7179 \| 0.1677 \| 0.0927 \| \| No log \| 8.0 \| 200 \| 63.8878 \| 0.775 \| 0.3677 \| 1.5868 \| 0.775 \| 0.7675 \| 0.1922 \| 0.0867 \| \| No log \| 9.0 \| 225 \| 64.2141 \| 0.715 \| 0.4341 \| 2.1772 \| 0.715 \| 0.7074 \| 0.2273 \| 0.1130 \| \| No log \| 10.0 \| 250 \| 63.8037 \| 0.775 \| 0.3649 \| 1.4471 \| 0.775 \| 0.7221 \| 0.1744 \| 0.0766 \| \| No log \| 11.0 \| 275 \| 64.2412 \| 0.655 \| 0.5396 \| 2.0448 \| 0.655 \| 0.6532 \| 0.2692 \| 0.1670 \| \| No log \| 12.0 \| 300 \| 63.8514 \| 0.73 \| 0.4372 \| 1.9358 \| 0.7300 \| 0.6997 \| 0.2194 \| 0.1008 \| \| No log \| 13.0 \| 325 \| 63.4352 \| 0.815 \| 0.2888 \| 1.6462 \| 0.815 \| 0.7858 \| 0.1490 \| 0.0469 \| \| No log \| 14.0 \| 350 \| 63.5101 \| 0.795 \| 0.3262 \| 1.3794 \| 0.795 \| 0.7816 \| 0.1599 \| 0.0648 \| \| No log \| 15.0 \| 375 \| 63.6365 \| 0.785 \| 0.3704 \| 1.4766 \| 0.785 \| 0.7627 \| 0.1691 \| 0.0781 \| \| No log \| 16.0 \| 400 \| 63.7245 \| 0.73 \| 0.4145 \| 1.7447 \| 0.7300 \| 0.7261 \| 0.2087 \| 0.0917 \| \| No log \| 17.0 \| 425 \| 63.4312 \| 0.795 \| 0.3148 \| 1.4363 \| 0.795 \| 0.7868 \| 0.1665 \| 0.0636 \| \| No log \| 18.0 \| 450 \| 63.7070 \| 0.835 \| 0.2915 \| 1.4455 \| 0.835 \| 0.8078 \| 0.1542 \| 0.0570 \| \| No log \| 19.0 \| 475 \| 63.3600 \| 0.81 \| 0.2997 \| 1.3326 \| 0.81 \| 0.7878 \| 0.1602 \| 0.0534 \| \| 62.5692 \| 20.0 \| 500 \| 63.4339 \| 0.81 \| 0.3158 \| 1.2051 \| 0.81 \| 0.7809 \| 0.1541 \| 0.0506 \| \| 62.5692 \| 21.0 \| 525 \| 63.3477 \| 0.805 \| 0.3088 \| 1.2575 \| 0.805 \| 0.7943 \| 0.1570 \| 0.0489 \| \| 62.5692 \| 22.0 \| 550 \| 63.3362 \| 0.83 \| 0.2911 \| 1.3409 \| 0.83 \| 0.8104 \| 0.1440 \| 0.0514 \| \| 62.5692 \| 23.0 \| 575 \| 63.3897 \| 0.805 \| 0.3004 \| 1.2505 \| 0.805 \| 0.7833 \| 0.1595 \| 0.0453 \| \| 62.5692 \| 24.0 \| 600 \| 63.3475 \| 0.8 \| 0.3185 \| 1.1190 \| 0.8000 \| 0.7750 \| 0.1626 \| 0.0486 \| \| 62.5692 \| 25.0 \| 625 \| 63.4552 \| 0.805 \| 0.3470 \| 1.2483 \| 0.805 \| 0.7904 \| 0.1818 \| 0.0652 \| \| 62.5692 \| 26.0 \| 650 \| 63.4364 \| 0.79 \| 0.3453 \| 1.1298 \| 0.79 \| 0.7798 \| 0.1827 \| 0.0651 \| \| 62.5692 \| 27.0 \| 675 \| 63.3001 \| 0.83 \| 0.2899 \| 1.4329 \| 0.83 \| 0.8141 \| 0.1370 \| 0.0466 \| \| 62.5692 \| 28.0 \| 700 \| 63.1848 \| 0.85 \| 0.2514 \| 1.2175 \| 0.85 \| 0.8354 \| 0.1220 \| 0.0396 \| \| 62.5692 \| 29.0 \| 725 \| 63.2303 \| 0.835 \| 0.2744 \| 1.4886 \| 0.835 \| 0.8143 \| 0.1389 \| 0.0532 \| \| 62.5692 \| 30.0 \| 750 \| 63.2275 \| 0.84 \| 0.2774 \| 1.0426 \| 0.8400 \| 0.8405 \| 0.1325 \| 0.0632 \| \| 62.5692 \| 31.0 \| 775 \| 63.1341 \| 0.835 \| 0.2597 \| 1.1066 \| 0.835 \| 0.8083 \| 0.1170 \| 0.0510 \| \| 62.5692 \| 32.0 \| 800 \| 63.2045 \| 0.81 \| 0.2970 \| 1.0432 \| 0.81 \| 0.8028 \| 0.1561 \| 0.0552 \| \| 62.5692 \| 33.0 \| 825 \| 63.1898 \| 0.82 \| 0.2952 \| 1.0189 \| 0.82 \| 0.8060 \| 0.1440 \| 0.0521 \| \| 62.5692 \| 34.0 \| 850 \| 63.1330 \| 0.835 \| 0.2760 \| 1.0095 \| 0.835 \| 0.8265 \| 0.1356 \| 0.0542 \| \| 62.5692 \| 35.0 \| 875 \| 63.1572 \| 0.84 \| 0.2834 \| 1.0174 \| 0.8400 \| 0.8234 \| 0.1337 \| 0.0508 \| \| 62.5692 \| 36.0 \| 900 \| 63.1922 \| 0.835 \| 0.2894 \| 1.0102 \| 0.835 \| 0.8233 \| 0.1469 \| 0.0500 \| \| 62.5692 \| 37.0 \| 925 \| 63.1305 \| 0.83 \| 0.2818 \| 1.0146 \| 0.83 \| 0.8172 \| 0.1387 \| 0.0510 \| \| 62.5692 \| 38.0 \| 950 \| 63.1902 \| 0.815 \| 0.2865 \| 1.0101 \| 0.815 \| 0.8016 \| 0.1500 \| 0.0516 \| \| 62.5692 \| 39.0 \| 975 \| 63.1835 \| 0.825 \| 0.2851 \| 1.0177 \| 0.825 \| 0.8162 \| 0.1436 \| 0.0496 \| \| 61.2333 \| 40.0 \| 1000 \| 63.1741 \| 0.84 \| 0.2783 \| 1.0160 \| 0.8400 \| 0.8266 \| 0.1275 \| 0.0510 \| \| 61.2333 \| 41.0 \| 1025 \| 63.1755 \| 0.835 \| 0.2756 \| 1.0117 \| 0.835 \| 0.8192 \| 0.1447 \| 0.0483 \| \| 61.2333 \| 42.0 \| 1050 \| 63.1281 \| 0.83 \| 0.2820 \| 1.0142 \| 0.83 \| 0.8169 \| 0.1415 \| 0.0466 \| \| 61.2333 \| 43.0 \| 1075 \| 63.1697 \| 0.85 \| 0.2675 \| 0.9929 \| 0.85 \| 0.8358 \| 0.1423 \| 0.0484 \| \| 61.2333 \| 44.0 \| 1100 \| 63.1141 \| 0.835 \| 0.2767 \| 1.0005 \| 0.835 \| 0.8237 \| 0.1293 \| 0.0481 \| \| 61.2333 \| 45.0 \| 1125 \| 63.1441 \| 0.85 \| 0.2638 \| 1.0023 \| 0.85 \| 0.8383 \| 0.1335 \| 0.0471 \| \| 61.2333 \| 46.0 \| 1150 \| 63.1221 \| 0.84 \| 0.2745 \| 0.9981 \| 0.8400 \| 0.8308 \| 0.1271 \| 0.0451 \| \| 61.2333 \| 47.0 \| 1175 \| 63.1140 \| 0.845 \| 0.2654 \| 0.9891 \| 0.845 \| 0.8317 \| 0.1351 \| 0.0458 \| \| 61.2333 \| 48.0 \| 1200 \| 63.1056 \| 0.845 \| 0.2654 \| 1.0016 \| 0.845 \| 0.8351 \| 0.1364 \| 0.0458 \| \| 61.2333 \| 49.0 \| 1225 \| 63.0906 \| 0.83 \| 0.2713 \| 1.0042 \| 0.83 \| 0.8221 \| 0.1455 \| 0.0449 \| \| 61.2333 \| 50.0 \| 1250 \| 63.0942 \| 0.835 \| 0.2633 \| 1.0003 \| 0.835 \| 0.8314 \| 0.1397 \| 0.0452 \| \| 61.2333 \| 51.0 \| 1275 \| 63.0929 \| 0.84 \| 0.2641 \| 0.9957 \| 0.8400 \| 0.8359 \| 0.1340 \| 0.0440 \| \| 61.2333 \| 52.0 \| 1300 \| 63.0913 \| 0.83 \| 0.2646 \| 1.0040 \| 0.83 \| 0.8242 \| 0.1422 \| 0.0440 \| \| 61.2333 \| 53.0 \| 1325 \| 63.1152 \| 0.83 \| 0.2754 \| 0.9985 \| 0.83 \| 0.8250 \| 0.1416 \| 0.0447 \| \| 61.2333 \| 54.0 \| 1350 \| 63.0923 \| 0.835 \| 0.2649 \| 0.9997 \| 0.835 \| 0.8278 \| 0.1356 \| 0.0426 \| \| 61.2333 \| 55.0 \| 1375 \| 63.0720 \| 0.83 \| 0.2686 \| 0.9988 \| 0.83 \| 0.8243 \| 0.1396 \| 0.0431 \| \| 61.2333 \| 56.0 \| 1400 \| 63.0627 \| 0.83 \| 0.2636 \| 1.0713 \| 0.83 \| 0.8243 \| 0.1369 \| 0.0427 \| \| 61.2333 \| 57.0 \| 1425 \| 63.0742 \| 0.835 \| 0.2692 \| 1.0572 \| 0.835 \| 0.8305 \| 0.1391 \| 0.0425 \| \| 61.2333 \| 58.0 \| 1450 \| 63.0910 \| 0.84 \| 0.2639 \| 1.0727 \| 0.8400 \| 0.8334 \| 0.1320 \| 0.0432 \| \| 61.2333 \| 59.0 \| 1475 \| 63.1015 \| 0.84 \| 0.2648 \| 1.1382 \| 0.8400 \| 0.8354 \| 0.1331 \| 0.0423 \| \| 61.0482 \| 60.0 \| 1500 \| 63.0557 \| 0.835 \| 0.2655 \| 1.0688 \| 0.835 \| 0.8293 \| 0.1333 \| 0.0420 \| \| 61.0482 \| 61.0 \| 1525 \| 63.0590 \| 0.835 \| 0.2655 \| 1.1378 \| 0.835 \| 0.8315 \| 0.1425 \| 0.0416 \| \| 61.0482 \| 62.0 \| 1550 \| 63.0732 \| 0.845 \| 0.2661 \| 1.0565 \| 0.845 \| 0.8381 \| 0.1404 \| 0.0413 \| \| 61.0482 \| 63.0 \| 1575 \| 63.0972 \| 0.855 \| 0.2659 \| 1.1274 \| 0.855 \| 0.8501 \| 0.1424 \| 0.0416 \| \| 61.0482 \| 64.0 \| 1600 \| 63.0528 \| 0.84 \| 0.2694 \| 1.1315 \| 0.8400 \| 0.8330 \| 0.1355 \| 0.0418 \| \| 61.0482 \| 65.0 \| 1625 \| 63.0625 \| 0.835 \| 0.2683 \| 1.1336 \| 0.835 \| 0.8281 \| 0.1373 \| 0.0411 \| \| 61.0482 \| 66.0 \| 1650 \| 63.0512 \| 0.835 \| 0.2747 \| 1.1250 \| 0.835 \| 0.8242 \| 0.1436 \| 0.0410 \| \| 61.0482 \| 67.0 \| 1675 \| 63.0634 \| 0.85 \| 0.2671 \| 1.1270 \| 0.85 \| 0.8397 \| 0.1376 \| 0.0419 \| \| 61.0482 \| 68.0 \| 1700 \| 63.0609 \| 0.835 \| 0.2717 \| 1.1311 \| 0.835 \| 0.8295 \| 0.1365 \| 0.0411 \| \| 61.0482 \| 69.0 \| 1725 \| 63.0513 \| 0.835 \| 0.2707 \| 1.1261 \| 0.835 \| 0.8223 \| 0.1461 \| 0.0412 \| \| 61.0482 \| 70.0 \| 1750 \| 63.0510 \| 0.845 \| 0.2712 \| 1.1219 \| 0.845 \| 0.8396 \| 0.1369 \| 0.0411 \| \| 61.0482 \| 71.0 \| 1775 \| 63.0530 \| 0.845 \| 0.2688 \| 1.1244 \| 0.845 \| 0.8364 \| 0.1403 \| 0.0412 \| \| 61.0482 \| 72.0 \| 1800 \| 63.0456 \| 0.84 \| 0.2665 \| 1.1204 \| 0.8400 \| 0.8293 \| 0.1341 \| 0.0400 \| \| 61.0482 \| 73.0 \| 1825 \| 63.0459 \| 0.845 \| 0.2680 \| 1.1244 \| 0.845 \| 0.8360 \| 0.1430 \| 0.0398 \| \| 61.0482 \| 74.0 \| 1850 \| 63.0773 \| 0.85 \| 0.2684 \| 1.1291 \| 0.85 \| 0.8440 \| 0.1386 \| 0.0410 \| \| 61.0482 \| 75.0 \| 1875 \| 63.0497 \| 0.85 \| 0.2664 \| 1.1285 \| 0.85 \| 0.8388 \| 0.1293 \| 0.0392 \| \| 61.0482 \| 76.0 \| 1900 \| 63.0483 \| 0.845 \| 0.2695 \| 1.1256 \| 0.845 \| 0.8352 \| 0.1440 \| 0.0409 \| \| 61.0482 \| 77.0 \| 1925 \| 63.0352 \| 0.845 \| 0.2680 \| 1.1229 \| 0.845 \| 0.8352 \| 0.1420 \| 0.0398 \| \| 61.0482 \| 78.0 \| 1950 \| 63.0291 \| 0.845 \| 0.2701 \| 1.1225 \| 0.845 \| 0.8352 \| 0.1342 \| 0.0394 \| \| 61.0482 \| 79.0 \| 1975 \| 63.0508 \| 0.85 \| 0.2695 \| 1.1224 \| 0.85 \| 0.8388 \| 0.1418 \| 0.0399 \| \| 60.9704 \| 80.0 \| 2000 \| 63.0510 \| 0.85 \| 0.2708 \| 1.1169 \| 0.85 \| 0.8388 \| 0.1376 \| 0.0394 \| \| 60.9704 \| 81.0 \| 2025 \| 63.0460 \| 0.85 \| 0.2648 \| 1.1205 \| 0.85 \| 0.8440 \| 0.1357 \| 0.0397 \| \| 60.9704 \| 82.0 \| 2050 \| 63.0505 \| 0.845 \| 0.2697 \| 1.1148 \| 0.845 \| 0.8352 \| 0.1464 \| 0.0392 \| \| 60.9704 \| 83.0 \| 2075 \| 63.0425 \| 0.845 \| 0.2651 \| 1.1229 \| 0.845 \| 0.8352 \| 0.1396 \| 0.0389 \| \| 60.9704 \| 84.0 \| 2100 \| 63.0398 \| 0.845 \| 0.2664 \| 1.1197 \| 0.845 \| 0.8337 \| 0.1330 \| 0.0388 \| \| 60.9704 \| 85.0 \| 2125 \| 63.0355 \| 0.845 \| 0.2667 \| 1.1192 \| 0.845 \| 0.8360 \| 0.1307 \| 0.0387 \| \| 60.9704 \| 86.0 \| 2150 \| 63.0386 \| 0.85 \| 0.2649 \| 1.1223 \| 0.85 \| 0.8409 \| 0.1279 \| 0.0379 \| \| 60.9704 \| 87.0 \| 2175 \| 63.0405 \| 0.85 \| 0.2642 \| 1.1218 \| 0.85 \| 0.8409 \| 0.1437 \| 0.0378 \| \| 60.9704 \| 88.0 \| 2200 \| 63.0363 \| 0.85 \| 0.2667 \| 1.1165 \| 0.85 \| 0.8388 \| 0.1320 \| 0.0390 \| \| 60.9704 \| 89.0 \| 2225 \| 63.0456 \| 0.845 \| 0.2644 \| 1.1180 \| 0.845 \| 0.8352 \| 0.1354 \| 0.0381 \| \| 60.9704 \| 90.0 \| 2250 \| 63.0343 \| 0.845 \| 0.2656 \| 1.1159 \| 0.845 \| 0.8337 \| 0.1390 \| 0.0385 \| \| 60.9704 \| 91.0 \| 2275 \| 63.0391 \| 0.85 \| 0.2654 \| 1.1194 \| 0.85 \| 0.8409 \| 0.1389 \| 0.0380 \| \| 60.9704 \| 92.0 \| 2300 \| 63.0354 \| 0.85 \| 0.2665 \| 1.1203 \| 0.85 \| 0.8409 \| 0.1419 \| 0.0377 \| \| 60.9704 \| 93.0 \| 2325 \| 63.0272 \| 0.845 \| 0.2650 \| 1.1166 \| 0.845 \| 0.8381 \| 0.1370 \| 0.0381 \| \| 60.9704 \| 94.0 \| 2350 \| 63.0313 \| 0.85 \| 0.2647 \| 1.1181 \| 0.85 \| 0.8409 \| 0.1322 \| 0.0380 \| \| 60.9704 \| 95.0 \| 2375 \| 63.0220 \| 0.845 \| 0.2658 \| 1.1197 \| 0.845 \| 0.8357 \| 0.1311 \| 0.0378 \| \| 60.9704 \| 96.0 \| 2400 \| 63.0345 \| 0.85 \| 0.2639 \| 1.1179 \| 0.85 \| 0.8409 \| 0.1284 \| 0.0381 \| \| 60.9704 \| 97.0 \| 2425 \| 63.0330 \| 0.845 \| 0.2651 \| 1.1163 \| 0.845 \| 0.8352 \| 0.1391 \| 0.0382 \| \| 60.9704 \| 98.0 \| 2450 \| 63.0302 \| 0.85 \| 0.2646 \| 1.1182 \| 0.85 \| 0.8409 \| 0.1311 \| 0.0380 \| \| 60.9704 \| 99.0 \| 2475 \| 63.0287 \| 0.85 \| 0.2646 \| 1.1175 \| 0.85 \| 0.8409 \| 0.1250 \| 0.0380 \| \| 60.9392 \| 100.0 \| 2500 \| 63.0326 \| 0.85 \| 0.2647 \| 1.1178 \| 0.85 \| 0.8409 \| 0.1296 \| 0.0380 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/60-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 60-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4631 - Accuracy: 0.81 - Brier Loss: 0.3427 - Nll: 0.9896 - F1 Micro: 0.81 - F1 Macro: 0.7794 - Ece: 0.3002 - Aurc: 0.0534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.0115 \| 0.1 \| 1.0070 \| 8.1872 \| 0.1000 \| 0.0765 \| 0.3197 \| 0.8958 \| \| No log \| 2.0 \| 14 \| 1.3480 \| 0.19 \| 0.8667 \| 5.2268 \| 0.19 \| 0.1599 \| 0.2694 \| 0.7716 \| \| No log \| 3.0 \| 21 \| 1.0745 \| 0.38 \| 0.7608 \| 4.8555 \| 0.38 \| 0.3111 \| 0.2804 \| 0.4226 \| \| No log \| 4.0 \| 28 \| 0.8841 \| 0.57 \| 0.6204 \| 2.8740 \| 0.57 \| 0.4526 \| 0.2904 \| 0.2536 \| \| No log \| 5.0 \| 35 \| 0.7962 \| 0.63 \| 0.5646 \| 2.4082 \| 0.63 \| 0.5462 \| 0.3178 \| 0.2005 \| \| No log \| 6.0 \| 42 \| 0.7065 \| 0.68 \| 0.4894 \| 2.3364 \| 0.68 \| 0.5773 \| 0.2660 \| 0.1461 \| \| No log \| 7.0 \| 49 \| 0.6639 \| 0.7 \| 0.4548 \| 1.8260 \| 0.7 \| 0.6412 \| 0.2748 \| 0.1340 \| \| No log \| 8.0 \| 56 \| 0.6374 \| 0.745 \| 0.4265 \| 1.8720 \| 0.745 \| 0.6620 \| 0.3127 \| 0.1047 \| \| No log \| 9.0 \| 63 \| 0.6184 \| 0.73 \| 0.4136 \| 1.7957 \| 0.7300 \| 0.6650 \| 0.3022 \| 0.0845 \| \| No log \| 10.0 \| 70 \| 0.5569 \| 0.8 \| 0.3838 \| 1.2127 \| 0.8000 \| 0.7683 \| 0.3047 \| 0.0721 \| \| No log \| 11.0 \| 77 \| 0.5225 \| 0.815 \| 0.3514 \| 1.0539 \| 0.815 \| 0.7871 \| 0.2818 \| 0.0625 \| \| No log \| 12.0 \| 84 \| 0.5330 \| 0.795 \| 0.3609 \| 1.0544 \| 0.795 \| 0.7695 \| 0.2723 \| 0.0786 \| \| No log \| 13.0 \| 91 \| 0.5241 \| 0.78 \| 0.3615 \| 1.1338 \| 0.78 \| 0.7599 \| 0.2681 \| 0.0726 \| \| No log \| 14.0 \| 98 \| 0.5345 \| 0.795 \| 0.3640 \| 1.1229 \| 0.795 \| 0.7527 \| 0.2950 \| 0.0661 \| \| No log \| 15.0 \| 105 \| 0.5042 \| 0.8 \| 0.3459 \| 1.1690 \| 0.8000 \| 0.7664 \| 0.2915 \| 0.0664 \| \| No log \| 16.0 \| 112 \| 0.5285 \| 0.795 \| 0.3779 \| 1.1973 \| 0.795 \| 0.7468 \| 0.3056 \| 0.0647 \| \| No log \| 17.0 \| 119 \| 0.5374 \| 0.77 \| 0.3716 \| 1.5324 \| 0.7700 \| 0.7406 \| 0.2658 \| 0.0910 \| \| No log \| 18.0 \| 126 \| 0.5132 \| 0.79 \| 0.3677 \| 1.1252 \| 0.79 \| 0.7699 \| 0.2842 \| 0.0722 \| \| No log \| 19.0 \| 133 \| 0.4961 \| 0.815 \| 0.3558 \| 0.8880 \| 0.815 \| 0.7941 \| 0.2985 \| 0.0611 \| \| No log \| 20.0 \| 140 \| 0.4971 \| 0.82 \| 0.3556 \| 1.0845 \| 0.82 \| 0.8049 \| 0.3045 \| 0.0631 \| \| No log \| 21.0 \| 147 \| 0.4791 \| 0.835 \| 0.3328 \| 0.9109 \| 0.835 \| 0.8168 \| 0.2803 \| 0.0534 \| \| No log \| 22.0 \| 154 \| 0.4953 \| 0.805 \| 0.3531 \| 1.0087 \| 0.805 \| 0.7758 \| 0.3015 \| 0.0642 \| \| No log \| 23.0 \| 161 \| 0.4962 \| 0.825 \| 0.3543 \| 0.8886 \| 0.825 \| 0.8091 \| 0.3085 \| 0.0672 \| \| No log \| 24.0 \| 168 \| 0.4753 \| 0.82 \| 0.3435 \| 1.0609 \| 0.82 \| 0.8025 \| 0.2994 \| 0.0508 \| \| No log \| 25.0 \| 175 \| 0.4856 \| 0.825 \| 0.3495 \| 1.1336 \| 0.825 \| 0.7846 \| 0.3018 \| 0.0539 \| \| No log \| 26.0 \| 182 \| 0.4824 \| 0.805 \| 0.3499 \| 1.0142 \| 0.805 \| 0.7890 \| 0.3000 \| 0.0577 \| \| No log \| 27.0 \| 189 \| 0.4843 \| 0.83 \| 0.3506 \| 0.7550 \| 0.83 \| 0.8090 \| 0.3163 \| 0.0556 \| \| No log \| 28.0 \| 196 \| 0.4728 \| 0.825 \| 0.3451 \| 1.1111 \| 0.825 \| 0.8012 \| 0.3006 \| 0.0554 \| \| No log \| 29.0 \| 203 \| 0.4844 \| 0.8 \| 0.3483 \| 1.2220 \| 0.8000 \| 0.7750 \| 0.2913 \| 0.0609 \| \| No log \| 30.0 \| 210 \| 0.4728 \| 0.815 \| 0.3478 \| 1.1306 \| 0.815 \| 0.7840 \| 0.3051 \| 0.0540 \| \| No log \| 31.0 \| 217 \| 0.4694 \| 0.83 \| 0.3411 \| 0.8267 \| 0.83 \| 0.8156 \| 0.3101 \| 0.0474 \| \| No log \| 32.0 \| 224 \| 0.4685 \| 0.82 \| 0.3406 \| 1.0564 \| 0.82 \| 0.7909 \| 0.3269 \| 0.0529 \| \| No log \| 33.0 \| 231 \| 0.4692 \| 0.81 \| 0.3407 \| 0.8508 \| 0.81 \| 0.7900 \| 0.2838 \| 0.0517 \| \| No log \| 34.0 \| 238 \| 0.4647 \| 0.815 \| 0.3404 \| 1.0430 \| 0.815 \| 0.7894 \| 0.3098 \| 0.0563 \| \| No log \| 35.0 \| 245 \| 0.4761 \| 0.795 \| 0.3503 \| 1.0340 \| 0.795 \| 0.7687 \| 0.2731 \| 0.0594 \| \| No log \| 36.0 \| 252 \| 0.4802 \| 0.83 \| 0.3571 \| 0.8575 \| 0.83 \| 0.8046 \| 0.3275 \| 0.0511 \| \| No log \| 37.0 \| 259 \| 0.4686 \| 0.8 \| 0.3414 \| 0.8818 \| 0.8000 \| 0.7836 \| 0.3020 \| 0.0560 \| \| No log \| 38.0 \| 266 \| 0.4612 \| 0.815 \| 0.3361 \| 0.9901 \| 0.815 \| 0.7807 \| 0.2988 \| 0.0528 \| \| No log \| 39.0 \| 273 \| 0.4721 \| 0.81 \| 0.3475 \| 0.9803 \| 0.81 \| 0.7875 \| 0.3019 \| 0.0576 \| \| No log \| 40.0 \| 280 \| 0.4646 \| 0.83 \| 0.3425 \| 0.8495 \| 0.83 \| 0.8059 \| 0.3063 \| 0.0507 \| \| No log \| 41.0 \| 287 \| 0.4622 \| 0.805 \| 0.3396 \| 1.0087 \| 0.805 \| 0.7688 \| 0.2903 \| 0.0558 \| \| No log \| 42.0 \| 294 \| 0.4599 \| 0.82 \| 0.3375 \| 0.9166 \| 0.82 \| 0.7996 \| 0.3319 \| 0.0533 \| \| No log \| 43.0 \| 301 \| 0.4688 \| 0.805 \| 0.3475 \| 1.0195 \| 0.805 \| 0.7771 \| 0.2994 \| 0.0573 \| \| No log \| 44.0 \| 308 \| 0.4652 \| 0.805 \| 0.3437 \| 0.9095 \| 0.805 \| 0.7809 \| 0.3134 \| 0.0545 \| \| No log \| 45.0 \| 315 \| 0.4641 \| 0.81 \| 0.3418 \| 0.8922 \| 0.81 \| 0.7806 \| 0.3014 \| 0.0550 \| \| No log \| 46.0 \| 322 \| 0.4579 \| 0.825 \| 0.3361 \| 0.9516 \| 0.825 \| 0.7956 \| 0.2971 \| 0.0490 \| \| No log \| 47.0 \| 329 \| 0.4638 \| 0.82 \| 0.3423 \| 0.8830 \| 0.82 \| 0.7961 \| 0.3259 \| 0.0557 \| \| No log \| 48.0 \| 336 \| 0.4643 \| 0.81 \| 0.3434 \| 1.0046 \| 0.81 \| 0.7789 \| 0.3042 \| 0.0541 \| \| No log \| 49.0 \| 343 \| 0.4596 \| 0.81 \| 0.3388 \| 0.9862 \| 0.81 \| 0.7835 \| 0.3170 \| 0.0532 \| \| No log \| 50.0 \| 350 \| 0.4603 \| 0.815 \| 0.3399 \| 0.9288 \| 0.815 \| 0.7963 \| 0.3031 \| 0.0533 \| \| No log \| 51.0 \| 357 \| 0.4610 \| 0.815 \| 0.3403 \| 0.9900 \| 0.815 \| 0.7898 \| 0.3306 \| 0.0546 \| \| No log \| 52.0 \| 364 \| 0.4617 \| 0.81 \| 0.3412 \| 0.9834 \| 0.81 \| 0.7793 \| 0.3079 \| 0.0533 \| \| No log \| 53.0 \| 371 \| 0.4627 \| 0.815 \| 0.3423 \| 0.9901 \| 0.815 \| 0.7898 \| 0.3023 \| 0.0543 \| \| No log \| 54.0 \| 378 \| 0.4612 \| 0.815 \| 0.3415 \| 0.9868 \| 0.815 \| 0.7962 \| 0.3178 \| 0.0534 \| \| No log \| 55.0 \| 385 \| 0.4617 \| 0.815 \| 0.3416 \| 0.9904 \| 0.815 \| 0.7898 \| 0.3117 \| 0.0533 \| \| No log \| 56.0 \| 392 \| 0.4605 \| 0.81 \| 0.3399 \| 0.9845 \| 0.81 \| 0.7793 \| 0.3069 \| 0.0535 \| \| No log \| 57.0 \| 399 \| 0.4606 \| 0.81 \| 0.3405 \| 0.9818 \| 0.81 \| 0.7793 \| 0.3045 \| 0.0531 \| \| No log \| 58.0 \| 406 \| 0.4614 \| 0.81 \| 0.3413 \| 0.9853 \| 0.81 \| 0.7793 \| 0.3114 \| 0.0537 \| \| No log \| 59.0 \| 413 \| 0.4623 \| 0.81 \| 0.3424 \| 0.9848 \| 0.81 \| 0.7793 \| 0.3045 \| 0.0534 \| \| No log \| 60.0 \| 420 \| 0.4621 \| 0.81 \| 0.3421 \| 0.9898 \| 0.81 \| 0.7863 \| 0.3150 \| 0.0536 \| \| No log \| 61.0 \| 427 \| 0.4620 \| 0.81 \| 0.3417 \| 0.9868 \| 0.81 \| 0.7793 \| 0.3060 \| 0.0534 \| \| No log \| 62.0 \| 434 \| 0.4618 \| 0.81 \| 0.3413 \| 0.9843 \| 0.81 \| 0.7793 \| 0.3029 \| 0.0533 \| \| No log \| 63.0 \| 441 \| 0.4622 \| 0.81 \| 0.3419 \| 0.9868 \| 0.81 \| 0.7793 \| 0.2969 \| 0.0535 \| \| No log \| 64.0 \| 448 \| 0.4621 \| 0.81 \| 0.3419 \| 0.9881 \| 0.81 \| 0.7793 \| 0.3070 \| 0.0542 \| \| No log \| 65.0 \| 455 \| 0.4625 \| 0.81 \| 0.3422 \| 0.9871 \| 0.81 \| 0.7794 \| 0.3131 \| 0.0532 \| \| No log \| 66.0 \| 462 \| 0.4626 \| 0.81 \| 0.3423 \| 0.9880 \| 0.81 \| 0.7794 \| 0.3066 \| 0.0533 \| \| No log \| 67.0 \| 469 \| 0.4621 \| 0.81 \| 0.3420 \| 0.9872 \| 0.81 \| 0.7793 \| 0.3066 \| 0.0536 \| \| No log \| 68.0 \| 476 \| 0.4624 \| 0.81 \| 0.3421 \| 0.9882 \| 0.81 \| 0.7794 \| 0.2966 \| 0.0533 \| \| No log \| 69.0 \| 483 \| 0.4627 \| 0.81 \| 0.3425 \| 0.9891 \| 0.81 \| 0.7794 \| 0.3160 \| 0.0534 \| \| No log \| 70.0 \| 490 \| 0.4628 \| 0.81 \| 0.3424 \| 0.9899 \| 0.81 \| 0.7794 \| 0.2970 \| 0.0533 \| \| No log \| 71.0 \| 497 \| 0.4627 \| 0.81 \| 0.3423 \| 0.9890 \| 0.81 \| 0.7794 \| 0.2968 \| 0.0532 \| \| 0.3139 \| 72.0 \| 504 \| 0.4625 \| 0.81 \| 0.3423 \| 0.9886 \| 0.81 \| 0.7794 \| 0.2971 \| 0.0534 \| \| 0.3139 \| 73.0 \| 511 \| 0.4625 \| 0.81 \| 0.3423 \| 0.9892 \| 0.81 \| 0.7794 \| 0.3043 \| 0.0535 \| \| 0.3139 \| 74.0 \| 518 \| 0.4626 \| 0.81 \| 0.3422 \| 0.9881 \| 0.81 \| 0.7794 \| 0.2969 \| 0.0533 \| \| 0.3139 \| 75.0 \| 525 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9896 \| 0.81 \| 0.7794 \| 0.3142 \| 0.0533 \| \| 0.3139 \| 76.0 \| 532 \| 0.4628 \| 0.81 \| 0.3425 \| 0.9893 \| 0.81 \| 0.7794 \| 0.3138 \| 0.0532 \| \| 0.3139 \| 77.0 \| 539 \| 0.4627 \| 0.81 \| 0.3423 \| 0.9889 \| 0.81 \| 0.7794 \| 0.3040 \| 0.0533 \| \| 0.3139 \| 78.0 \| 546 \| 0.4628 \| 0.81 \| 0.3425 \| 0.9888 \| 0.81 \| 0.7794 \| 0.3138 \| 0.0533 \| \| 0.3139 \| 79.0 \| 553 \| 0.4629 \| 0.81 \| 0.3426 \| 0.9898 \| 0.81 \| 0.7794 \| 0.3002 \| 0.0535 \| \| 0.3139 \| 80.0 \| 560 \| 0.4630 \| 0.81 \| 0.3426 \| 0.9892 \| 0.81 \| 0.7794 \| 0.3041 \| 0.0534 \| \| 0.3139 \| 81.0 \| 567 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9899 \| 0.81 \| 0.7794 \| 0.3042 \| 0.0534 \| \| 0.3139 \| 82.0 \| 574 \| 0.4628 \| 0.81 \| 0.3424 \| 0.9889 \| 0.81 \| 0.7794 \| 0.3039 \| 0.0532 \| \| 0.3139 \| 83.0 \| 581 \| 0.4630 \| 0.81 \| 0.3427 \| 0.9893 \| 0.81 \| 0.7794 \| 0.3068 \| 0.0533 \| \| 0.3139 \| 84.0 \| 588 \| 0.4629 \| 0.81 \| 0.3426 \| 0.9894 \| 0.81 \| 0.7794 \| 0.3069 \| 0.0534 \| \| 0.3139 \| 85.0 \| 595 \| 0.4629 \| 0.81 \| 0.3425 \| 0.9893 \| 0.81 \| 0.7794 \| 0.3138 \| 0.0535 \| \| 0.3139 \| 86.0 \| 602 \| 0.4630 \| 0.81 \| 0.3427 \| 0.9896 \| 0.81 \| 0.7794 \| 0.3070 \| 0.0533 \| \| 0.3139 \| 87.0 \| 609 \| 0.4630 \| 0.81 \| 0.3426 \| 0.9890 \| 0.81 \| 0.7794 \| 0.3069 \| 0.0534 \| \| 0.3139 \| 88.0 \| 616 \| 0.4630 \| 0.81 \| 0.3426 \| 0.9893 \| 0.81 \| 0.7794 \| 0.3069 \| 0.0533 \| \| 0.3139 \| 89.0 \| 623 \| 0.4630 \| 0.81 \| 0.3426 \| 0.9897 \| 0.81 \| 0.7794 \| 0.3001 \| 0.0535 \| \| 0.3139 \| 90.0 \| 630 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9902 \| 0.81 \| 0.7794 \| 0.2904 \| 0.0534 \| \| 0.3139 \| 91.0 \| 637 \| 0.4631 \| 0.81 \| 0.3427 \| 0.9892 \| 0.81 \| 0.7794 \| 0.3139 \| 0.0533 \| \| 0.3139 \| 92.0 \| 644 \| 0.4631 \| 0.81 \| 0.3427 \| 0.9894 \| 0.81 \| 0.7794 \| 0.3071 \| 0.0535 \| \| 0.3139 \| 93.0 \| 651 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9899 \| 0.81 \| 0.7794 \| 0.3001 \| 0.0534 \| \| 0.3139 \| 94.0 \| 658 \| 0.4630 \| 0.81 \| 0.3427 \| 0.9894 \| 0.81 \| 0.7794 \| 0.3069 \| 0.0534 \| \| 0.3139 \| 95.0 \| 665 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9896 \| 0.81 \| 0.7794 \| 0.3071 \| 0.0534 \| \| 0.3139 \| 96.0 \| 672 \| 0.4630 \| 0.81 \| 0.3427 \| 0.9893 \| 0.81 \| 0.7794 \| 0.3070 \| 0.0534 \| \| 0.3139 \| 97.0 \| 679 \| 0.4631 \| 0.81 \| 0.3427 \| 0.9895 \| 0.81 \| 0.7794 \| 0.3002 \| 0.0535 \| \| 0.3139 \| 98.0 \| 686 \| 0.4631 \| 0.81 \| 0.3428 \| 0.9899 \| 0.81 \| 0.7794 \| 0.3002 \| 0.0534 \| \| 0.3139 \| 99.0 \| 693 \| 0.4631 \| 0.81 \| 0.3427 \| 0.9897 \| 0.81 \| 0.7794 \| 0.3002 \| 0.0534 \| \| 0.3139 \| 100.0 \| 700 \| 0.4631 \| 0.81 \| 0.3427 \| 0.9896 \| 0.81 \| 0.7794 \| 0.3002 \| 0.0534 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
Sanfee18/my_awesome_food_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6056 - Accuracy: 0.901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6911 \| 0.99 \| 62 \| 2.5097 \| 0.856 \| \| 1.8202 \| 2.0 \| 125 \| 1.7735 \| 0.894 \| \| 1.5694 \| 2.98 \| 186 \| 1.6056 \| 0.901 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
jordyvl/60-tiny_tobacco3482	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 60-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1098 - Accuracy: 0.79 - Brier Loss: 0.4604 - Nll: 0.9058 - F1 Micro: 0.79 - F1 Macro: 0.7539 - Ece: 0.4083 - Aurc: 0.0644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.9988 \| 0.16 \| 1.0265 \| 8.4697 \| 0.16 \| 0.0543 \| 0.3527 \| 0.8493 \| \| No log \| 2.0 \| 14 \| 0.9241 \| 0.1 \| 0.9095 \| 7.2634 \| 0.1000 \| 0.0853 \| 0.2311 \| 0.8707 \| \| No log \| 3.0 \| 21 \| 0.5325 \| 0.29 \| 0.8525 \| 5.4845 \| 0.29 \| 0.1287 \| 0.2809 \| 0.6344 \| \| No log \| 4.0 \| 28 \| 0.4050 \| 0.36 \| 0.7891 \| 3.8464 \| 0.36 \| 0.2714 \| 0.2948 \| 0.4598 \| \| No log \| 5.0 \| 35 \| 0.3282 \| 0.495 \| 0.7189 \| 2.5837 \| 0.495 \| 0.3741 \| 0.3317 \| 0.3367 \| \| No log \| 6.0 \| 42 \| 0.2722 \| 0.585 \| 0.6827 \| 2.2625 \| 0.585 \| 0.4818 \| 0.3751 \| 0.2241 \| \| No log \| 7.0 \| 49 \| 0.2426 \| 0.605 \| 0.6424 \| 2.0945 \| 0.605 \| 0.4641 \| 0.3516 \| 0.2034 \| \| No log \| 8.0 \| 56 \| 0.2223 \| 0.685 \| 0.6171 \| 1.9679 \| 0.685 \| 0.5711 \| 0.4148 \| 0.1545 \| \| No log \| 9.0 \| 63 \| 0.1939 \| 0.67 \| 0.5684 \| 1.8320 \| 0.67 \| 0.5495 \| 0.3460 \| 0.1438 \| \| No log \| 10.0 \| 70 \| 0.1950 \| 0.695 \| 0.5454 \| 1.3666 \| 0.695 \| 0.5750 \| 0.3667 \| 0.1276 \| \| No log \| 11.0 \| 77 \| 0.1838 \| 0.69 \| 0.5436 \| 1.4378 \| 0.69 \| 0.5745 \| 0.3846 \| 0.1162 \| \| No log \| 12.0 \| 84 \| 0.1787 \| 0.735 \| 0.5138 \| 1.4550 \| 0.735 \| 0.6490 \| 0.3893 \| 0.0949 \| \| No log \| 13.0 \| 91 \| 0.1682 \| 0.74 \| 0.5209 \| 1.6406 \| 0.74 \| 0.6452 \| 0.3904 \| 0.1052 \| \| No log \| 14.0 \| 98 \| 0.1734 \| 0.75 \| 0.5420 \| 1.4280 \| 0.75 \| 0.6656 \| 0.4440 \| 0.0864 \| \| No log \| 15.0 \| 105 \| 0.1404 \| 0.725 \| 0.4961 \| 1.3478 \| 0.7250 \| 0.6343 \| 0.3816 \| 0.0895 \| \| No log \| 16.0 \| 112 \| 0.1439 \| 0.76 \| 0.4709 \| 1.4366 \| 0.76 \| 0.6679 \| 0.3721 \| 0.0754 \| \| No log \| 17.0 \| 119 \| 0.1356 \| 0.74 \| 0.4745 \| 1.3609 \| 0.74 \| 0.6596 \| 0.3643 \| 0.0868 \| \| No log \| 18.0 \| 126 \| 0.1373 \| 0.75 \| 0.4760 \| 1.4421 \| 0.75 \| 0.6703 \| 0.3773 \| 0.0783 \| \| No log \| 19.0 \| 133 \| 0.1352 \| 0.765 \| 0.4851 \| 1.1693 \| 0.765 \| 0.6803 \| 0.3952 \| 0.0693 \| \| No log \| 20.0 \| 140 \| 0.1323 \| 0.765 \| 0.4838 \| 1.0026 \| 0.765 \| 0.6844 \| 0.3866 \| 0.0741 \| \| No log \| 21.0 \| 147 \| 0.1334 \| 0.785 \| 0.4713 \| 1.1479 \| 0.785 \| 0.7430 \| 0.4105 \| 0.0737 \| \| No log \| 22.0 \| 154 \| 0.1267 \| 0.775 \| 0.4706 \| 1.1082 \| 0.775 \| 0.7355 \| 0.3838 \| 0.0727 \| \| No log \| 23.0 \| 161 \| 0.1279 \| 0.77 \| 0.4598 \| 1.0869 \| 0.7700 \| 0.7254 \| 0.3846 \| 0.0732 \| \| No log \| 24.0 \| 168 \| 0.1229 \| 0.805 \| 0.4838 \| 1.0060 \| 0.805 \| 0.7635 \| 0.4268 \| 0.0625 \| \| No log \| 25.0 \| 175 \| 0.1250 \| 0.79 \| 0.4740 \| 0.9769 \| 0.79 \| 0.7462 \| 0.3898 \| 0.0684 \| \| No log \| 26.0 \| 182 \| 0.1371 \| 0.795 \| 0.4784 \| 1.1316 \| 0.795 \| 0.7641 \| 0.4246 \| 0.0732 \| \| No log \| 27.0 \| 189 \| 0.1230 \| 0.77 \| 0.4625 \| 0.9606 \| 0.7700 \| 0.7185 \| 0.3816 \| 0.0712 \| \| No log \| 28.0 \| 196 \| 0.1161 \| 0.775 \| 0.4661 \| 0.9889 \| 0.775 \| 0.7375 \| 0.3925 \| 0.0658 \| \| No log \| 29.0 \| 203 \| 0.1194 \| 0.775 \| 0.4688 \| 1.0280 \| 0.775 \| 0.7320 \| 0.4087 \| 0.0709 \| \| No log \| 30.0 \| 210 \| 0.1211 \| 0.795 \| 0.4680 \| 1.0785 \| 0.795 \| 0.7677 \| 0.4168 \| 0.0671 \| \| No log \| 31.0 \| 217 \| 0.1208 \| 0.79 \| 0.4629 \| 0.9986 \| 0.79 \| 0.7536 \| 0.3892 \| 0.0658 \| \| No log \| 32.0 \| 224 \| 0.1194 \| 0.77 \| 0.4588 \| 0.9202 \| 0.7700 \| 0.7313 \| 0.3791 \| 0.0679 \| \| No log \| 33.0 \| 231 \| 0.1167 \| 0.795 \| 0.4567 \| 0.9374 \| 0.795 \| 0.7602 \| 0.3852 \| 0.0668 \| \| No log \| 34.0 \| 238 \| 0.1205 \| 0.77 \| 0.4653 \| 0.9700 \| 0.7700 \| 0.7291 \| 0.3829 \| 0.0721 \| \| No log \| 35.0 \| 245 \| 0.1179 \| 0.77 \| 0.4616 \| 0.9313 \| 0.7700 \| 0.7366 \| 0.3797 \| 0.0724 \| \| No log \| 36.0 \| 252 \| 0.1155 \| 0.78 \| 0.4566 \| 0.9870 \| 0.78 \| 0.7391 \| 0.3718 \| 0.0661 \| \| No log \| 37.0 \| 259 \| 0.1151 \| 0.785 \| 0.4614 \| 0.8936 \| 0.785 \| 0.7455 \| 0.4010 \| 0.0684 \| \| No log \| 38.0 \| 266 \| 0.1126 \| 0.78 \| 0.4588 \| 0.9190 \| 0.78 \| 0.7406 \| 0.3874 \| 0.0669 \| \| No log \| 39.0 \| 273 \| 0.1139 \| 0.78 \| 0.4637 \| 0.9150 \| 0.78 \| 0.7408 \| 0.3874 \| 0.0708 \| \| No log \| 40.0 \| 280 \| 0.1138 \| 0.785 \| 0.4650 \| 0.9096 \| 0.785 \| 0.7500 \| 0.4002 \| 0.0680 \| \| No log \| 41.0 \| 287 \| 0.1139 \| 0.79 \| 0.4644 \| 0.9092 \| 0.79 \| 0.7590 \| 0.4034 \| 0.0668 \| \| No log \| 42.0 \| 294 \| 0.1140 \| 0.79 \| 0.4670 \| 0.9062 \| 0.79 \| 0.7494 \| 0.4042 \| 0.0665 \| \| No log \| 43.0 \| 301 \| 0.1126 \| 0.785 \| 0.4623 \| 0.8470 \| 0.785 \| 0.7502 \| 0.4079 \| 0.0660 \| \| No log \| 44.0 \| 308 \| 0.1146 \| 0.775 \| 0.4651 \| 0.9065 \| 0.775 \| 0.7314 \| 0.3907 \| 0.0721 \| \| No log \| 45.0 \| 315 \| 0.1122 \| 0.785 \| 0.4643 \| 0.8626 \| 0.785 \| 0.7443 \| 0.3991 \| 0.0639 \| \| No log \| 46.0 \| 322 \| 0.1109 \| 0.795 \| 0.4644 \| 0.9087 \| 0.795 \| 0.7631 \| 0.4017 \| 0.0629 \| \| No log \| 47.0 \| 329 \| 0.1116 \| 0.79 \| 0.4640 \| 0.8473 \| 0.79 \| 0.7584 \| 0.3959 \| 0.0634 \| \| No log \| 48.0 \| 336 \| 0.1147 \| 0.78 \| 0.4662 \| 0.8717 \| 0.78 \| 0.7467 \| 0.3859 \| 0.0677 \| \| No log \| 49.0 \| 343 \| 0.1154 \| 0.765 \| 0.4586 \| 1.0035 \| 0.765 \| 0.7366 \| 0.3826 \| 0.0764 \| \| No log \| 50.0 \| 350 \| 0.1112 \| 0.79 \| 0.4582 \| 0.9230 \| 0.79 \| 0.7525 \| 0.3854 \| 0.0672 \| \| No log \| 51.0 \| 357 \| 0.1104 \| 0.79 \| 0.4633 \| 0.9120 \| 0.79 \| 0.7598 \| 0.4000 \| 0.0667 \| \| No log \| 52.0 \| 364 \| 0.1115 \| 0.79 \| 0.4641 \| 0.8550 \| 0.79 \| 0.7603 \| 0.3914 \| 0.0672 \| \| No log \| 53.0 \| 371 \| 0.1150 \| 0.77 \| 0.4613 \| 0.9215 \| 0.7700 \| 0.7333 \| 0.3882 \| 0.0733 \| \| No log \| 54.0 \| 378 \| 0.1100 \| 0.8 \| 0.4596 \| 0.9149 \| 0.8000 \| 0.7610 \| 0.4055 \| 0.0657 \| \| No log \| 55.0 \| 385 \| 0.1094 \| 0.785 \| 0.4613 \| 0.9060 \| 0.785 \| 0.7506 \| 0.3956 \| 0.0664 \| \| No log \| 56.0 \| 392 \| 0.1087 \| 0.785 \| 0.4607 \| 0.9068 \| 0.785 \| 0.7498 \| 0.3984 \| 0.0649 \| \| No log \| 57.0 \| 399 \| 0.1094 \| 0.785 \| 0.4630 \| 0.8993 \| 0.785 \| 0.7491 \| 0.3943 \| 0.0674 \| \| No log \| 58.0 \| 406 \| 0.1100 \| 0.805 \| 0.4627 \| 0.9130 \| 0.805 \| 0.7693 \| 0.4018 \| 0.0637 \| \| No log \| 59.0 \| 413 \| 0.1103 \| 0.795 \| 0.4619 \| 0.8483 \| 0.795 \| 0.7618 \| 0.3992 \| 0.0632 \| \| No log \| 60.0 \| 420 \| 0.1093 \| 0.79 \| 0.4631 \| 0.9007 \| 0.79 \| 0.7539 \| 0.3936 \| 0.0647 \| \| No log \| 61.0 \| 427 \| 0.1095 \| 0.79 \| 0.4594 \| 0.9073 \| 0.79 \| 0.7539 \| 0.4129 \| 0.0654 \| \| No log \| 62.0 \| 434 \| 0.1092 \| 0.79 \| 0.4591 \| 0.9087 \| 0.79 \| 0.7539 \| 0.3956 \| 0.0638 \| \| No log \| 63.0 \| 441 \| 0.1096 \| 0.79 \| 0.4611 \| 0.9075 \| 0.79 \| 0.7539 \| 0.4088 \| 0.0654 \| \| No log \| 64.0 \| 448 \| 0.1093 \| 0.79 \| 0.4610 \| 0.9041 \| 0.79 \| 0.7539 \| 0.3953 \| 0.0650 \| \| No log \| 65.0 \| 455 \| 0.1092 \| 0.79 \| 0.4602 \| 0.9049 \| 0.79 \| 0.7539 \| 0.3845 \| 0.0642 \| \| No log \| 66.0 \| 462 \| 0.1092 \| 0.79 \| 0.4605 \| 0.9027 \| 0.79 \| 0.7539 \| 0.3870 \| 0.0646 \| \| No log \| 67.0 \| 469 \| 0.1094 \| 0.79 \| 0.4610 \| 0.9047 \| 0.79 \| 0.7539 \| 0.3967 \| 0.0643 \| \| No log \| 68.0 \| 476 \| 0.1094 \| 0.79 \| 0.4602 \| 0.9053 \| 0.79 \| 0.7539 \| 0.3956 \| 0.0645 \| \| No log \| 69.0 \| 483 \| 0.1094 \| 0.79 \| 0.4599 \| 0.9054 \| 0.79 \| 0.7539 \| 0.3950 \| 0.0646 \| \| No log \| 70.0 \| 490 \| 0.1095 \| 0.79 \| 0.4609 \| 0.9036 \| 0.79 \| 0.7539 \| 0.4054 \| 0.0647 \| \| No log \| 71.0 \| 497 \| 0.1095 \| 0.79 \| 0.4601 \| 0.9066 \| 0.79 \| 0.7539 \| 0.3937 \| 0.0646 \| \| 0.1361 \| 72.0 \| 504 \| 0.1095 \| 0.79 \| 0.4602 \| 0.9045 \| 0.79 \| 0.7539 \| 0.3958 \| 0.0644 \| \| 0.1361 \| 73.0 \| 511 \| 0.1095 \| 0.79 \| 0.4605 \| 0.9064 \| 0.79 \| 0.7539 \| 0.3900 \| 0.0645 \| \| 0.1361 \| 74.0 \| 518 \| 0.1095 \| 0.79 \| 0.4606 \| 0.9037 \| 0.79 \| 0.7539 \| 0.4116 \| 0.0642 \| \| 0.1361 \| 75.0 \| 525 \| 0.1095 \| 0.79 \| 0.4606 \| 0.9072 \| 0.79 \| 0.7539 \| 0.4079 \| 0.0646 \| \| 0.1361 \| 76.0 \| 532 \| 0.1096 \| 0.79 \| 0.4604 \| 0.9066 \| 0.79 \| 0.7539 \| 0.4017 \| 0.0644 \| \| 0.1361 \| 77.0 \| 539 \| 0.1095 \| 0.79 \| 0.4603 \| 0.9062 \| 0.79 \| 0.7539 \| 0.4014 \| 0.0646 \| \| 0.1361 \| 78.0 \| 546 \| 0.1096 \| 0.79 \| 0.4600 \| 0.9053 \| 0.79 \| 0.7539 \| 0.3957 \| 0.0644 \| \| 0.1361 \| 79.0 \| 553 \| 0.1096 \| 0.79 \| 0.4606 \| 0.9056 \| 0.79 \| 0.7539 \| 0.3986 \| 0.0645 \| \| 0.1361 \| 80.0 \| 560 \| 0.1097 \| 0.79 \| 0.4602 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4023 \| 0.0647 \| \| 0.1361 \| 81.0 \| 567 \| 0.1096 \| 0.79 \| 0.4604 \| 0.9056 \| 0.79 \| 0.7539 \| 0.4042 \| 0.0645 \| \| 0.1361 \| 82.0 \| 574 \| 0.1097 \| 0.79 \| 0.4603 \| 0.9058 \| 0.79 \| 0.7539 \| 0.4082 \| 0.0646 \| \| 0.1361 \| 83.0 \| 581 \| 0.1097 \| 0.79 \| 0.4606 \| 0.9066 \| 0.79 \| 0.7539 \| 0.4085 \| 0.0645 \| \| 0.1361 \| 84.0 \| 588 \| 0.1097 \| 0.79 \| 0.4603 \| 0.9060 \| 0.79 \| 0.7539 \| 0.4040 \| 0.0645 \| \| 0.1361 \| 85.0 \| 595 \| 0.1097 \| 0.79 \| 0.4606 \| 0.9059 \| 0.79 \| 0.7539 \| 0.3949 \| 0.0645 \| \| 0.1361 \| 86.0 \| 602 \| 0.1097 \| 0.79 \| 0.4603 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4040 \| 0.0645 \| \| 0.1361 \| 87.0 \| 609 \| 0.1097 \| 0.79 \| 0.4605 \| 0.9051 \| 0.79 \| 0.7539 \| 0.4025 \| 0.0644 \| \| 0.1361 \| 88.0 \| 616 \| 0.1097 \| 0.79 \| 0.4605 \| 0.9055 \| 0.79 \| 0.7539 \| 0.3962 \| 0.0643 \| \| 0.1361 \| 89.0 \| 623 \| 0.1097 \| 0.79 \| 0.4603 \| 0.9056 \| 0.79 \| 0.7539 \| 0.4040 \| 0.0643 \| \| 0.1361 \| 90.0 \| 630 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9051 \| 0.79 \| 0.7539 \| 0.3962 \| 0.0643 \| \| 0.1361 \| 91.0 \| 637 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9064 \| 0.79 \| 0.7539 \| 0.4041 \| 0.0644 \| \| 0.1361 \| 92.0 \| 644 \| 0.1098 \| 0.79 \| 0.4605 \| 0.9055 \| 0.79 \| 0.7539 \| 0.4004 \| 0.0644 \| \| 0.1361 \| 93.0 \| 651 \| 0.1098 \| 0.79 \| 0.4605 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4042 \| 0.0644 \| \| 0.1361 \| 94.0 \| 658 \| 0.1098 \| 0.79 \| 0.4603 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4094 \| 0.0643 \| \| 0.1361 \| 95.0 \| 665 \| 0.1098 \| 0.79 \| 0.4605 \| 0.9056 \| 0.79 \| 0.7539 \| 0.4138 \| 0.0645 \| \| 0.1361 \| 96.0 \| 672 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4095 \| 0.0643 \| \| 0.1361 \| 97.0 \| 679 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9057 \| 0.79 \| 0.7539 \| 0.4137 \| 0.0643 \| \| 0.1361 \| 98.0 \| 686 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4096 \| 0.0643 \| \| 0.1361 \| 99.0 \| 693 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9059 \| 0.79 \| 0.7539 \| 0.4137 \| 0.0644 \| \| 0.1361 \| 100.0 \| 700 \| 0.1098 \| 0.79 \| 0.4604 \| 0.9058 \| 0.79 \| 0.7539 \| 0.4083 \| 0.0644 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/60-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 60-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0655 - Accuracy: 0.82 - Brier Loss: 0.2655 - Nll: 1.0777 - F1 Micro: 0.82 - F1 Macro: 0.7953 - Ece: 0.1364 - Aurc: 0.0539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 5.7153 \| 0.09 \| 1.0182 \| 8.6654 \| 0.09 \| 0.0745 \| 0.3226 \| 0.8984 \| \| No log \| 2.0 \| 14 \| 4.7081 \| 0.13 \| 0.9028 \| 8.0605 \| 0.13 \| 0.0949 \| 0.2402 \| 0.8297 \| \| No log \| 3.0 \| 21 \| 4.3197 \| 0.215 \| 0.8419 \| 6.1917 \| 0.2150 \| 0.1338 \| 0.2566 \| 0.5994 \| \| No log \| 4.0 \| 28 \| 4.1502 \| 0.405 \| 0.7493 \| 4.1855 \| 0.405 \| 0.3261 \| 0.3035 \| 0.4065 \| \| No log \| 5.0 \| 35 \| 4.0310 \| 0.545 \| 0.6643 \| 2.9984 \| 0.545 \| 0.4451 \| 0.3080 \| 0.2731 \| \| No log \| 6.0 \| 42 \| 3.9472 \| 0.64 \| 0.6089 \| 2.4473 \| 0.64 \| 0.5047 \| 0.3290 \| 0.1849 \| \| No log \| 7.0 \| 49 \| 3.8616 \| 0.655 \| 0.5435 \| 2.3233 \| 0.655 \| 0.5434 \| 0.3086 \| 0.1536 \| \| No log \| 8.0 \| 56 \| 3.8289 \| 0.72 \| 0.5177 \| 2.1266 \| 0.72 \| 0.6162 \| 0.3496 \| 0.1202 \| \| No log \| 9.0 \| 63 \| 3.7812 \| 0.715 \| 0.4461 \| 1.7718 \| 0.715 \| 0.6276 \| 0.2464 \| 0.1099 \| \| No log \| 10.0 \| 70 \| 3.7599 \| 0.715 \| 0.3960 \| 1.7795 \| 0.715 \| 0.6158 \| 0.2262 \| 0.0879 \| \| No log \| 11.0 \| 77 \| 3.7506 \| 0.74 \| 0.3758 \| 1.9447 \| 0.74 \| 0.6284 \| 0.2212 \| 0.0847 \| \| No log \| 12.0 \| 84 \| 3.7261 \| 0.79 \| 0.3515 \| 1.7839 \| 0.79 \| 0.7107 \| 0.2419 \| 0.0698 \| \| No log \| 13.0 \| 91 \| 3.8045 \| 0.72 \| 0.3765 \| 1.8827 \| 0.72 \| 0.6249 \| 0.1943 \| 0.0941 \| \| No log \| 14.0 \| 98 \| 3.6964 \| 0.79 \| 0.3309 \| 1.8042 \| 0.79 \| 0.6873 \| 0.2162 \| 0.0629 \| \| No log \| 15.0 \| 105 \| 3.7058 \| 0.78 \| 0.3285 \| 1.8229 \| 0.78 \| 0.6827 \| 0.1999 \| 0.0672 \| \| No log \| 16.0 \| 112 \| 3.8395 \| 0.74 \| 0.3502 \| 1.9683 \| 0.74 \| 0.6554 \| 0.1777 \| 0.0880 \| \| No log \| 17.0 \| 119 \| 3.7568 \| 0.795 \| 0.3120 \| 1.3060 \| 0.795 \| 0.7121 \| 0.1656 \| 0.0633 \| \| No log \| 18.0 \| 126 \| 3.7399 \| 0.795 \| 0.3099 \| 1.6225 \| 0.795 \| 0.7133 \| 0.1721 \| 0.0615 \| \| No log \| 19.0 \| 133 \| 3.7813 \| 0.82 \| 0.2884 \| 1.3165 \| 0.82 \| 0.7475 \| 0.1698 \| 0.0565 \| \| No log \| 20.0 \| 140 \| 3.7907 \| 0.8 \| 0.3074 \| 1.2638 \| 0.8000 \| 0.7701 \| 0.1862 \| 0.0654 \| \| No log \| 21.0 \| 147 \| 3.7687 \| 0.83 \| 0.2860 \| 1.2646 \| 0.83 \| 0.8077 \| 0.1730 \| 0.0569 \| \| No log \| 22.0 \| 154 \| 3.7182 \| 0.82 \| 0.2813 \| 1.3271 \| 0.82 \| 0.7848 \| 0.1762 \| 0.0551 \| \| No log \| 23.0 \| 161 \| 3.7801 \| 0.83 \| 0.2679 \| 1.1397 \| 0.83 \| 0.8015 \| 0.1574 \| 0.0568 \| \| No log \| 24.0 \| 168 \| 3.7659 \| 0.82 \| 0.2752 \| 1.1301 \| 0.82 \| 0.7893 \| 0.1581 \| 0.0582 \| \| No log \| 25.0 \| 175 \| 3.7960 \| 0.825 \| 0.2755 \| 1.2565 \| 0.825 \| 0.8017 \| 0.1663 \| 0.0540 \| \| No log \| 26.0 \| 182 \| 3.7630 \| 0.82 \| 0.2715 \| 1.0265 \| 0.82 \| 0.7971 \| 0.1439 \| 0.0536 \| \| No log \| 27.0 \| 189 \| 3.8411 \| 0.82 \| 0.2822 \| 1.2532 \| 0.82 \| 0.7929 \| 0.1621 \| 0.0623 \| \| No log \| 28.0 \| 196 \| 3.7869 \| 0.83 \| 0.2608 \| 1.1781 \| 0.83 \| 0.8013 \| 0.1461 \| 0.0531 \| \| No log \| 29.0 \| 203 \| 3.7798 \| 0.81 \| 0.2724 \| 1.0950 \| 0.81 \| 0.7785 \| 0.1436 \| 0.0562 \| \| No log \| 30.0 \| 210 \| 3.8412 \| 0.82 \| 0.2724 \| 1.3168 \| 0.82 \| 0.7892 \| 0.1473 \| 0.0596 \| \| No log \| 31.0 \| 217 \| 3.7937 \| 0.83 \| 0.2683 \| 1.1818 \| 0.83 \| 0.8137 \| 0.1523 \| 0.0515 \| \| No log \| 32.0 \| 224 \| 3.7964 \| 0.825 \| 0.2655 \| 1.1605 \| 0.825 \| 0.8028 \| 0.1580 \| 0.0551 \| \| No log \| 33.0 \| 231 \| 3.7950 \| 0.81 \| 0.2685 \| 1.2514 \| 0.81 \| 0.7854 \| 0.1275 \| 0.0534 \| \| No log \| 34.0 \| 238 \| 3.8086 \| 0.83 \| 0.2599 \| 0.9670 \| 0.83 \| 0.8022 \| 0.1309 \| 0.0529 \| \| No log \| 35.0 \| 245 \| 3.8393 \| 0.83 \| 0.2649 \| 0.9615 \| 0.83 \| 0.8050 \| 0.1445 \| 0.0543 \| \| No log \| 36.0 \| 252 \| 3.8140 \| 0.84 \| 0.2528 \| 1.0685 \| 0.8400 \| 0.8194 \| 0.1371 \| 0.0528 \| \| No log \| 37.0 \| 259 \| 3.8164 \| 0.825 \| 0.2665 \| 1.0001 \| 0.825 \| 0.8030 \| 0.1501 \| 0.0548 \| \| No log \| 38.0 \| 266 \| 3.8298 \| 0.82 \| 0.2669 \| 1.0166 \| 0.82 \| 0.8007 \| 0.1413 \| 0.0549 \| \| No log \| 39.0 \| 273 \| 3.8346 \| 0.815 \| 0.2646 \| 1.1609 \| 0.815 \| 0.7876 \| 0.1453 \| 0.0551 \| \| No log \| 40.0 \| 280 \| 3.8409 \| 0.825 \| 0.2610 \| 1.1811 \| 0.825 \| 0.8054 \| 0.1432 \| 0.0551 \| \| No log \| 41.0 \| 287 \| 3.8106 \| 0.835 \| 0.2642 \| 1.0262 \| 0.835 \| 0.8078 \| 0.1471 \| 0.0547 \| \| No log \| 42.0 \| 294 \| 3.8402 \| 0.81 \| 0.2591 \| 0.9683 \| 0.81 \| 0.7863 \| 0.1638 \| 0.0546 \| \| No log \| 43.0 \| 301 \| 3.8480 \| 0.82 \| 0.2604 \| 1.1408 \| 0.82 \| 0.7933 \| 0.1448 \| 0.0538 \| \| No log \| 44.0 \| 308 \| 3.8627 \| 0.835 \| 0.2717 \| 1.0142 \| 0.835 \| 0.8113 \| 0.1472 \| 0.0550 \| \| No log \| 45.0 \| 315 \| 3.8274 \| 0.83 \| 0.2589 \| 1.0148 \| 0.83 \| 0.8138 \| 0.1333 \| 0.0518 \| \| No log \| 46.0 \| 322 \| 3.8349 \| 0.83 \| 0.2563 \| 0.9773 \| 0.83 \| 0.8086 \| 0.1237 \| 0.0537 \| \| No log \| 47.0 \| 329 \| 3.8656 \| 0.82 \| 0.2668 \| 0.9728 \| 0.82 \| 0.7947 \| 0.1330 \| 0.0546 \| \| No log \| 48.0 \| 336 \| 3.8455 \| 0.84 \| 0.2514 \| 0.9784 \| 0.8400 \| 0.8175 \| 0.1519 \| 0.0509 \| \| No log \| 49.0 \| 343 \| 3.8827 \| 0.81 \| 0.2740 \| 0.9515 \| 0.81 \| 0.7852 \| 0.1325 \| 0.0565 \| \| No log \| 50.0 \| 350 \| 3.8617 \| 0.815 \| 0.2659 \| 1.2129 \| 0.815 \| 0.7885 \| 0.1430 \| 0.0565 \| \| No log \| 51.0 \| 357 \| 3.8555 \| 0.825 \| 0.2601 \| 0.9544 \| 0.825 \| 0.8023 \| 0.1500 \| 0.0535 \| \| No log \| 52.0 \| 364 \| 3.8794 \| 0.825 \| 0.2611 \| 1.1499 \| 0.825 \| 0.7998 \| 0.1520 \| 0.0550 \| \| No log \| 53.0 \| 371 \| 3.8563 \| 0.815 \| 0.2592 \| 1.0856 \| 0.815 \| 0.7896 \| 0.1198 \| 0.0525 \| \| No log \| 54.0 \| 378 \| 3.8851 \| 0.825 \| 0.2579 \| 0.8909 \| 0.825 \| 0.8042 \| 0.1320 \| 0.0543 \| \| No log \| 55.0 \| 385 \| 3.8682 \| 0.815 \| 0.2589 \| 1.1381 \| 0.815 \| 0.7906 \| 0.1376 \| 0.0553 \| \| No log \| 56.0 \| 392 \| 3.8646 \| 0.83 \| 0.2582 \| 1.0602 \| 0.83 \| 0.8144 \| 0.1330 \| 0.0532 \| \| No log \| 57.0 \| 399 \| 3.9205 \| 0.82 \| 0.2678 \| 1.0739 \| 0.82 \| 0.7991 \| 0.1403 \| 0.0569 \| \| No log \| 58.0 \| 406 \| 3.8878 \| 0.835 \| 0.2613 \| 0.9320 \| 0.835 \| 0.8112 \| 0.1575 \| 0.0527 \| \| No log \| 59.0 \| 413 \| 3.8944 \| 0.82 \| 0.2662 \| 1.0116 \| 0.82 \| 0.7966 \| 0.1304 \| 0.0544 \| \| No log \| 60.0 \| 420 \| 3.8819 \| 0.825 \| 0.2597 \| 1.1330 \| 0.825 \| 0.8019 \| 0.1471 \| 0.0529 \| \| No log \| 61.0 \| 427 \| 3.9219 \| 0.82 \| 0.2719 \| 1.0206 \| 0.82 \| 0.7957 \| 0.1465 \| 0.0563 \| \| No log \| 62.0 \| 434 \| 3.8903 \| 0.815 \| 0.2583 \| 0.9983 \| 0.815 \| 0.7945 \| 0.1428 \| 0.0532 \| \| No log \| 63.0 \| 441 \| 3.9258 \| 0.82 \| 0.2674 \| 1.0722 \| 0.82 \| 0.7941 \| 0.1312 \| 0.0552 \| \| No log \| 64.0 \| 448 \| 3.9086 \| 0.83 \| 0.2546 \| 1.1474 \| 0.83 \| 0.8049 \| 0.1375 \| 0.0522 \| \| No log \| 65.0 \| 455 \| 3.9260 \| 0.83 \| 0.2655 \| 1.0795 \| 0.83 \| 0.8074 \| 0.1300 \| 0.0537 \| \| No log \| 66.0 \| 462 \| 3.9424 \| 0.82 \| 0.2634 \| 1.0220 \| 0.82 \| 0.7978 \| 0.1298 \| 0.0542 \| \| No log \| 67.0 \| 469 \| 3.9315 \| 0.815 \| 0.2605 \| 1.0206 \| 0.815 \| 0.7882 \| 0.1404 \| 0.0534 \| \| No log \| 68.0 \| 476 \| 3.9583 \| 0.82 \| 0.2634 \| 1.0018 \| 0.82 \| 0.7991 \| 0.1321 \| 0.0532 \| \| No log \| 69.0 \| 483 \| 3.9447 \| 0.825 \| 0.2606 \| 1.0840 \| 0.825 \| 0.8003 \| 0.1326 \| 0.0533 \| \| No log \| 70.0 \| 490 \| 3.9698 \| 0.815 \| 0.2646 \| 0.9721 \| 0.815 \| 0.7940 \| 0.1359 \| 0.0546 \| \| No log \| 71.0 \| 497 \| 3.9621 \| 0.82 \| 0.2615 \| 1.0207 \| 0.82 \| 0.7953 \| 0.1312 \| 0.0540 \| \| 3.4582 \| 72.0 \| 504 \| 3.9687 \| 0.81 \| 0.2628 \| 1.0078 \| 0.81 \| 0.7861 \| 0.1379 \| 0.0549 \| \| 3.4582 \| 73.0 \| 511 \| 3.9699 \| 0.825 \| 0.2575 \| 1.0739 \| 0.825 \| 0.8024 \| 0.1257 \| 0.0534 \| \| 3.4582 \| 74.0 \| 518 \| 3.9791 \| 0.82 \| 0.2638 \| 1.0290 \| 0.82 \| 0.7978 \| 0.1337 \| 0.0543 \| \| 3.4582 \| 75.0 \| 525 \| 3.9893 \| 0.82 \| 0.2664 \| 1.0711 \| 0.82 \| 0.7978 \| 0.1323 \| 0.0543 \| \| 3.4582 \| 76.0 \| 532 \| 3.9843 \| 0.825 \| 0.2620 \| 0.9559 \| 0.825 \| 0.7999 \| 0.1307 \| 0.0535 \| \| 3.4582 \| 77.0 \| 539 \| 3.9983 \| 0.82 \| 0.2626 \| 1.0239 \| 0.82 \| 0.7953 \| 0.1333 \| 0.0543 \| \| 3.4582 \| 78.0 \| 546 \| 4.0068 \| 0.82 \| 0.2663 \| 1.0205 \| 0.82 \| 0.7953 \| 0.1421 \| 0.0542 \| \| 3.4582 \| 79.0 \| 553 \| 4.0096 \| 0.82 \| 0.2636 \| 1.0204 \| 0.82 \| 0.7953 \| 0.1296 \| 0.0541 \| \| 3.4582 \| 80.0 \| 560 \| 4.0211 \| 0.82 \| 0.2655 \| 1.0147 \| 0.82 \| 0.7953 \| 0.1365 \| 0.0541 \| \| 3.4582 \| 81.0 \| 567 \| 4.0206 \| 0.815 \| 0.2631 \| 1.0781 \| 0.815 \| 0.7902 \| 0.1378 \| 0.0543 \| \| 3.4582 \| 82.0 \| 574 \| 4.0270 \| 0.82 \| 0.2652 \| 1.0752 \| 0.82 \| 0.7953 \| 0.1407 \| 0.0539 \| \| 3.4582 \| 83.0 \| 581 \| 4.0301 \| 0.82 \| 0.2642 \| 1.0763 \| 0.82 \| 0.7953 \| 0.1444 \| 0.0537 \| \| 3.4582 \| 84.0 \| 588 \| 4.0369 \| 0.82 \| 0.2652 \| 1.0758 \| 0.82 \| 0.7953 \| 0.1355 \| 0.0539 \| \| 3.4582 \| 85.0 \| 595 \| 4.0395 \| 0.82 \| 0.2648 \| 1.0244 \| 0.82 \| 0.7953 \| 0.1300 \| 0.0539 \| \| 3.4582 \| 86.0 \| 602 \| 4.0428 \| 0.82 \| 0.2651 \| 1.0768 \| 0.82 \| 0.7953 \| 0.1209 \| 0.0538 \| \| 3.4582 \| 87.0 \| 609 \| 4.0461 \| 0.82 \| 0.2653 \| 1.0780 \| 0.82 \| 0.7953 \| 0.1295 \| 0.0536 \| \| 3.4582 \| 88.0 \| 616 \| 4.0485 \| 0.82 \| 0.2650 \| 1.0780 \| 0.82 \| 0.7953 \| 0.1375 \| 0.0537 \| \| 3.4582 \| 89.0 \| 623 \| 4.0521 \| 0.82 \| 0.2652 \| 1.0259 \| 0.82 \| 0.7953 \| 0.1296 \| 0.0537 \| \| 3.4582 \| 90.0 \| 630 \| 4.0540 \| 0.82 \| 0.2652 \| 1.0466 \| 0.82 \| 0.7953 \| 0.1282 \| 0.0538 \| \| 3.4582 \| 91.0 \| 637 \| 4.0562 \| 0.82 \| 0.2653 \| 1.0778 \| 0.82 \| 0.7953 \| 0.1304 \| 0.0538 \| \| 3.4582 \| 92.0 \| 644 \| 4.0587 \| 0.82 \| 0.2655 \| 1.0774 \| 0.82 \| 0.7953 \| 0.1241 \| 0.0537 \| \| 3.4582 \| 93.0 \| 651 \| 4.0607 \| 0.82 \| 0.2657 \| 1.0771 \| 0.82 \| 0.7953 \| 0.1357 \| 0.0538 \| \| 3.4582 \| 94.0 \| 658 \| 4.0615 \| 0.82 \| 0.2655 \| 1.0775 \| 0.82 \| 0.7953 \| 0.1268 \| 0.0538 \| \| 3.4582 \| 95.0 \| 665 \| 4.0625 \| 0.82 \| 0.2654 \| 1.0300 \| 0.82 \| 0.7953 \| 0.1211 \| 0.0538 \| \| 3.4582 \| 96.0 \| 672 \| 4.0641 \| 0.82 \| 0.2657 \| 1.0771 \| 0.82 \| 0.7953 \| 0.1365 \| 0.0538 \| \| 3.4582 \| 97.0 \| 679 \| 4.0641 \| 0.82 \| 0.2654 \| 1.0312 \| 0.82 \| 0.7953 \| 0.1210 \| 0.0538 \| \| 3.4582 \| 98.0 \| 686 \| 4.0652 \| 0.82 \| 0.2656 \| 1.0776 \| 0.82 \| 0.7953 \| 0.1269 \| 0.0538 \| \| 3.4582 \| 99.0 \| 693 \| 4.0654 \| 0.82 \| 0.2656 \| 1.0775 \| 0.82 \| 0.7953 \| 0.1364 \| 0.0539 \| \| 3.4582 \| 100.0 \| 700 \| 4.0655 \| 0.82 \| 0.2655 \| 1.0777 \| 0.82 \| 0.7953 \| 0.1364 \| 0.0539 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/60-tiny_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 60-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 63.5396 - Accuracy: 0.84 - Brier Loss: 0.3043 - Nll: 1.1495 - F1 Micro: 0.8400 - F1 Macro: 0.8244 - Ece: 0.1568 - Aurc: 0.0457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 66.6267 \| 0.26 \| 0.8706 \| 4.9000 \| 0.26 \| 0.1920 \| 0.2904 \| 0.7812 \| \| No log \| 2.0 \| 50 \| 65.8408 \| 0.54 \| 0.5951 \| 2.8543 \| 0.54 \| 0.4535 \| 0.2542 \| 0.2567 \| \| No log \| 3.0 \| 75 \| 65.3708 \| 0.675 \| 0.4400 \| 1.6094 \| 0.675 \| 0.6134 \| 0.2395 \| 0.1333 \| \| No log \| 4.0 \| 100 \| 65.0889 \| 0.76 \| 0.3809 \| 1.5505 \| 0.76 \| 0.7422 \| 0.2333 \| 0.1125 \| \| No log \| 5.0 \| 125 \| 64.7800 \| 0.8 \| 0.3080 \| 1.7523 \| 0.8000 \| 0.7663 \| 0.1734 \| 0.0708 \| \| No log \| 6.0 \| 150 \| 64.6296 \| 0.78 \| 0.3286 \| 1.7771 \| 0.78 \| 0.7427 \| 0.1752 \| 0.0642 \| \| No log \| 7.0 \| 175 \| 64.3879 \| 0.765 \| 0.3584 \| 1.7400 \| 0.765 \| 0.6986 \| 0.1799 \| 0.0937 \| \| No log \| 8.0 \| 200 \| 64.4361 \| 0.72 \| 0.4640 \| 1.4368 \| 0.72 \| 0.7385 \| 0.2350 \| 0.1314 \| \| No log \| 9.0 \| 225 \| 64.2223 \| 0.76 \| 0.3846 \| 1.6420 \| 0.76 \| 0.7417 \| 0.2006 \| 0.0915 \| \| No log \| 10.0 \| 250 \| 64.2618 \| 0.725 \| 0.4268 \| 1.6667 \| 0.7250 \| 0.7132 \| 0.2131 \| 0.1110 \| \| No log \| 11.0 \| 275 \| 64.2839 \| 0.7 \| 0.4830 \| 1.7975 \| 0.7 \| 0.6829 \| 0.2213 \| 0.1207 \| \| No log \| 12.0 \| 300 \| 64.0218 \| 0.785 \| 0.3523 \| 1.7098 \| 0.785 \| 0.7363 \| 0.1742 \| 0.0702 \| \| No log \| 13.0 \| 325 \| 63.8071 \| 0.78 \| 0.3218 \| 1.4587 \| 0.78 \| 0.7574 \| 0.1640 \| 0.0674 \| \| No log \| 14.0 \| 350 \| 64.7387 \| 0.645 \| 0.5871 \| 2.0188 \| 0.645 \| 0.6360 \| 0.2996 \| 0.1765 \| \| No log \| 15.0 \| 375 \| 64.2173 \| 0.765 \| 0.3832 \| 1.8093 \| 0.765 \| 0.6909 \| 0.1978 \| 0.0892 \| \| No log \| 16.0 \| 400 \| 64.2233 \| 0.765 \| 0.3897 \| 1.4456 \| 0.765 \| 0.7432 \| 0.1983 \| 0.0805 \| \| No log \| 17.0 \| 425 \| 63.7977 \| 0.825 \| 0.2971 \| 1.4248 \| 0.825 \| 0.8057 \| 0.1583 \| 0.0546 \| \| No log \| 18.0 \| 450 \| 63.5818 \| 0.82 \| 0.2983 \| 1.3079 \| 0.82 \| 0.7936 \| 0.1532 \| 0.0474 \| \| No log \| 19.0 \| 475 \| 64.1935 \| 0.78 \| 0.3764 \| 1.6662 \| 0.78 \| 0.7618 \| 0.1911 \| 0.0669 \| \| 63.0313 \| 20.0 \| 500 \| 63.6054 \| 0.825 \| 0.2871 \| 1.4054 \| 0.825 \| 0.8118 \| 0.1605 \| 0.0520 \| \| 63.0313 \| 21.0 \| 525 \| 63.6316 \| 0.79 \| 0.3258 \| 1.3131 \| 0.79 \| 0.7714 \| 0.1632 \| 0.0485 \| \| 63.0313 \| 22.0 \| 550 \| 63.6978 \| 0.84 \| 0.2935 \| 1.2425 \| 0.8400 \| 0.8236 \| 0.1508 \| 0.0586 \| \| 63.0313 \| 23.0 \| 575 \| 63.8266 \| 0.825 \| 0.3117 \| 1.5766 \| 0.825 \| 0.8019 \| 0.1550 \| 0.0554 \| \| 63.0313 \| 24.0 \| 600 \| 63.6750 \| 0.825 \| 0.3130 \| 1.1848 \| 0.825 \| 0.8158 \| 0.1553 \| 0.0462 \| \| 63.0313 \| 25.0 \| 625 \| 63.8469 \| 0.82 \| 0.3259 \| 1.3997 \| 0.82 \| 0.8007 \| 0.1603 \| 0.0564 \| \| 63.0313 \| 26.0 \| 650 \| 63.7656 \| 0.815 \| 0.3285 \| 1.2752 \| 0.815 \| 0.7969 \| 0.1656 \| 0.0535 \| \| 63.0313 \| 27.0 \| 675 \| 63.8074 \| 0.805 \| 0.3455 \| 1.1282 \| 0.805 \| 0.7870 \| 0.1732 \| 0.0542 \| \| 63.0313 \| 28.0 \| 700 \| 63.8411 \| 0.81 \| 0.3437 \| 1.1501 \| 0.81 \| 0.7917 \| 0.1759 \| 0.0529 \| \| 63.0313 \| 29.0 \| 725 \| 63.8158 \| 0.81 \| 0.3345 \| 1.1519 \| 0.81 \| 0.7901 \| 0.1706 \| 0.0544 \| \| 63.0313 \| 30.0 \| 750 \| 63.7917 \| 0.815 \| 0.3383 \| 1.2013 \| 0.815 \| 0.8006 \| 0.1706 \| 0.0557 \| \| 63.0313 \| 31.0 \| 775 \| 63.7855 \| 0.815 \| 0.3396 \| 1.2088 \| 0.815 \| 0.7974 \| 0.1687 \| 0.0551 \| \| 63.0313 \| 32.0 \| 800 \| 63.8003 \| 0.825 \| 0.3297 \| 1.2233 \| 0.825 \| 0.8091 \| 0.1694 \| 0.0547 \| \| 63.0313 \| 33.0 \| 825 \| 63.8029 \| 0.815 \| 0.3405 \| 1.2628 \| 0.815 \| 0.8007 \| 0.1729 \| 0.0547 \| \| 63.0313 \| 34.0 \| 850 \| 63.7752 \| 0.81 \| 0.3352 \| 1.2587 \| 0.81 \| 0.7979 \| 0.1727 \| 0.0574 \| \| 63.0313 \| 35.0 \| 875 \| 63.7800 \| 0.815 \| 0.3346 \| 1.1948 \| 0.815 \| 0.7977 \| 0.1679 \| 0.0560 \| \| 63.0313 \| 36.0 \| 900 \| 63.7885 \| 0.825 \| 0.3313 \| 1.2728 \| 0.825 \| 0.8173 \| 0.1591 \| 0.0569 \| \| 63.0313 \| 37.0 \| 925 \| 63.7730 \| 0.815 \| 0.3354 \| 1.2726 \| 0.815 \| 0.8027 \| 0.1689 \| 0.0555 \| \| 63.0313 \| 38.0 \| 950 \| 63.8327 \| 0.815 \| 0.3405 \| 1.4350 \| 0.815 \| 0.8043 \| 0.1675 \| 0.0632 \| \| 63.0313 \| 39.0 \| 975 \| 63.7324 \| 0.785 \| 0.3686 \| 1.6439 \| 0.785 \| 0.7745 \| 0.1808 \| 0.0666 \| \| 61.6786 \| 40.0 \| 1000 \| 63.8625 \| 0.765 \| 0.3946 \| 1.6127 \| 0.765 \| 0.7727 \| 0.1961 \| 0.0723 \| \| 61.6786 \| 41.0 \| 1025 \| 64.1254 \| 0.765 \| 0.3904 \| 1.5456 \| 0.765 \| 0.7570 \| 0.2020 \| 0.0850 \| \| 61.6786 \| 42.0 \| 1050 \| 63.6201 \| 0.78 \| 0.3728 \| 1.4198 \| 0.78 \| 0.7447 \| 0.1869 \| 0.0647 \| \| 61.6786 \| 43.0 \| 1075 \| 63.6033 \| 0.835 \| 0.2968 \| 1.5430 \| 0.835 \| 0.8059 \| 0.1574 \| 0.0479 \| \| 61.6786 \| 44.0 \| 1100 \| 63.6777 \| 0.795 \| 0.3606 \| 1.3542 \| 0.795 \| 0.7638 \| 0.1806 \| 0.0529 \| \| 61.6786 \| 45.0 \| 1125 \| 63.5747 \| 0.83 \| 0.2996 \| 1.5403 \| 0.83 \| 0.8079 \| 0.1450 \| 0.0504 \| \| 61.6786 \| 46.0 \| 1150 \| 63.6022 \| 0.805 \| 0.3389 \| 1.3842 \| 0.805 \| 0.7791 \| 0.1794 \| 0.0466 \| \| 61.6786 \| 47.0 \| 1175 \| 63.6342 \| 0.81 \| 0.3346 \| 1.2861 \| 0.81 \| 0.7811 \| 0.1678 \| 0.0476 \| \| 61.6786 \| 48.0 \| 1200 \| 63.6065 \| 0.81 \| 0.3298 \| 1.2911 \| 0.81 \| 0.7807 \| 0.1654 \| 0.0465 \| \| 61.6786 \| 49.0 \| 1225 \| 63.5937 \| 0.815 \| 0.3260 \| 1.3576 \| 0.815 \| 0.7844 \| 0.1613 \| 0.0467 \| \| 61.6786 \| 50.0 \| 1250 \| 63.6029 \| 0.815 \| 0.3241 \| 1.2826 \| 0.815 \| 0.7844 \| 0.1662 \| 0.0467 \| \| 61.6786 \| 51.0 \| 1275 \| 63.5947 \| 0.81 \| 0.3232 \| 1.4156 \| 0.81 \| 0.7789 \| 0.1631 \| 0.0471 \| \| 61.6786 \| 52.0 \| 1300 \| 63.6501 \| 0.81 \| 0.3268 \| 1.4148 \| 0.81 \| 0.7785 \| 0.1703 \| 0.0468 \| \| 61.6786 \| 53.0 \| 1325 \| 63.6207 \| 0.81 \| 0.3207 \| 1.2785 \| 0.81 \| 0.7785 \| 0.1698 \| 0.0479 \| \| 61.6786 \| 54.0 \| 1350 \| 63.6021 \| 0.815 \| 0.3233 \| 1.3519 \| 0.815 \| 0.7818 \| 0.1629 \| 0.0456 \| \| 61.6786 \| 55.0 \| 1375 \| 63.6128 \| 0.815 \| 0.3207 \| 1.2837 \| 0.815 \| 0.7818 \| 0.1641 \| 0.0474 \| \| 61.6786 \| 56.0 \| 1400 \| 63.5974 \| 0.81 \| 0.3194 \| 1.3542 \| 0.81 \| 0.7789 \| 0.1679 \| 0.0474 \| \| 61.6786 \| 57.0 \| 1425 \| 63.6173 \| 0.81 \| 0.3260 \| 1.2907 \| 0.81 \| 0.7761 \| 0.1653 \| 0.0486 \| \| 61.6786 \| 58.0 \| 1450 \| 63.6057 \| 0.81 \| 0.3163 \| 1.2981 \| 0.81 \| 0.7789 \| 0.1651 \| 0.0471 \| \| 61.6786 \| 59.0 \| 1475 \| 63.6052 \| 0.81 \| 0.3197 \| 1.3444 \| 0.81 \| 0.7789 \| 0.1680 \| 0.0467 \| \| 61.52 \| 60.0 \| 1500 \| 63.5865 \| 0.82 \| 0.3143 \| 1.2748 \| 0.82 \| 0.7920 \| 0.1617 \| 0.0465 \| \| 61.52 \| 61.0 \| 1525 \| 63.5754 \| 0.82 \| 0.3126 \| 1.2677 \| 0.82 \| 0.7920 \| 0.1595 \| 0.0468 \| \| 61.52 \| 62.0 \| 1550 \| 63.5876 \| 0.815 \| 0.3120 \| 1.2691 \| 0.815 \| 0.7879 \| 0.1567 \| 0.0478 \| \| 61.52 \| 63.0 \| 1575 \| 63.6040 \| 0.82 \| 0.3110 \| 1.2632 \| 0.82 \| 0.7920 \| 0.1526 \| 0.0472 \| \| 61.52 \| 64.0 \| 1600 \| 63.5956 \| 0.82 \| 0.3111 \| 1.1976 \| 0.82 \| 0.7963 \| 0.1592 \| 0.0468 \| \| 61.52 \| 65.0 \| 1625 \| 63.5792 \| 0.815 \| 0.3095 \| 1.1928 \| 0.815 \| 0.7879 \| 0.1571 \| 0.0469 \| \| 61.52 \| 66.0 \| 1650 \| 63.5704 \| 0.82 \| 0.3086 \| 1.2509 \| 0.82 \| 0.7936 \| 0.1543 \| 0.0467 \| \| 61.52 \| 67.0 \| 1675 \| 63.5918 \| 0.82 \| 0.3118 \| 1.2536 \| 0.82 \| 0.7936 \| 0.1619 \| 0.0471 \| \| 61.52 \| 68.0 \| 1700 \| 63.5741 \| 0.82 \| 0.3072 \| 1.2491 \| 0.82 \| 0.7963 \| 0.1562 \| 0.0465 \| \| 61.52 \| 69.0 \| 1725 \| 63.5581 \| 0.825 \| 0.3085 \| 1.2490 \| 0.825 \| 0.8021 \| 0.1566 \| 0.0460 \| \| 61.52 \| 70.0 \| 1750 \| 63.5796 \| 0.82 \| 0.3087 \| 1.2456 \| 0.82 \| 0.7963 \| 0.1556 \| 0.0471 \| \| 61.52 \| 71.0 \| 1775 \| 63.5776 \| 0.825 \| 0.3073 \| 1.2530 \| 0.825 \| 0.8021 \| 0.1571 \| 0.0474 \| \| 61.52 \| 72.0 \| 1800 \| 63.5524 \| 0.825 \| 0.3064 \| 1.2402 \| 0.825 \| 0.8021 \| 0.1555 \| 0.0465 \| \| 61.52 \| 73.0 \| 1825 \| 63.5638 \| 0.825 \| 0.3075 \| 1.2465 \| 0.825 \| 0.8021 \| 0.1607 \| 0.0466 \| \| 61.52 \| 74.0 \| 1850 \| 63.5654 \| 0.82 \| 0.3058 \| 1.2425 \| 0.82 \| 0.7963 \| 0.1552 \| 0.0468 \| \| 61.52 \| 75.0 \| 1875 \| 63.5654 \| 0.825 \| 0.3041 \| 1.2439 \| 0.825 \| 0.8021 \| 0.1563 \| 0.0466 \| \| 61.52 \| 76.0 \| 1900 \| 63.5499 \| 0.83 \| 0.3018 \| 1.2432 \| 0.83 \| 0.8082 \| 0.1541 \| 0.0463 \| \| 61.52 \| 77.0 \| 1925 \| 63.5563 \| 0.825 \| 0.3059 \| 1.2385 \| 0.825 \| 0.8021 \| 0.1570 \| 0.0466 \| \| 61.52 \| 78.0 \| 1950 \| 63.5524 \| 0.825 \| 0.3045 \| 1.2364 \| 0.825 \| 0.8021 \| 0.1524 \| 0.0464 \| \| 61.52 \| 79.0 \| 1975 \| 63.5507 \| 0.825 \| 0.3064 \| 1.2344 \| 0.825 \| 0.8021 \| 0.1523 \| 0.0463 \| \| 61.4257 \| 80.0 \| 2000 \| 63.5531 \| 0.825 \| 0.3062 \| 1.2266 \| 0.825 \| 0.8035 \| 0.1625 \| 0.0463 \| \| 61.4257 \| 81.0 \| 2025 \| 63.5486 \| 0.825 \| 0.3029 \| 1.1850 \| 0.825 \| 0.8024 \| 0.1506 \| 0.0463 \| \| 61.4257 \| 82.0 \| 2050 \| 63.5479 \| 0.82 \| 0.3081 \| 1.2269 \| 0.82 \| 0.7963 \| 0.1588 \| 0.0458 \| \| 61.4257 \| 83.0 \| 2075 \| 63.5444 \| 0.835 \| 0.3029 \| 1.1721 \| 0.835 \| 0.8139 \| 0.1475 \| 0.0461 \| \| 61.4257 \| 84.0 \| 2100 \| 63.5435 \| 0.835 \| 0.3047 \| 1.2306 \| 0.835 \| 0.8171 \| 0.1529 \| 0.0464 \| \| 61.4257 \| 85.0 \| 2125 \| 63.5393 \| 0.83 \| 0.3058 \| 1.2255 \| 0.83 \| 0.8081 \| 0.1462 \| 0.0464 \| \| 61.4257 \| 86.0 \| 2150 \| 63.5437 \| 0.835 \| 0.3048 \| 1.2254 \| 0.835 \| 0.8171 \| 0.1481 \| 0.0464 \| \| 61.4257 \| 87.0 \| 2175 \| 63.5463 \| 0.83 \| 0.3039 \| 1.1549 \| 0.83 \| 0.8115 \| 0.1562 \| 0.0463 \| \| 61.4257 \| 88.0 \| 2200 \| 63.5408 \| 0.835 \| 0.3055 \| 1.2211 \| 0.835 \| 0.8187 \| 0.1485 \| 0.0462 \| \| 61.4257 \| 89.0 \| 2225 \| 63.5477 \| 0.825 \| 0.3054 \| 1.1541 \| 0.825 \| 0.8024 \| 0.1521 \| 0.0463 \| \| 61.4257 \| 90.0 \| 2250 \| 63.5383 \| 0.83 \| 0.3051 \| 1.1577 \| 0.83 \| 0.8095 \| 0.1532 \| 0.0463 \| \| 61.4257 \| 91.0 \| 2275 \| 63.5466 \| 0.84 \| 0.3057 \| 1.1583 \| 0.8400 \| 0.8244 \| 0.1516 \| 0.0458 \| \| 61.4257 \| 92.0 \| 2300 \| 63.5447 \| 0.835 \| 0.3049 \| 1.1518 \| 0.835 \| 0.8188 \| 0.1615 \| 0.0462 \| \| 61.4257 \| 93.0 \| 2325 \| 63.5327 \| 0.84 \| 0.3044 \| 1.1540 \| 0.8400 \| 0.8244 \| 0.1508 \| 0.0459 \| \| 61.4257 \| 94.0 \| 2350 \| 63.5392 \| 0.84 \| 0.3046 \| 1.1506 \| 0.8400 \| 0.8244 \| 0.1569 \| 0.0459 \| \| 61.4257 \| 95.0 \| 2375 \| 63.5305 \| 0.835 \| 0.3050 \| 1.1520 \| 0.835 \| 0.8188 \| 0.1571 \| 0.0457 \| \| 61.4257 \| 96.0 \| 2400 \| 63.5413 \| 0.835 \| 0.3042 \| 1.1494 \| 0.835 \| 0.8188 \| 0.1571 \| 0.0461 \| \| 61.4257 \| 97.0 \| 2425 \| 63.5387 \| 0.835 \| 0.3047 \| 1.1489 \| 0.835 \| 0.8188 \| 0.1652 \| 0.0461 \| \| 61.4257 \| 98.0 \| 2450 \| 63.5383 \| 0.84 \| 0.3046 \| 1.1503 \| 0.8400 \| 0.8244 \| 0.1568 \| 0.0458 \| \| 61.4257 \| 99.0 \| 2475 \| 63.5374 \| 0.835 \| 0.3045 \| 1.1489 \| 0.835 \| 0.8188 \| 0.1570 \| 0.0456 \| \| 61.3919 \| 100.0 \| 2500 \| 63.5396 \| 0.84 \| 0.3043 \| 1.1495 \| 0.8400 \| 0.8244 \| 0.1568 \| 0.0457 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/39-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 39-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5285 - Accuracy: 0.81 - Brier Loss: 0.3907 - Nll: 0.9159 - F1 Micro: 0.81 - F1 Macro: 0.7907 - Ece: 0.3421 - Aurc: 0.0542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.8936 \| 0.11 \| 1.0097 \| 8.5078 \| 0.11 \| 0.0902 \| 0.3251 \| 0.8953 \| \| No log \| 2.0 \| 14 \| 1.2597 \| 0.16 \| 0.8753 \| 5.5353 \| 0.16 \| 0.1308 \| 0.2539 \| 0.7902 \| \| No log \| 3.0 \| 21 \| 1.0230 \| 0.355 \| 0.7833 \| 5.1396 \| 0.3550 \| 0.2872 \| 0.2810 \| 0.4521 \| \| No log \| 4.0 \| 28 \| 0.8743 \| 0.545 \| 0.6497 \| 3.0412 \| 0.545 \| 0.4328 \| 0.3224 \| 0.2775 \| \| No log \| 5.0 \| 35 \| 0.8020 \| 0.625 \| 0.5958 \| 2.6099 \| 0.625 \| 0.5465 \| 0.3186 \| 0.2136 \| \| No log \| 6.0 \| 42 \| 0.7221 \| 0.675 \| 0.5300 \| 2.3085 \| 0.675 \| 0.5632 \| 0.3257 \| 0.1562 \| \| No log \| 7.0 \| 49 \| 0.6964 \| 0.68 \| 0.4843 \| 1.9033 \| 0.68 \| 0.5761 \| 0.3039 \| 0.1453 \| \| No log \| 8.0 \| 56 \| 0.6729 \| 0.72 \| 0.4598 \| 1.8200 \| 0.72 \| 0.6195 \| 0.3089 \| 0.1170 \| \| No log \| 9.0 \| 63 \| 0.6470 \| 0.77 \| 0.4318 \| 1.5607 \| 0.7700 \| 0.7058 \| 0.3518 \| 0.0897 \| \| No log \| 10.0 \| 70 \| 0.5889 \| 0.795 \| 0.4019 \| 1.1238 \| 0.795 \| 0.7546 \| 0.3324 \| 0.0675 \| \| No log \| 11.0 \| 77 \| 0.5829 \| 0.795 \| 0.4013 \| 1.0267 \| 0.795 \| 0.7667 \| 0.3142 \| 0.0728 \| \| No log \| 12.0 \| 84 \| 0.5763 \| 0.785 \| 0.3923 \| 1.2697 \| 0.785 \| 0.7655 \| 0.3286 \| 0.0751 \| \| No log \| 13.0 \| 91 \| 0.5854 \| 0.765 \| 0.3934 \| 1.4915 \| 0.765 \| 0.7291 \| 0.2936 \| 0.0806 \| \| No log \| 14.0 \| 98 \| 0.5779 \| 0.795 \| 0.3983 \| 1.2207 \| 0.795 \| 0.7409 \| 0.3141 \| 0.0681 \| \| No log \| 15.0 \| 105 \| 0.5564 \| 0.795 \| 0.3752 \| 1.1974 \| 0.795 \| 0.7687 \| 0.3201 \| 0.0626 \| \| No log \| 16.0 \| 112 \| 0.5599 \| 0.815 \| 0.3945 \| 1.0987 \| 0.815 \| 0.7827 \| 0.3233 \| 0.0618 \| \| No log \| 17.0 \| 119 \| 0.5748 \| 0.77 \| 0.4001 \| 1.2395 \| 0.7700 \| 0.7497 \| 0.3136 \| 0.0866 \| \| No log \| 18.0 \| 126 \| 0.5611 \| 0.79 \| 0.4028 \| 1.3279 \| 0.79 \| 0.7738 \| 0.3127 \| 0.0680 \| \| No log \| 19.0 \| 133 \| 0.5514 \| 0.805 \| 0.4063 \| 0.8598 \| 0.805 \| 0.7873 \| 0.3656 \| 0.0575 \| \| No log \| 20.0 \| 140 \| 0.5566 \| 0.81 \| 0.4028 \| 0.9944 \| 0.81 \| 0.7943 \| 0.3449 \| 0.0676 \| \| No log \| 21.0 \| 147 \| 0.5489 \| 0.81 \| 0.3879 \| 1.1351 \| 0.81 \| 0.7966 \| 0.3432 \| 0.0682 \| \| No log \| 22.0 \| 154 \| 0.5586 \| 0.82 \| 0.4091 \| 1.1107 \| 0.82 \| 0.7894 \| 0.3526 \| 0.0580 \| \| No log \| 23.0 \| 161 \| 0.5593 \| 0.795 \| 0.4131 \| 1.1693 \| 0.795 \| 0.7765 \| 0.3483 \| 0.0641 \| \| No log \| 24.0 \| 168 \| 0.5493 \| 0.79 \| 0.3962 \| 1.2363 \| 0.79 \| 0.7740 \| 0.3494 \| 0.0646 \| \| No log \| 25.0 \| 175 \| 0.5489 \| 0.8 \| 0.3930 \| 1.0310 \| 0.8000 \| 0.7638 \| 0.3342 \| 0.0614 \| \| No log \| 26.0 \| 182 \| 0.5492 \| 0.79 \| 0.3944 \| 1.3201 \| 0.79 \| 0.7670 \| 0.3096 \| 0.0667 \| \| No log \| 27.0 \| 189 \| 0.5441 \| 0.805 \| 0.4002 \| 1.1304 \| 0.805 \| 0.7886 \| 0.3528 \| 0.0600 \| \| No log \| 28.0 \| 196 \| 0.5397 \| 0.815 \| 0.3960 \| 1.1210 \| 0.815 \| 0.7902 \| 0.3630 \| 0.0544 \| \| No log \| 29.0 \| 203 \| 0.5418 \| 0.785 \| 0.3977 \| 0.9580 \| 0.785 \| 0.7575 \| 0.3536 \| 0.0646 \| \| No log \| 30.0 \| 210 \| 0.5374 \| 0.815 \| 0.3931 \| 1.0186 \| 0.815 \| 0.7855 \| 0.3422 \| 0.0604 \| \| No log \| 31.0 \| 217 \| 0.5405 \| 0.815 \| 0.3983 \| 0.8948 \| 0.815 \| 0.7980 \| 0.3671 \| 0.0531 \| \| No log \| 32.0 \| 224 \| 0.5394 \| 0.805 \| 0.3998 \| 1.0680 \| 0.805 \| 0.7841 \| 0.3695 \| 0.0568 \| \| No log \| 33.0 \| 231 \| 0.5296 \| 0.81 \| 0.3868 \| 1.1222 \| 0.81 \| 0.7891 \| 0.3530 \| 0.0545 \| \| No log \| 34.0 \| 238 \| 0.5338 \| 0.81 \| 0.3952 \| 1.1333 \| 0.81 \| 0.7825 \| 0.3453 \| 0.0559 \| \| No log \| 35.0 \| 245 \| 0.5339 \| 0.805 \| 0.3941 \| 0.8600 \| 0.805 \| 0.7905 \| 0.3552 \| 0.0554 \| \| No log \| 36.0 \| 252 \| 0.5332 \| 0.81 \| 0.3918 \| 0.9018 \| 0.81 \| 0.7996 \| 0.3669 \| 0.0527 \| \| No log \| 37.0 \| 259 \| 0.5336 \| 0.79 \| 0.3907 \| 0.7768 \| 0.79 \| 0.7612 \| 0.3374 \| 0.0611 \| \| No log \| 38.0 \| 266 \| 0.5327 \| 0.805 \| 0.3906 \| 0.9987 \| 0.805 \| 0.7750 \| 0.3430 \| 0.0564 \| \| No log \| 39.0 \| 273 \| 0.5342 \| 0.805 \| 0.3898 \| 1.1024 \| 0.805 \| 0.7837 \| 0.3295 \| 0.0563 \| \| No log \| 40.0 \| 280 \| 0.5310 \| 0.81 \| 0.3906 \| 0.8426 \| 0.81 \| 0.7820 \| 0.3513 \| 0.0556 \| \| No log \| 41.0 \| 287 \| 0.5327 \| 0.81 \| 0.3950 \| 1.0952 \| 0.81 \| 0.7927 \| 0.3418 \| 0.0570 \| \| No log \| 42.0 \| 294 \| 0.5305 \| 0.82 \| 0.3961 \| 0.7830 \| 0.82 \| 0.8011 \| 0.3501 \| 0.0545 \| \| No log \| 43.0 \| 301 \| 0.5308 \| 0.81 \| 0.3926 \| 0.9752 \| 0.81 \| 0.7907 \| 0.3534 \| 0.0573 \| \| No log \| 44.0 \| 308 \| 0.5287 \| 0.81 \| 0.3898 \| 0.9838 \| 0.81 \| 0.7904 \| 0.3454 \| 0.0570 \| \| No log \| 45.0 \| 315 \| 0.5270 \| 0.815 \| 0.3890 \| 0.8682 \| 0.815 \| 0.8004 \| 0.3499 \| 0.0543 \| \| No log \| 46.0 \| 322 \| 0.5272 \| 0.81 \| 0.3884 \| 0.9784 \| 0.81 \| 0.7827 \| 0.3415 \| 0.0541 \| \| No log \| 47.0 \| 329 \| 0.5306 \| 0.805 \| 0.3900 \| 1.1153 \| 0.805 \| 0.7800 \| 0.3388 \| 0.0571 \| \| No log \| 48.0 \| 336 \| 0.5288 \| 0.82 \| 0.3915 \| 0.9916 \| 0.82 \| 0.7912 \| 0.3519 \| 0.0527 \| \| No log \| 49.0 \| 343 \| 0.5274 \| 0.81 \| 0.3886 \| 0.8415 \| 0.81 \| 0.7855 \| 0.3524 \| 0.0550 \| \| No log \| 50.0 \| 350 \| 0.5264 \| 0.81 \| 0.3868 \| 0.9713 \| 0.81 \| 0.7907 \| 0.3408 \| 0.0559 \| \| No log \| 51.0 \| 357 \| 0.5295 \| 0.815 \| 0.3916 \| 1.0340 \| 0.815 \| 0.7933 \| 0.3683 \| 0.0536 \| \| No log \| 52.0 \| 364 \| 0.5294 \| 0.81 \| 0.3920 \| 0.9178 \| 0.81 \| 0.7854 \| 0.3499 \| 0.0563 \| \| No log \| 53.0 \| 371 \| 0.5283 \| 0.81 \| 0.3912 \| 0.8517 \| 0.81 \| 0.7907 \| 0.3648 \| 0.0540 \| \| No log \| 54.0 \| 378 \| 0.5301 \| 0.815 \| 0.3927 \| 0.9279 \| 0.815 \| 0.7933 \| 0.3579 \| 0.0558 \| \| No log \| 55.0 \| 385 \| 0.5275 \| 0.805 \| 0.3888 \| 0.9225 \| 0.805 \| 0.7800 \| 0.3406 \| 0.0553 \| \| No log \| 56.0 \| 392 \| 0.5284 \| 0.815 \| 0.3903 \| 0.9064 \| 0.815 \| 0.7933 \| 0.3463 \| 0.0551 \| \| No log \| 57.0 \| 399 \| 0.5261 \| 0.81 \| 0.3872 \| 0.9072 \| 0.81 \| 0.7907 \| 0.3527 \| 0.0551 \| \| No log \| 58.0 \| 406 \| 0.5278 \| 0.815 \| 0.3900 \| 0.8469 \| 0.815 \| 0.7966 \| 0.3622 \| 0.0526 \| \| No log \| 59.0 \| 413 \| 0.5280 \| 0.81 \| 0.3900 \| 0.9220 \| 0.81 \| 0.7907 \| 0.3467 \| 0.0551 \| \| No log \| 60.0 \| 420 \| 0.5296 \| 0.81 \| 0.3932 \| 0.9166 \| 0.81 \| 0.7907 \| 0.3620 \| 0.0555 \| \| No log \| 61.0 \| 427 \| 0.5288 \| 0.815 \| 0.3925 \| 0.8647 \| 0.815 \| 0.7966 \| 0.3491 \| 0.0529 \| \| No log \| 62.0 \| 434 \| 0.5288 \| 0.81 \| 0.3909 \| 0.9205 \| 0.81 \| 0.7907 \| 0.3482 \| 0.0552 \| \| No log \| 63.0 \| 441 \| 0.5274 \| 0.81 \| 0.3889 \| 0.9143 \| 0.81 \| 0.7907 \| 0.3457 \| 0.0541 \| \| No log \| 64.0 \| 448 \| 0.5283 \| 0.81 \| 0.3905 \| 0.9141 \| 0.81 \| 0.7907 \| 0.3578 \| 0.0549 \| \| No log \| 65.0 \| 455 \| 0.5283 \| 0.81 \| 0.3907 \| 0.9177 \| 0.81 \| 0.7907 \| 0.3536 \| 0.0548 \| \| No log \| 66.0 \| 462 \| 0.5289 \| 0.81 \| 0.3912 \| 0.9179 \| 0.81 \| 0.7907 \| 0.3502 \| 0.0550 \| \| No log \| 67.0 \| 469 \| 0.5282 \| 0.81 \| 0.3903 \| 0.9134 \| 0.81 \| 0.7907 \| 0.3511 \| 0.0547 \| \| No log \| 68.0 \| 476 \| 0.5279 \| 0.81 \| 0.3901 \| 0.9105 \| 0.81 \| 0.7907 \| 0.3473 \| 0.0541 \| \| No log \| 69.0 \| 483 \| 0.5283 \| 0.81 \| 0.3907 \| 0.9128 \| 0.81 \| 0.7907 \| 0.3558 \| 0.0539 \| \| No log \| 70.0 \| 490 \| 0.5283 \| 0.81 \| 0.3904 \| 0.9191 \| 0.81 \| 0.7907 \| 0.3414 \| 0.0543 \| \| No log \| 71.0 \| 497 \| 0.5284 \| 0.81 \| 0.3905 \| 0.9183 \| 0.81 \| 0.7907 \| 0.3478 \| 0.0546 \| \| 0.3962 \| 72.0 \| 504 \| 0.5285 \| 0.81 \| 0.3909 \| 0.9151 \| 0.81 \| 0.7907 \| 0.3415 \| 0.0545 \| \| 0.3962 \| 73.0 \| 511 \| 0.5283 \| 0.81 \| 0.3906 \| 0.9144 \| 0.81 \| 0.7907 \| 0.3499 \| 0.0542 \| \| 0.3962 \| 74.0 \| 518 \| 0.5282 \| 0.81 \| 0.3903 \| 0.9146 \| 0.81 \| 0.7907 \| 0.3411 \| 0.0541 \| \| 0.3962 \| 75.0 \| 525 \| 0.5284 \| 0.81 \| 0.3909 \| 0.9159 \| 0.81 \| 0.7907 \| 0.3571 \| 0.0542 \| \| 0.3962 \| 76.0 \| 532 \| 0.5284 \| 0.81 \| 0.3906 \| 0.9155 \| 0.81 \| 0.7907 \| 0.3361 \| 0.0543 \| \| 0.3962 \| 77.0 \| 539 \| 0.5283 \| 0.81 \| 0.3906 \| 0.9159 \| 0.81 \| 0.7907 \| 0.3480 \| 0.0541 \| \| 0.3962 \| 78.0 \| 546 \| 0.5282 \| 0.81 \| 0.3905 \| 0.9120 \| 0.81 \| 0.7907 \| 0.3413 \| 0.0540 \| \| 0.3962 \| 79.0 \| 553 \| 0.5283 \| 0.81 \| 0.3905 \| 0.9162 \| 0.81 \| 0.7907 \| 0.3412 \| 0.0542 \| \| 0.3962 \| 80.0 \| 560 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9189 \| 0.81 \| 0.7907 \| 0.3361 \| 0.0543 \| \| 0.3962 \| 81.0 \| 567 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9162 \| 0.81 \| 0.7907 \| 0.3470 \| 0.0541 \| \| 0.3962 \| 82.0 \| 574 \| 0.5283 \| 0.81 \| 0.3904 \| 0.9144 \| 0.81 \| 0.7907 \| 0.3411 \| 0.0540 \| \| 0.3962 \| 83.0 \| 581 \| 0.5284 \| 0.81 \| 0.3906 \| 0.9153 \| 0.81 \| 0.7907 \| 0.3361 \| 0.0542 \| \| 0.3962 \| 84.0 \| 588 \| 0.5284 \| 0.81 \| 0.3907 \| 0.9151 \| 0.81 \| 0.7907 \| 0.3419 \| 0.0542 \| \| 0.3962 \| 85.0 \| 595 \| 0.5283 \| 0.81 \| 0.3905 \| 0.9143 \| 0.81 \| 0.7907 \| 0.3362 \| 0.0541 \| \| 0.3962 \| 86.0 \| 602 \| 0.5285 \| 0.81 \| 0.3908 \| 0.9152 \| 0.81 \| 0.7907 \| 0.3418 \| 0.0540 \| \| 0.3962 \| 87.0 \| 609 \| 0.5284 \| 0.81 \| 0.3907 \| 0.9156 \| 0.81 \| 0.7907 \| 0.3365 \| 0.0543 \| \| 0.3962 \| 88.0 \| 616 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9155 \| 0.81 \| 0.7907 \| 0.3419 \| 0.0541 \| \| 0.3962 \| 89.0 \| 623 \| 0.5284 \| 0.81 \| 0.3906 \| 0.9154 \| 0.81 \| 0.7907 \| 0.3360 \| 0.0541 \| \| 0.3962 \| 90.0 \| 630 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9168 \| 0.81 \| 0.7907 \| 0.3418 \| 0.0543 \| \| 0.3962 \| 91.0 \| 637 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9160 \| 0.81 \| 0.7907 \| 0.3420 \| 0.0543 \| \| 0.3962 \| 92.0 \| 644 \| 0.5285 \| 0.81 \| 0.3908 \| 0.9164 \| 0.81 \| 0.7907 \| 0.3421 \| 0.0541 \| \| 0.3962 \| 93.0 \| 651 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9164 \| 0.81 \| 0.7907 \| 0.3473 \| 0.0542 \| \| 0.3962 \| 94.0 \| 658 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9164 \| 0.81 \| 0.7907 \| 0.3420 \| 0.0542 \| \| 0.3962 \| 95.0 \| 665 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9161 \| 0.81 \| 0.7907 \| 0.3473 \| 0.0541 \| \| 0.3962 \| 96.0 \| 672 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9157 \| 0.81 \| 0.7907 \| 0.3421 \| 0.0542 \| \| 0.3962 \| 97.0 \| 679 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9154 \| 0.81 \| 0.7907 \| 0.3363 \| 0.0542 \| \| 0.3962 \| 98.0 \| 686 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9164 \| 0.81 \| 0.7907 \| 0.3420 \| 0.0542 \| \| 0.3962 \| 99.0 \| 693 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9162 \| 0.81 \| 0.7907 \| 0.3420 \| 0.0542 \| \| 0.3962 \| 100.0 \| 700 \| 0.5285 \| 0.81 \| 0.3907 \| 0.9159 \| 0.81 \| 0.7907 \| 0.3421 \| 0.0542 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/39-tiny_tobacco3482	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 39-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0652 - Accuracy: 0.68 - Brier Loss: 0.8297 - Nll: 1.9403 - F1 Micro: 0.68 - F1 Macro: 0.5899 - Ece: 0.5728 - Aurc: 0.1321 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.0877 \| 0.11 \| 0.8983 \| 8.4701 \| 0.11 \| 0.0377 \| 0.1765 \| 0.9065 \| \| No log \| 2.0 \| 50 \| 0.0811 \| 0.09 \| 0.8984 \| 6.6861 \| 0.09 \| 0.0364 \| 0.1641 \| 0.9033 \| \| No log \| 3.0 \| 75 \| 0.0803 \| 0.165 \| 0.8981 \| 7.2209 \| 0.165 \| 0.0520 \| 0.2115 \| 0.7480 \| \| No log \| 4.0 \| 100 \| 0.0799 \| 0.17 \| 0.8977 \| 7.1709 \| 0.17 \| 0.0713 \| 0.2184 \| 0.6439 \| \| No log \| 5.0 \| 125 \| 0.0794 \| 0.15 \| 0.8970 \| 7.1655 \| 0.15 \| 0.0553 \| 0.2075 \| 0.6278 \| \| No log \| 6.0 \| 150 \| 0.0786 \| 0.225 \| 0.8959 \| 6.1378 \| 0.225 \| 0.1083 \| 0.2511 \| 0.5827 \| \| No log \| 7.0 \| 175 \| 0.0777 \| 0.275 \| 0.8944 \| 5.1450 \| 0.275 \| 0.1053 \| 0.2840 \| 0.5127 \| \| No log \| 8.0 \| 200 \| 0.0767 \| 0.305 \| 0.8920 \| 5.0689 \| 0.305 \| 0.1177 \| 0.3095 \| 0.4372 \| \| No log \| 9.0 \| 225 \| 0.0756 \| 0.305 \| 0.8893 \| 4.6689 \| 0.305 \| 0.1015 \| 0.3114 \| 0.4279 \| \| No log \| 10.0 \| 250 \| 0.0747 \| 0.325 \| 0.8865 \| 5.1088 \| 0.325 \| 0.1067 \| 0.3257 \| 0.3891 \| \| No log \| 11.0 \| 275 \| 0.0738 \| 0.345 \| 0.8803 \| 4.9911 \| 0.345 \| 0.1297 \| 0.3418 \| 0.3672 \| \| No log \| 12.0 \| 300 \| 0.0729 \| 0.405 \| 0.8748 \| 4.7892 \| 0.405 \| 0.1649 \| 0.3774 \| 0.3276 \| \| No log \| 13.0 \| 325 \| 0.0721 \| 0.395 \| 0.8696 \| 4.6346 \| 0.395 \| 0.1561 \| 0.3723 \| 0.3221 \| \| No log \| 14.0 \| 350 \| 0.0713 \| 0.44 \| 0.8676 \| 3.8051 \| 0.44 \| 0.1780 \| 0.4047 \| 0.2743 \| \| No log \| 15.0 \| 375 \| 0.0705 \| 0.43 \| 0.8612 \| 3.6440 \| 0.4300 \| 0.1893 \| 0.3994 \| 0.2701 \| \| No log \| 16.0 \| 400 \| 0.0698 \| 0.48 \| 0.8569 \| 3.4952 \| 0.48 \| 0.2607 \| 0.4216 \| 0.2640 \| \| No log \| 17.0 \| 425 \| 0.0690 \| 0.54 \| 0.8533 \| 3.2018 \| 0.54 \| 0.3042 \| 0.4771 \| 0.2168 \| \| No log \| 18.0 \| 450 \| 0.0683 \| 0.54 \| 0.8500 \| 2.7042 \| 0.54 \| 0.3161 \| 0.4677 \| 0.2075 \| \| No log \| 19.0 \| 475 \| 0.0679 \| 0.6 \| 0.8469 \| 2.5343 \| 0.6 \| 0.4246 \| 0.5186 \| 0.1865 \| \| 0.0767 \| 20.0 \| 500 \| 0.0677 \| 0.615 \| 0.8455 \| 2.6485 \| 0.615 \| 0.4407 \| 0.5207 \| 0.1908 \| \| 0.0767 \| 21.0 \| 525 \| 0.0673 \| 0.625 \| 0.8463 \| 2.2841 \| 0.625 \| 0.4471 \| 0.5369 \| 0.1558 \| \| 0.0767 \| 22.0 \| 550 \| 0.0667 \| 0.645 \| 0.8398 \| 2.4032 \| 0.645 \| 0.4633 \| 0.5462 \| 0.1500 \| \| 0.0767 \| 23.0 \| 575 \| 0.0664 \| 0.63 \| 0.8388 \| 2.4376 \| 0.63 \| 0.4600 \| 0.5356 \| 0.1583 \| \| 0.0767 \| 24.0 \| 600 \| 0.0663 \| 0.645 \| 0.8371 \| 2.3057 \| 0.645 \| 0.4731 \| 0.5443 \| 0.1437 \| \| 0.0767 \| 25.0 \| 625 \| 0.0662 \| 0.635 \| 0.8386 \| 2.2486 \| 0.635 \| 0.4606 \| 0.5425 \| 0.1515 \| \| 0.0767 \| 26.0 \| 650 \| 0.0661 \| 0.63 \| 0.8374 \| 2.2367 \| 0.63 \| 0.4543 \| 0.5423 \| 0.1549 \| \| 0.0767 \| 27.0 \| 675 \| 0.0660 \| 0.64 \| 0.8358 \| 2.1278 \| 0.64 \| 0.4554 \| 0.5486 \| 0.1350 \| \| 0.0767 \| 28.0 \| 700 \| 0.0660 \| 0.64 \| 0.8360 \| 2.2416 \| 0.64 \| 0.4726 \| 0.5363 \| 0.1429 \| \| 0.0767 \| 29.0 \| 725 \| 0.0660 \| 0.67 \| 0.8364 \| 2.1574 \| 0.67 \| 0.4990 \| 0.5648 \| 0.1264 \| \| 0.0767 \| 30.0 \| 750 \| 0.0659 \| 0.665 \| 0.8357 \| 2.2015 \| 0.665 \| 0.5113 \| 0.5645 \| 0.1383 \| \| 0.0767 \| 31.0 \| 775 \| 0.0658 \| 0.65 \| 0.8347 \| 2.1367 \| 0.65 \| 0.4995 \| 0.5522 \| 0.1461 \| \| 0.0767 \| 32.0 \| 800 \| 0.0656 \| 0.67 \| 0.8341 \| 2.1025 \| 0.67 \| 0.5110 \| 0.5666 \| 0.1307 \| \| 0.0767 \| 33.0 \| 825 \| 0.0656 \| 0.645 \| 0.8354 \| 2.0398 \| 0.645 \| 0.5034 \| 0.5442 \| 0.1334 \| \| 0.0767 \| 34.0 \| 850 \| 0.0656 \| 0.67 \| 0.8346 \| 2.1934 \| 0.67 \| 0.5112 \| 0.5569 \| 0.1299 \| \| 0.0767 \| 35.0 \| 875 \| 0.0658 \| 0.665 \| 0.8353 \| 2.0671 \| 0.665 \| 0.5255 \| 0.5646 \| 0.1295 \| \| 0.0767 \| 36.0 \| 900 \| 0.0655 \| 0.665 \| 0.8320 \| 2.0168 \| 0.665 \| 0.5138 \| 0.5680 \| 0.1306 \| \| 0.0767 \| 37.0 \| 925 \| 0.0655 \| 0.675 \| 0.8315 \| 2.0974 \| 0.675 \| 0.5229 \| 0.5672 \| 0.1333 \| \| 0.0767 \| 38.0 \| 950 \| 0.0655 \| 0.675 \| 0.8341 \| 2.0624 \| 0.675 \| 0.5457 \| 0.5750 \| 0.1256 \| \| 0.0767 \| 39.0 \| 975 \| 0.0653 \| 0.69 \| 0.8321 \| 2.0556 \| 0.69 \| 0.5498 \| 0.5856 \| 0.1250 \| \| 0.0625 \| 40.0 \| 1000 \| 0.0653 \| 0.69 \| 0.8330 \| 1.9627 \| 0.69 \| 0.5812 \| 0.5765 \| 0.1243 \| \| 0.0625 \| 41.0 \| 1025 \| 0.0653 \| 0.705 \| 0.8335 \| 2.0491 \| 0.705 \| 0.5900 \| 0.5919 \| 0.1155 \| \| 0.0625 \| 42.0 \| 1050 \| 0.0653 \| 0.705 \| 0.8335 \| 2.0357 \| 0.705 \| 0.5984 \| 0.5945 \| 0.1250 \| \| 0.0625 \| 43.0 \| 1075 \| 0.0652 \| 0.7 \| 0.8316 \| 2.0326 \| 0.7 \| 0.5957 \| 0.5932 \| 0.1230 \| \| 0.0625 \| 44.0 \| 1100 \| 0.0653 \| 0.69 \| 0.8323 \| 2.0244 \| 0.69 \| 0.5904 \| 0.5911 \| 0.1252 \| \| 0.0625 \| 45.0 \| 1125 \| 0.0653 \| 0.68 \| 0.8310 \| 2.0410 \| 0.68 \| 0.5644 \| 0.5699 \| 0.1305 \| \| 0.0625 \| 46.0 \| 1150 \| 0.0653 \| 0.695 \| 0.8323 \| 2.0288 \| 0.695 \| 0.5944 \| 0.5837 \| 0.1251 \| \| 0.0625 \| 47.0 \| 1175 \| 0.0652 \| 0.685 \| 0.8312 \| 1.9613 \| 0.685 \| 0.5894 \| 0.5834 \| 0.1244 \| \| 0.0625 \| 48.0 \| 1200 \| 0.0652 \| 0.685 \| 0.8312 \| 1.9620 \| 0.685 \| 0.5753 \| 0.5728 \| 0.1321 \| \| 0.0625 \| 49.0 \| 1225 \| 0.0652 \| 0.695 \| 0.8317 \| 1.9706 \| 0.695 \| 0.5962 \| 0.5837 \| 0.1291 \| \| 0.0625 \| 50.0 \| 1250 \| 0.0651 \| 0.69 \| 0.8314 \| 1.9661 \| 0.69 \| 0.5902 \| 0.5759 \| 0.1315 \| \| 0.0625 \| 51.0 \| 1275 \| 0.0652 \| 0.68 \| 0.8319 \| 1.9542 \| 0.68 \| 0.5695 \| 0.5704 \| 0.1288 \| \| 0.0625 \| 52.0 \| 1300 \| 0.0651 \| 0.695 \| 0.8308 \| 1.9577 \| 0.695 \| 0.5834 \| 0.5823 \| 0.1276 \| \| 0.0625 \| 53.0 \| 1325 \| 0.0652 \| 0.67 \| 0.8315 \| 1.8876 \| 0.67 \| 0.5604 \| 0.5680 \| 0.1326 \| \| 0.0625 \| 54.0 \| 1350 \| 0.0651 \| 0.68 \| 0.8318 \| 1.8731 \| 0.68 \| 0.5925 \| 0.5644 \| 0.1317 \| \| 0.0625 \| 55.0 \| 1375 \| 0.0651 \| 0.7 \| 0.8292 \| 1.9448 \| 0.7 \| 0.5856 \| 0.5903 \| 0.1214 \| \| 0.0625 \| 56.0 \| 1400 \| 0.0652 \| 0.705 \| 0.8310 \| 2.0042 \| 0.705 \| 0.6059 \| 0.5881 \| 0.1195 \| \| 0.0625 \| 57.0 \| 1425 \| 0.0651 \| 0.685 \| 0.8309 \| 1.9467 \| 0.685 \| 0.5832 \| 0.5734 \| 0.1273 \| \| 0.0625 \| 58.0 \| 1450 \| 0.0651 \| 0.705 \| 0.8306 \| 1.9480 \| 0.705 \| 0.6064 \| 0.5956 \| 0.1227 \| \| 0.0625 \| 59.0 \| 1475 \| 0.0651 \| 0.695 \| 0.8302 \| 1.9453 \| 0.695 \| 0.5998 \| 0.5806 \| 0.1310 \| \| 0.0604 \| 60.0 \| 1500 \| 0.0651 \| 0.68 \| 0.8305 \| 1.8892 \| 0.68 \| 0.5813 \| 0.5643 \| 0.1276 \| \| 0.0604 \| 61.0 \| 1525 \| 0.0651 \| 0.725 \| 0.8302 \| 1.9304 \| 0.7250 \| 0.6346 \| 0.6022 \| 0.1194 \| \| 0.0604 \| 62.0 \| 1550 \| 0.0651 \| 0.685 \| 0.8303 \| 1.8831 \| 0.685 \| 0.5773 \| 0.5815 \| 0.1322 \| \| 0.0604 \| 63.0 \| 1575 \| 0.0650 \| 0.71 \| 0.8299 \| 1.9502 \| 0.7100 \| 0.6140 \| 0.5944 \| 0.1257 \| \| 0.0604 \| 64.0 \| 1600 \| 0.0651 \| 0.68 \| 0.8296 \| 1.9407 \| 0.68 \| 0.5701 \| 0.5727 \| 0.1337 \| \| 0.0604 \| 65.0 \| 1625 \| 0.0651 \| 0.695 \| 0.8309 \| 1.9413 \| 0.695 \| 0.5995 \| 0.5884 \| 0.1234 \| \| 0.0604 \| 66.0 \| 1650 \| 0.0651 \| 0.69 \| 0.8298 \| 1.9474 \| 0.69 \| 0.5865 \| 0.5723 \| 0.1293 \| \| 0.0604 \| 67.0 \| 1675 \| 0.0650 \| 0.705 \| 0.8298 \| 1.8996 \| 0.705 \| 0.6109 \| 0.5966 \| 0.1258 \| \| 0.0604 \| 68.0 \| 1700 \| 0.0651 \| 0.7 \| 0.8298 \| 1.9938 \| 0.7 \| 0.6089 \| 0.5895 \| 0.1283 \| \| 0.0604 \| 69.0 \| 1725 \| 0.0651 \| 0.695 \| 0.8296 \| 1.9273 \| 0.695 \| 0.5923 \| 0.5776 \| 0.1251 \| \| 0.0604 \| 70.0 \| 1750 \| 0.0651 \| 0.705 \| 0.8297 \| 1.8920 \| 0.705 \| 0.6162 \| 0.5868 \| 0.1323 \| \| 0.0604 \| 71.0 \| 1775 \| 0.0651 \| 0.7 \| 0.8304 \| 1.9852 \| 0.7 \| 0.6123 \| 0.5878 \| 0.1282 \| \| 0.0604 \| 72.0 \| 1800 \| 0.0651 \| 0.68 \| 0.8310 \| 1.9399 \| 0.68 \| 0.5963 \| 0.5633 \| 0.1345 \| \| 0.0604 \| 73.0 \| 1825 \| 0.0650 \| 0.725 \| 0.8302 \| 1.9237 \| 0.7250 \| 0.6266 \| 0.6029 \| 0.1192 \| \| 0.0604 \| 74.0 \| 1850 \| 0.0651 \| 0.68 \| 0.8306 \| 1.9521 \| 0.68 \| 0.5967 \| 0.5745 \| 0.1342 \| \| 0.0604 \| 75.0 \| 1875 \| 0.0651 \| 0.695 \| 0.8301 \| 1.9911 \| 0.695 \| 0.6047 \| 0.5841 \| 0.1317 \| \| 0.0604 \| 76.0 \| 1900 \| 0.0651 \| 0.695 \| 0.8299 \| 1.9333 \| 0.695 \| 0.5935 \| 0.5715 \| 0.1299 \| \| 0.0604 \| 77.0 \| 1925 \| 0.0651 \| 0.695 \| 0.8298 \| 1.9429 \| 0.695 \| 0.6041 \| 0.5679 \| 0.1293 \| \| 0.0604 \| 78.0 \| 1950 \| 0.0651 \| 0.695 \| 0.8298 \| 1.9367 \| 0.695 \| 0.6101 \| 0.5792 \| 0.1279 \| \| 0.0604 \| 79.0 \| 1975 \| 0.0651 \| 0.695 \| 0.8301 \| 1.9934 \| 0.695 \| 0.6095 \| 0.5898 \| 0.1324 \| \| 0.0596 \| 80.0 \| 2000 \| 0.0651 \| 0.7 \| 0.8297 \| 1.9332 \| 0.7 \| 0.6071 \| 0.5778 \| 0.1271 \| \| 0.0596 \| 81.0 \| 2025 \| 0.0651 \| 0.685 \| 0.8303 \| 1.9457 \| 0.685 \| 0.5986 \| 0.5807 \| 0.1320 \| \| 0.0596 \| 82.0 \| 2050 \| 0.0651 \| 0.7 \| 0.8300 \| 1.9337 \| 0.7 \| 0.6072 \| 0.5896 \| 0.1296 \| \| 0.0596 \| 83.0 \| 2075 \| 0.0651 \| 0.685 \| 0.8298 \| 1.9424 \| 0.685 \| 0.5985 \| 0.5753 \| 0.1319 \| \| 0.0596 \| 84.0 \| 2100 \| 0.0651 \| 0.7 \| 0.8297 \| 1.9407 \| 0.7 \| 0.6116 \| 0.5847 \| 0.1311 \| \| 0.0596 \| 85.0 \| 2125 \| 0.0651 \| 0.685 \| 0.8298 \| 1.9364 \| 0.685 \| 0.5983 \| 0.5841 \| 0.1311 \| \| 0.0596 \| 86.0 \| 2150 \| 0.0651 \| 0.685 \| 0.8299 \| 1.9345 \| 0.685 \| 0.5983 \| 0.5806 \| 0.1318 \| \| 0.0596 \| 87.0 \| 2175 \| 0.0652 \| 0.685 \| 0.8299 \| 1.9402 \| 0.685 \| 0.5979 \| 0.5778 \| 0.1317 \| \| 0.0596 \| 88.0 \| 2200 \| 0.0651 \| 0.685 \| 0.8298 \| 1.9385 \| 0.685 \| 0.5983 \| 0.5726 \| 0.1315 \| \| 0.0596 \| 89.0 \| 2225 \| 0.0652 \| 0.68 \| 0.8296 \| 1.9367 \| 0.68 \| 0.5899 \| 0.5732 \| 0.1314 \| \| 0.0596 \| 90.0 \| 2250 \| 0.0652 \| 0.68 \| 0.8298 \| 1.9383 \| 0.68 \| 0.5896 \| 0.5782 \| 0.1321 \| \| 0.0596 \| 91.0 \| 2275 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9408 \| 0.68 \| 0.5896 \| 0.5782 \| 0.1317 \| \| 0.0596 \| 92.0 \| 2300 \| 0.0652 \| 0.68 \| 0.8299 \| 1.9370 \| 0.68 \| 0.5899 \| 0.5701 \| 0.1320 \| \| 0.0596 \| 93.0 \| 2325 \| 0.0652 \| 0.68 \| 0.8298 \| 1.9395 \| 0.68 \| 0.5899 \| 0.5754 \| 0.1321 \| \| 0.0596 \| 94.0 \| 2350 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9392 \| 0.68 \| 0.5899 \| 0.5701 \| 0.1326 \| \| 0.0596 \| 95.0 \| 2375 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9393 \| 0.68 \| 0.5899 \| 0.5651 \| 0.1320 \| \| 0.0596 \| 96.0 \| 2400 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9397 \| 0.68 \| 0.5899 \| 0.5701 \| 0.1321 \| \| 0.0596 \| 97.0 \| 2425 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9400 \| 0.68 \| 0.5899 \| 0.5676 \| 0.1322 \| \| 0.0596 \| 98.0 \| 2450 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9391 \| 0.68 \| 0.5899 \| 0.5677 \| 0.1320 \| \| 0.0596 \| 99.0 \| 2475 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9397 \| 0.68 \| 0.5899 \| 0.5701 \| 0.1321 \| \| 0.0592 \| 100.0 \| 2500 \| 0.0652 \| 0.68 \| 0.8297 \| 1.9403 \| 0.68 \| 0.5899 \| 0.5728 \| 0.1321 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/39-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 39-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0812 - Accuracy: 0.835 - Brier Loss: 0.2748 - Nll: 1.2215 - F1 Micro: 0.835 - F1 Macro: 0.8213 - Ece: 0.1443 - Aurc: 0.0548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 5.1938 \| 0.095 \| 1.0201 \| 8.6917 \| 0.095 \| 0.0778 \| 0.3242 \| 0.9009 \| \| No log \| 2.0 \| 14 \| 4.3129 \| 0.13 \| 0.9109 \| 8.2379 \| 0.13 \| 0.0910 \| 0.2544 \| 0.8466 \| \| No log \| 3.0 \| 21 \| 3.9690 \| 0.225 \| 0.8600 \| 6.8547 \| 0.225 \| 0.1401 \| 0.2626 \| 0.6398 \| \| No log \| 4.0 \| 28 \| 3.8651 \| 0.375 \| 0.7978 \| 5.6610 \| 0.375 \| 0.2964 \| 0.3198 \| 0.4692 \| \| No log \| 5.0 \| 35 \| 3.8115 \| 0.465 \| 0.7222 \| 3.4731 \| 0.465 \| 0.3435 \| 0.3007 \| 0.3464 \| \| No log \| 6.0 \| 42 \| 3.7351 \| 0.575 \| 0.6691 \| 2.6672 \| 0.575 \| 0.4736 \| 0.3509 \| 0.2284 \| \| No log \| 7.0 \| 49 \| 3.6913 \| 0.62 \| 0.6152 \| 2.6026 \| 0.62 \| 0.4700 \| 0.3255 \| 0.1827 \| \| No log \| 8.0 \| 56 \| 3.6687 \| 0.68 \| 0.5820 \| 1.9726 \| 0.68 \| 0.5400 \| 0.3735 \| 0.1472 \| \| No log \| 9.0 \| 63 \| 3.6771 \| 0.645 \| 0.5464 \| 1.9938 \| 0.645 \| 0.5211 \| 0.3013 \| 0.1595 \| \| No log \| 10.0 \| 70 \| 3.6759 \| 0.685 \| 0.4884 \| 1.9735 \| 0.685 \| 0.5678 \| 0.2672 \| 0.1278 \| \| No log \| 11.0 \| 77 \| 3.6587 \| 0.71 \| 0.4696 \| 2.0625 \| 0.7100 \| 0.6080 \| 0.2956 \| 0.1115 \| \| No log \| 12.0 \| 84 \| 3.6317 \| 0.72 \| 0.4121 \| 2.2088 \| 0.72 \| 0.6137 \| 0.2372 \| 0.0925 \| \| No log \| 13.0 \| 91 \| 3.6799 \| 0.745 \| 0.4167 \| 2.0639 \| 0.745 \| 0.6372 \| 0.2480 \| 0.0978 \| \| No log \| 14.0 \| 98 \| 3.6191 \| 0.745 \| 0.3850 \| 1.9955 \| 0.745 \| 0.6384 \| 0.2363 \| 0.0728 \| \| No log \| 15.0 \| 105 \| 3.6813 \| 0.715 \| 0.3814 \| 2.0731 \| 0.715 \| 0.6026 \| 0.1995 \| 0.0918 \| \| No log \| 16.0 \| 112 \| 3.6394 \| 0.75 \| 0.3644 \| 1.9093 \| 0.75 \| 0.6492 \| 0.1904 \| 0.0777 \| \| No log \| 17.0 \| 119 \| 3.7661 \| 0.735 \| 0.3786 \| 1.5402 \| 0.735 \| 0.6352 \| 0.2032 \| 0.0982 \| \| No log \| 18.0 \| 126 \| 3.6849 \| 0.79 \| 0.3369 \| 1.8761 \| 0.79 \| 0.6965 \| 0.1954 \| 0.0708 \| \| No log \| 19.0 \| 133 \| 3.6776 \| 0.775 \| 0.3358 \| 1.4981 \| 0.775 \| 0.7021 \| 0.1919 \| 0.0744 \| \| No log \| 20.0 \| 140 \| 3.6814 \| 0.755 \| 0.3546 \| 1.5225 \| 0.755 \| 0.6873 \| 0.1840 \| 0.0794 \| \| No log \| 21.0 \| 147 \| 3.6948 \| 0.775 \| 0.3267 \| 1.4776 \| 0.775 \| 0.7052 \| 0.1630 \| 0.0710 \| \| No log \| 22.0 \| 154 \| 3.7210 \| 0.795 \| 0.3191 \| 1.3634 \| 0.795 \| 0.7383 \| 0.1737 \| 0.0705 \| \| No log \| 23.0 \| 161 \| 3.7231 \| 0.805 \| 0.3062 \| 1.3141 \| 0.805 \| 0.7679 \| 0.1629 \| 0.0665 \| \| No log \| 24.0 \| 168 \| 3.7322 \| 0.815 \| 0.2903 \| 1.2030 \| 0.815 \| 0.7771 \| 0.1789 \| 0.0609 \| \| No log \| 25.0 \| 175 \| 3.7237 \| 0.815 \| 0.3020 \| 1.1721 \| 0.815 \| 0.7947 \| 0.1759 \| 0.0603 \| \| No log \| 26.0 \| 182 \| 3.8243 \| 0.8 \| 0.3138 \| 1.3356 \| 0.8000 \| 0.7699 \| 0.1735 \| 0.0720 \| \| No log \| 27.0 \| 189 \| 3.7675 \| 0.81 \| 0.3038 \| 1.2662 \| 0.81 \| 0.7853 \| 0.1891 \| 0.0699 \| \| No log \| 28.0 \| 196 \| 3.8006 \| 0.81 \| 0.2992 \| 1.3422 \| 0.81 \| 0.7805 \| 0.1709 \| 0.0698 \| \| No log \| 29.0 \| 203 \| 3.7783 \| 0.815 \| 0.3009 \| 1.3322 \| 0.815 \| 0.7959 \| 0.1729 \| 0.0669 \| \| No log \| 30.0 \| 210 \| 3.7547 \| 0.835 \| 0.2775 \| 0.9761 \| 0.835 \| 0.8228 \| 0.1751 \| 0.0566 \| \| No log \| 31.0 \| 217 \| 3.7810 \| 0.82 \| 0.2905 \| 1.1472 \| 0.82 \| 0.7953 \| 0.1670 \| 0.0631 \| \| No log \| 32.0 \| 224 \| 3.7935 \| 0.82 \| 0.2732 \| 1.2016 \| 0.82 \| 0.7967 \| 0.1429 \| 0.0590 \| \| No log \| 33.0 \| 231 \| 3.7871 \| 0.83 \| 0.2774 \| 1.2459 \| 0.83 \| 0.8134 \| 0.1495 \| 0.0562 \| \| No log \| 34.0 \| 238 \| 3.7689 \| 0.815 \| 0.2756 \| 1.1135 \| 0.815 \| 0.7825 \| 0.1609 \| 0.0596 \| \| No log \| 35.0 \| 245 \| 3.8169 \| 0.81 \| 0.2801 \| 1.2621 \| 0.81 \| 0.7880 \| 0.1570 \| 0.0624 \| \| No log \| 36.0 \| 252 \| 3.7973 \| 0.82 \| 0.2729 \| 1.1310 \| 0.82 \| 0.7894 \| 0.1466 \| 0.0585 \| \| No log \| 37.0 \| 259 \| 3.8560 \| 0.835 \| 0.2825 \| 1.3222 \| 0.835 \| 0.8114 \| 0.1466 \| 0.0606 \| \| No log \| 38.0 \| 266 \| 3.8351 \| 0.83 \| 0.2892 \| 1.2548 \| 0.83 \| 0.8178 \| 0.1489 \| 0.0593 \| \| No log \| 39.0 \| 273 \| 3.8258 \| 0.82 \| 0.2711 \| 1.1900 \| 0.82 \| 0.8037 \| 0.1455 \| 0.0589 \| \| No log \| 40.0 \| 280 \| 3.8288 \| 0.815 \| 0.2840 \| 1.2167 \| 0.815 \| 0.7913 \| 0.1574 \| 0.0619 \| \| No log \| 41.0 \| 287 \| 3.8264 \| 0.82 \| 0.2790 \| 1.1737 \| 0.82 \| 0.8020 \| 0.1394 \| 0.0609 \| \| No log \| 42.0 \| 294 \| 3.8276 \| 0.81 \| 0.2797 \| 1.1603 \| 0.81 \| 0.7888 \| 0.1585 \| 0.0580 \| \| No log \| 43.0 \| 301 \| 3.8554 \| 0.815 \| 0.2771 \| 1.1695 \| 0.815 \| 0.7943 \| 0.1310 \| 0.0594 \| \| No log \| 44.0 \| 308 \| 3.8405 \| 0.825 \| 0.2768 \| 1.1593 \| 0.825 \| 0.8149 \| 0.1413 \| 0.0569 \| \| No log \| 45.0 \| 315 \| 3.8640 \| 0.815 \| 0.2891 \| 1.1752 \| 0.815 \| 0.7980 \| 0.1516 \| 0.0590 \| \| No log \| 46.0 \| 322 \| 3.8624 \| 0.825 \| 0.2653 \| 1.1548 \| 0.825 \| 0.8024 \| 0.1384 \| 0.0581 \| \| No log \| 47.0 \| 329 \| 3.8546 \| 0.83 \| 0.2766 \| 1.1634 \| 0.83 \| 0.8106 \| 0.1411 \| 0.0594 \| \| No log \| 48.0 \| 336 \| 3.8652 \| 0.82 \| 0.2805 \| 1.1651 \| 0.82 \| 0.8069 \| 0.1278 \| 0.0581 \| \| No log \| 49.0 \| 343 \| 3.8716 \| 0.83 \| 0.2758 \| 1.1895 \| 0.83 \| 0.8065 \| 0.1486 \| 0.0590 \| \| No log \| 50.0 \| 350 \| 3.8720 \| 0.815 \| 0.2737 \| 1.1709 \| 0.815 \| 0.7937 \| 0.1375 \| 0.0578 \| \| No log \| 51.0 \| 357 \| 3.8812 \| 0.82 \| 0.2762 \| 1.2348 \| 0.82 \| 0.7993 \| 0.1292 \| 0.0600 \| \| No log \| 52.0 \| 364 \| 3.8844 \| 0.805 \| 0.2815 \| 1.0870 \| 0.805 \| 0.7843 \| 0.1525 \| 0.0581 \| \| No log \| 53.0 \| 371 \| 3.8968 \| 0.825 \| 0.2704 \| 1.2235 \| 0.825 \| 0.8011 \| 0.1452 \| 0.0582 \| \| No log \| 54.0 \| 378 \| 3.8996 \| 0.81 \| 0.2788 \| 1.3264 \| 0.81 \| 0.7909 \| 0.1453 \| 0.0573 \| \| No log \| 55.0 \| 385 \| 3.9037 \| 0.81 \| 0.2757 \| 1.2231 \| 0.81 \| 0.7928 \| 0.1307 \| 0.0574 \| \| No log \| 56.0 \| 392 \| 3.9024 \| 0.81 \| 0.2775 \| 1.2369 \| 0.81 \| 0.7869 \| 0.1493 \| 0.0581 \| \| No log \| 57.0 \| 399 \| 3.8951 \| 0.83 \| 0.2722 \| 1.2151 \| 0.83 \| 0.8171 \| 0.1491 \| 0.0556 \| \| No log \| 58.0 \| 406 \| 3.9224 \| 0.82 \| 0.2741 \| 1.2957 \| 0.82 \| 0.8001 \| 0.1351 \| 0.0575 \| \| No log \| 59.0 \| 413 \| 3.9397 \| 0.805 \| 0.2782 \| 1.3017 \| 0.805 \| 0.7870 \| 0.1342 \| 0.0584 \| \| No log \| 60.0 \| 420 \| 3.9250 \| 0.835 \| 0.2721 \| 1.2251 \| 0.835 \| 0.8151 \| 0.1466 \| 0.0570 \| \| No log \| 61.0 \| 427 \| 3.9381 \| 0.825 \| 0.2753 \| 1.2330 \| 0.825 \| 0.8044 \| 0.1384 \| 0.0577 \| \| No log \| 62.0 \| 434 \| 3.9475 \| 0.82 \| 0.2759 \| 1.2171 \| 0.82 \| 0.8054 \| 0.1485 \| 0.0576 \| \| No log \| 63.0 \| 441 \| 3.9591 \| 0.83 \| 0.2761 \| 1.2299 \| 0.83 \| 0.8122 \| 0.1551 \| 0.0568 \| \| No log \| 64.0 \| 448 \| 3.9496 \| 0.835 \| 0.2709 \| 1.2282 \| 0.835 \| 0.8223 \| 0.1397 \| 0.0559 \| \| No log \| 65.0 \| 455 \| 3.9360 \| 0.83 \| 0.2688 \| 1.2238 \| 0.83 \| 0.8171 \| 0.1384 \| 0.0535 \| \| No log \| 66.0 \| 462 \| 3.9594 \| 0.835 \| 0.2733 \| 1.2395 \| 0.835 \| 0.8094 \| 0.1540 \| 0.0563 \| \| No log \| 67.0 \| 469 \| 3.9648 \| 0.84 \| 0.2700 \| 1.2154 \| 0.8400 \| 0.8252 \| 0.1673 \| 0.0557 \| \| No log \| 68.0 \| 476 \| 3.9725 \| 0.83 \| 0.2712 \| 1.2297 \| 0.83 \| 0.8171 \| 0.1248 \| 0.0552 \| \| No log \| 69.0 \| 483 \| 3.9844 \| 0.835 \| 0.2719 \| 1.2243 \| 0.835 \| 0.8151 \| 0.1605 \| 0.0557 \| \| No log \| 70.0 \| 490 \| 3.9845 \| 0.83 \| 0.2699 \| 1.2288 \| 0.83 \| 0.8100 \| 0.1223 \| 0.0553 \| \| No log \| 71.0 \| 497 \| 3.9986 \| 0.835 \| 0.2729 \| 1.2206 \| 0.835 \| 0.8223 \| 0.1381 \| 0.0556 \| \| 3.4116 \| 72.0 \| 504 \| 3.9973 \| 0.835 \| 0.2727 \| 1.2242 \| 0.835 \| 0.8223 \| 0.1446 \| 0.0553 \| \| 3.4116 \| 73.0 \| 511 \| 4.0092 \| 0.835 \| 0.2733 \| 1.2226 \| 0.835 \| 0.8223 \| 0.1482 \| 0.0554 \| \| 3.4116 \| 74.0 \| 518 \| 4.0072 \| 0.83 \| 0.2714 \| 1.2248 \| 0.83 \| 0.8152 \| 0.1219 \| 0.0549 \| \| 3.4116 \| 75.0 \| 525 \| 4.0168 \| 0.835 \| 0.2742 \| 1.2200 \| 0.835 \| 0.8223 \| 0.1329 \| 0.0551 \| \| 3.4116 \| 76.0 \| 532 \| 4.0223 \| 0.835 \| 0.2737 \| 1.2248 \| 0.835 \| 0.8213 \| 0.1380 \| 0.0552 \| \| 3.4116 \| 77.0 \| 539 \| 4.0250 \| 0.84 \| 0.2719 \| 1.2208 \| 0.8400 \| 0.8252 \| 0.1405 \| 0.0551 \| \| 3.4116 \| 78.0 \| 546 \| 4.0338 \| 0.835 \| 0.2745 \| 1.2242 \| 0.835 \| 0.8213 \| 0.1536 \| 0.0551 \| \| 3.4116 \| 79.0 \| 553 \| 4.0380 \| 0.835 \| 0.2740 \| 1.2234 \| 0.835 \| 0.8213 \| 0.1494 \| 0.0552 \| \| 3.4116 \| 80.0 \| 560 \| 4.0445 \| 0.835 \| 0.2744 \| 1.2223 \| 0.835 \| 0.8213 \| 0.1500 \| 0.0555 \| \| 3.4116 \| 81.0 \| 567 \| 4.0449 \| 0.835 \| 0.2735 \| 1.2209 \| 0.835 \| 0.8213 \| 0.1504 \| 0.0552 \| \| 3.4116 \| 82.0 \| 574 \| 4.0515 \| 0.835 \| 0.2747 \| 1.2228 \| 0.835 \| 0.8213 \| 0.1526 \| 0.0549 \| \| 3.4116 \| 83.0 \| 581 \| 4.0534 \| 0.835 \| 0.2743 \| 1.2226 \| 0.835 \| 0.8213 \| 0.1501 \| 0.0548 \| \| 3.4116 \| 84.0 \| 588 \| 4.0572 \| 0.835 \| 0.2740 \| 1.2225 \| 0.835 \| 0.8213 \| 0.1447 \| 0.0550 \| \| 3.4116 \| 85.0 \| 595 \| 4.0605 \| 0.835 \| 0.2743 \| 1.2222 \| 0.835 \| 0.8213 \| 0.1466 \| 0.0548 \| \| 3.4116 \| 86.0 \| 602 \| 4.0621 \| 0.835 \| 0.2744 \| 1.2215 \| 0.835 \| 0.8213 \| 0.1427 \| 0.0548 \| \| 3.4116 \| 87.0 \| 609 \| 4.0653 \| 0.835 \| 0.2745 \| 1.2214 \| 0.835 \| 0.8213 \| 0.1439 \| 0.0549 \| \| 3.4116 \| 88.0 \| 616 \| 4.0673 \| 0.835 \| 0.2746 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1410 \| 0.0548 \| \| 3.4116 \| 89.0 \| 623 \| 4.0705 \| 0.835 \| 0.2748 \| 1.2214 \| 0.835 \| 0.8213 \| 0.1440 \| 0.0549 \| \| 3.4116 \| 90.0 \| 630 \| 4.0717 \| 0.835 \| 0.2744 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1426 \| 0.0547 \| \| 3.4116 \| 91.0 \| 637 \| 4.0740 \| 0.835 \| 0.2747 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1432 \| 0.0548 \| \| 3.4116 \| 92.0 \| 644 \| 4.0753 \| 0.835 \| 0.2748 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1442 \| 0.0547 \| \| 3.4116 \| 93.0 \| 651 \| 4.0763 \| 0.835 \| 0.2746 \| 1.2214 \| 0.835 \| 0.8213 \| 0.1434 \| 0.0546 \| \| 3.4116 \| 94.0 \| 658 \| 4.0777 \| 0.835 \| 0.2746 \| 1.2213 \| 0.835 \| 0.8213 \| 0.1433 \| 0.0547 \| \| 3.4116 \| 95.0 \| 665 \| 4.0788 \| 0.835 \| 0.2747 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1442 \| 0.0547 \| \| 3.4116 \| 96.0 \| 672 \| 4.0800 \| 0.835 \| 0.2748 \| 1.2217 \| 0.835 \| 0.8213 \| 0.1466 \| 0.0547 \| \| 3.4116 \| 97.0 \| 679 \| 4.0802 \| 0.835 \| 0.2747 \| 1.2215 \| 0.835 \| 0.8213 \| 0.1435 \| 0.0547 \| \| 3.4116 \| 98.0 \| 686 \| 4.0808 \| 0.835 \| 0.2747 \| 1.2214 \| 0.835 \| 0.8213 \| 0.1435 \| 0.0547 \| \| 3.4116 \| 99.0 \| 693 \| 4.0811 \| 0.835 \| 0.2748 \| 1.2214 \| 0.835 \| 0.8213 \| 0.1443 \| 0.0547 \| \| 3.4116 \| 100.0 \| 700 \| 4.0812 \| 0.835 \| 0.2748 \| 1.2215 \| 0.835 \| 0.8213 \| 0.1443 \| 0.0548 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
leopuv/dog_chicken_muffin_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # leopuv/dog_chicken_muffin_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0326 - Train Accuracy: 0.9947 - Validation Loss: 0.0191 - Validation Accuracy: 0.9947 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 22500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.4271 \| 0.996 \| 0.1010 \| 0.9960 \| 0 \| \| 0.1111 \| 0.9933 \| 0.0613 \| 0.9933 \| 1 \| \| 0.0774 \| 0.9947 \| 0.0451 \| 0.9947 \| 2 \| \| 0.0592 \| 0.9947 \| 0.0354 \| 0.9947 \| 3 \| \| 0.0457 \| 0.9987 \| 0.0252 \| 0.9987 \| 4 \| \| 0.0434 \| 0.992 \| 0.0346 \| 0.9920 \| 5 \| \| 0.0429 \| 0.996 \| 0.0270 \| 0.9960 \| 6 \| \| 0.0397 \| 0.9973 \| 0.0177 \| 0.9973 \| 7 \| \| 0.0326 \| 0.9973 \| 0.0161 \| 0.9973 \| 8 \| \| 0.0326 \| 0.9947 \| 0.0191 \| 0.9947 \| 9 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "chicken", "dog", "muffin" ]
jordyvl/39-tiny_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 39-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 65.8239 - Accuracy: 0.84 - Brier Loss: 0.2807 - Nll: 1.1327 - F1 Micro: 0.8400 - F1 Macro: 0.8280 - Ece: 0.1437 - Aurc: 0.0472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 69.1264 \| 0.26 \| 0.8707 \| 4.9002 \| 0.26 \| 0.1920 \| 0.3064 \| 0.7815 \| \| No log \| 2.0 \| 50 \| 68.3319 \| 0.545 \| 0.5960 \| 2.8558 \| 0.545 \| 0.4562 \| 0.2850 \| 0.2564 \| \| No log \| 3.0 \| 75 \| 67.8627 \| 0.68 \| 0.4406 \| 1.6064 \| 0.68 \| 0.6157 \| 0.2543 \| 0.1333 \| \| No log \| 4.0 \| 100 \| 67.5797 \| 0.75 \| 0.3829 \| 1.5484 \| 0.75 \| 0.7343 \| 0.2220 \| 0.1152 \| \| No log \| 5.0 \| 125 \| 67.2608 \| 0.8 \| 0.3072 \| 1.7491 \| 0.8000 \| 0.7573 \| 0.1809 \| 0.0698 \| \| No log \| 6.0 \| 150 \| 67.0950 \| 0.78 \| 0.3169 \| 1.7708 \| 0.78 \| 0.7441 \| 0.1576 \| 0.0607 \| \| No log \| 7.0 \| 175 \| 66.9178 \| 0.755 \| 0.3812 \| 1.6929 \| 0.755 \| 0.6848 \| 0.1899 \| 0.1043 \| \| No log \| 8.0 \| 200 \| 66.7335 \| 0.75 \| 0.3763 \| 1.7649 \| 0.75 \| 0.7399 \| 0.1870 \| 0.0806 \| \| No log \| 9.0 \| 225 \| 66.4371 \| 0.805 \| 0.3036 \| 1.4686 \| 0.805 \| 0.7896 \| 0.1378 \| 0.0545 \| \| No log \| 10.0 \| 250 \| 66.6823 \| 0.75 \| 0.3924 \| 1.8808 \| 0.75 \| 0.6665 \| 0.1964 \| 0.0829 \| \| No log \| 11.0 \| 275 \| 66.6079 \| 0.775 \| 0.3570 \| 1.8872 \| 0.775 \| 0.7442 \| 0.1811 \| 0.0839 \| \| No log \| 12.0 \| 300 \| 66.4364 \| 0.765 \| 0.3689 \| 1.6981 \| 0.765 \| 0.7550 \| 0.1909 \| 0.0732 \| \| No log \| 13.0 \| 325 \| 66.1317 \| 0.785 \| 0.3346 \| 1.4062 \| 0.785 \| 0.7823 \| 0.1753 \| 0.0572 \| \| No log \| 14.0 \| 350 \| 66.5182 \| 0.73 \| 0.4453 \| 1.4431 \| 0.7300 \| 0.7208 \| 0.2310 \| 0.0985 \| \| No log \| 15.0 \| 375 \| 66.5154 \| 0.775 \| 0.3769 \| 1.4897 \| 0.775 \| 0.7398 \| 0.1875 \| 0.0821 \| \| No log \| 16.0 \| 400 \| 66.4191 \| 0.8 \| 0.3315 \| 1.5327 \| 0.8000 \| 0.7771 \| 0.1651 \| 0.0685 \| \| No log \| 17.0 \| 425 \| 66.2374 \| 0.765 \| 0.3520 \| 1.5388 \| 0.765 \| 0.7401 \| 0.1767 \| 0.0760 \| \| No log \| 18.0 \| 450 \| 66.2010 \| 0.805 \| 0.3320 \| 1.4280 \| 0.805 \| 0.7721 \| 0.1756 \| 0.0684 \| \| No log \| 19.0 \| 475 \| 66.0335 \| 0.85 \| 0.2625 \| 1.3549 \| 0.85 \| 0.8352 \| 0.1431 \| 0.0430 \| \| 65.4034 \| 20.0 \| 500 \| 66.2213 \| 0.815 \| 0.3213 \| 1.3912 \| 0.815 \| 0.7955 \| 0.1645 \| 0.0579 \| \| 65.4034 \| 21.0 \| 525 \| 66.2647 \| 0.77 \| 0.3656 \| 1.3241 \| 0.7700 \| 0.7743 \| 0.1899 \| 0.0755 \| \| 65.4034 \| 22.0 \| 550 \| 66.1220 \| 0.86 \| 0.2684 \| 1.2459 \| 0.8600 \| 0.8354 \| 0.1327 \| 0.0473 \| \| 65.4034 \| 23.0 \| 575 \| 66.1615 \| 0.85 \| 0.2623 \| 1.3400 \| 0.85 \| 0.8231 \| 0.1291 \| 0.0448 \| \| 65.4034 \| 24.0 \| 600 \| 66.2114 \| 0.825 \| 0.3130 \| 1.4118 \| 0.825 \| 0.8122 \| 0.1565 \| 0.0498 \| \| 65.4034 \| 25.0 \| 625 \| 66.1048 \| 0.835 \| 0.2704 \| 1.3571 \| 0.835 \| 0.8196 \| 0.1405 \| 0.0450 \| \| 65.4034 \| 26.0 \| 650 \| 65.9832 \| 0.825 \| 0.2990 \| 1.1514 \| 0.825 \| 0.8253 \| 0.1603 \| 0.0423 \| \| 65.4034 \| 27.0 \| 675 \| 66.2567 \| 0.805 \| 0.3307 \| 1.3509 \| 0.805 \| 0.8022 \| 0.1699 \| 0.0634 \| \| 65.4034 \| 28.0 \| 700 \| 66.0668 \| 0.82 \| 0.3172 \| 1.1445 \| 0.82 \| 0.7973 \| 0.1538 \| 0.0419 \| \| 65.4034 \| 29.0 \| 725 \| 66.2254 \| 0.81 \| 0.3252 \| 1.3290 \| 0.81 \| 0.8011 \| 0.1659 \| 0.0523 \| \| 65.4034 \| 30.0 \| 750 \| 65.9643 \| 0.84 \| 0.2697 \| 1.2052 \| 0.8400 \| 0.8245 \| 0.1319 \| 0.0425 \| \| 65.4034 \| 31.0 \| 775 \| 66.3419 \| 0.81 \| 0.3249 \| 1.2772 \| 0.81 \| 0.7969 \| 0.1700 \| 0.0612 \| \| 65.4034 \| 32.0 \| 800 \| 66.0324 \| 0.825 \| 0.3003 \| 1.3138 \| 0.825 \| 0.8000 \| 0.1584 \| 0.0445 \| \| 65.4034 \| 33.0 \| 825 \| 66.3326 \| 0.82 \| 0.3336 \| 1.2983 \| 0.82 \| 0.7826 \| 0.1754 \| 0.0590 \| \| 65.4034 \| 34.0 \| 850 \| 66.1374 \| 0.825 \| 0.3061 \| 1.5645 \| 0.825 \| 0.8012 \| 0.1500 \| 0.0459 \| \| 65.4034 \| 35.0 \| 875 \| 66.2310 \| 0.815 \| 0.3207 \| 1.5607 \| 0.815 \| 0.7939 \| 0.1712 \| 0.0646 \| \| 65.4034 \| 36.0 \| 900 \| 66.0388 \| 0.84 \| 0.2873 \| 1.1966 \| 0.8400 \| 0.8327 \| 0.1456 \| 0.0585 \| \| 65.4034 \| 37.0 \| 925 \| 66.0520 \| 0.835 \| 0.2958 \| 1.2728 \| 0.835 \| 0.8180 \| 0.1508 \| 0.0477 \| \| 65.4034 \| 38.0 \| 950 \| 65.9916 \| 0.84 \| 0.2783 \| 1.1635 \| 0.8400 \| 0.8233 \| 0.1398 \| 0.0438 \| \| 65.4034 \| 39.0 \| 975 \| 65.9391 \| 0.845 \| 0.2743 \| 1.2660 \| 0.845 \| 0.8289 \| 0.1396 \| 0.0458 \| \| 64.0802 \| 40.0 \| 1000 \| 65.9291 \| 0.845 \| 0.2762 \| 1.3335 \| 0.845 \| 0.8259 \| 0.1373 \| 0.0430 \| \| 64.0802 \| 41.0 \| 1025 \| 65.8559 \| 0.85 \| 0.2686 \| 1.3432 \| 0.85 \| 0.8338 \| 0.1345 \| 0.0428 \| \| 64.0802 \| 42.0 \| 1050 \| 65.8612 \| 0.845 \| 0.2772 \| 1.2679 \| 0.845 \| 0.8255 \| 0.1389 \| 0.0431 \| \| 64.0802 \| 43.0 \| 1075 \| 65.8953 \| 0.84 \| 0.2742 \| 1.2614 \| 0.8400 \| 0.8227 \| 0.1408 \| 0.0435 \| \| 64.0802 \| 44.0 \| 1100 \| 65.8569 \| 0.835 \| 0.2769 \| 1.2730 \| 0.835 \| 0.8199 \| 0.1426 \| 0.0432 \| \| 64.0802 \| 45.0 \| 1125 \| 65.8610 \| 0.84 \| 0.2769 \| 1.2622 \| 0.8400 \| 0.8248 \| 0.1485 \| 0.0425 \| \| 64.0802 \| 46.0 \| 1150 \| 65.8237 \| 0.845 \| 0.2729 \| 1.1920 \| 0.845 \| 0.8334 \| 0.1462 \| 0.0432 \| \| 64.0802 \| 47.0 \| 1175 \| 65.8416 \| 0.845 \| 0.2785 \| 1.1826 \| 0.845 \| 0.8317 \| 0.1376 \| 0.0431 \| \| 64.0802 \| 48.0 \| 1200 \| 65.8452 \| 0.845 \| 0.2817 \| 1.1876 \| 0.845 \| 0.8317 \| 0.1417 \| 0.0441 \| \| 64.0802 \| 49.0 \| 1225 \| 65.8394 \| 0.845 \| 0.2750 \| 1.1993 \| 0.845 \| 0.8309 \| 0.1315 \| 0.0419 \| \| 64.0802 \| 50.0 \| 1250 \| 65.8527 \| 0.84 \| 0.2796 \| 1.1860 \| 0.8400 \| 0.8279 \| 0.1410 \| 0.0432 \| \| 64.0802 \| 51.0 \| 1275 \| 65.8286 \| 0.845 \| 0.2749 \| 1.1977 \| 0.845 \| 0.8333 \| 0.1444 \| 0.0428 \| \| 64.0802 \| 52.0 \| 1300 \| 65.8296 \| 0.83 \| 0.2779 \| 1.1926 \| 0.83 \| 0.8171 \| 0.1382 \| 0.0435 \| \| 64.0802 \| 53.0 \| 1325 \| 65.8121 \| 0.83 \| 0.2779 \| 1.1955 \| 0.83 \| 0.8155 \| 0.1387 \| 0.0436 \| \| 64.0802 \| 54.0 \| 1350 \| 65.8361 \| 0.825 \| 0.2769 \| 1.1909 \| 0.825 \| 0.8095 \| 0.1435 \| 0.0419 \| \| 64.0802 \| 55.0 \| 1375 \| 65.8370 \| 0.83 \| 0.2816 \| 1.1925 \| 0.83 \| 0.8171 \| 0.1416 \| 0.0435 \| \| 64.0802 \| 56.0 \| 1400 \| 65.8301 \| 0.825 \| 0.2763 \| 1.1908 \| 0.825 \| 0.8101 \| 0.1393 \| 0.0439 \| \| 64.0802 \| 57.0 \| 1425 \| 65.8301 \| 0.82 \| 0.2791 \| 1.1881 \| 0.82 \| 0.8040 \| 0.1443 \| 0.0440 \| \| 64.0802 \| 58.0 \| 1450 \| 65.8324 \| 0.83 \| 0.2754 \| 1.1938 \| 0.83 \| 0.8198 \| 0.1387 \| 0.0460 \| \| 64.0802 \| 59.0 \| 1475 \| 65.8407 \| 0.825 \| 0.2818 \| 1.1893 \| 0.825 \| 0.8138 \| 0.1393 \| 0.0439 \| \| 63.8765 \| 60.0 \| 1500 \| 65.8236 \| 0.84 \| 0.2782 \| 1.1871 \| 0.8400 \| 0.8290 \| 0.1512 \| 0.0449 \| \| 63.8765 \| 61.0 \| 1525 \| 65.8198 \| 0.825 \| 0.2846 \| 1.1752 \| 0.825 \| 0.8138 \| 0.1505 \| 0.0438 \| \| 63.8765 \| 62.0 \| 1550 \| 65.8243 \| 0.83 \| 0.2796 \| 1.1753 \| 0.83 \| 0.8196 \| 0.1480 \| 0.0445 \| \| 63.8765 \| 63.0 \| 1575 \| 65.8495 \| 0.835 \| 0.2781 \| 1.1766 \| 0.835 \| 0.8257 \| 0.1353 \| 0.0451 \| \| 63.8765 \| 64.0 \| 1600 \| 65.8204 \| 0.835 \| 0.2833 \| 1.1752 \| 0.835 \| 0.8239 \| 0.1400 \| 0.0447 \| \| 63.8765 \| 65.0 \| 1625 \| 65.8374 \| 0.835 \| 0.2800 \| 1.1829 \| 0.835 \| 0.8239 \| 0.1474 \| 0.0441 \| \| 63.8765 \| 66.0 \| 1650 \| 65.8433 \| 0.83 \| 0.2855 \| 1.1678 \| 0.83 \| 0.8148 \| 0.1498 \| 0.0444 \| \| 63.8765 \| 67.0 \| 1675 \| 65.8259 \| 0.835 \| 0.2820 \| 1.1725 \| 0.835 \| 0.8257 \| 0.1518 \| 0.0457 \| \| 63.8765 \| 68.0 \| 1700 \| 65.8443 \| 0.83 \| 0.2841 \| 1.1652 \| 0.83 \| 0.8196 \| 0.1491 \| 0.0457 \| \| 63.8765 \| 69.0 \| 1725 \| 65.8255 \| 0.835 \| 0.2849 \| 1.1620 \| 0.835 \| 0.8247 \| 0.1499 \| 0.0460 \| \| 63.8765 \| 70.0 \| 1750 \| 65.8421 \| 0.83 \| 0.2870 \| 1.1681 \| 0.83 \| 0.8196 \| 0.1418 \| 0.0475 \| \| 63.8765 \| 71.0 \| 1775 \| 65.8402 \| 0.835 \| 0.2839 \| 1.1614 \| 0.835 \| 0.8230 \| 0.1359 \| 0.0466 \| \| 63.8765 \| 72.0 \| 1800 \| 65.8224 \| 0.84 \| 0.2831 \| 1.1555 \| 0.8400 \| 0.8280 \| 0.1467 \| 0.0459 \| \| 63.8765 \| 73.0 \| 1825 \| 65.8233 \| 0.84 \| 0.2824 \| 1.1578 \| 0.8400 \| 0.8280 \| 0.1428 \| 0.0465 \| \| 63.8765 \| 74.0 \| 1850 \| 65.8299 \| 0.84 \| 0.2814 \| 1.1574 \| 0.8400 \| 0.8280 \| 0.1469 \| 0.0465 \| \| 63.8765 \| 75.0 \| 1875 \| 65.8309 \| 0.835 \| 0.2790 \| 1.1575 \| 0.835 \| 0.8219 \| 0.1407 \| 0.0465 \| \| 63.8765 \| 76.0 \| 1900 \| 65.8199 \| 0.84 \| 0.2789 \| 1.1496 \| 0.8400 \| 0.8280 \| 0.1437 \| 0.0460 \| \| 63.8765 \| 77.0 \| 1925 \| 65.8222 \| 0.84 \| 0.2828 \| 1.1520 \| 0.8400 \| 0.8280 \| 0.1539 \| 0.0461 \| \| 63.8765 \| 78.0 \| 1950 \| 65.8312 \| 0.84 \| 0.2801 \| 1.1459 \| 0.8400 \| 0.8280 \| 0.1354 \| 0.0458 \| \| 63.8765 \| 79.0 \| 1975 \| 65.8253 \| 0.84 \| 0.2836 \| 1.1448 \| 0.8400 \| 0.8280 \| 0.1542 \| 0.0465 \| \| 63.7964 \| 80.0 \| 2000 \| 65.8332 \| 0.84 \| 0.2839 \| 1.1408 \| 0.8400 \| 0.8280 \| 0.1486 \| 0.0462 \| \| 63.7964 \| 81.0 \| 2025 \| 65.8316 \| 0.84 \| 0.2818 \| 1.1419 \| 0.8400 \| 0.8280 \| 0.1430 \| 0.0460 \| \| 63.7964 \| 82.0 \| 2050 \| 65.8238 \| 0.84 \| 0.2824 \| 1.1387 \| 0.8400 \| 0.8280 \| 0.1411 \| 0.0452 \| \| 63.7964 \| 83.0 \| 2075 \| 65.8294 \| 0.84 \| 0.2786 \| 1.1410 \| 0.8400 \| 0.8280 \| 0.1539 \| 0.0469 \| \| 63.7964 \| 84.0 \| 2100 \| 65.8267 \| 0.84 \| 0.2818 \| 1.1391 \| 0.8400 \| 0.8280 \| 0.1463 \| 0.0471 \| \| 63.7964 \| 85.0 \| 2125 \| 65.8222 \| 0.84 \| 0.2814 \| 1.1401 \| 0.8400 \| 0.8280 \| 0.1463 \| 0.0470 \| \| 63.7964 \| 86.0 \| 2150 \| 65.8264 \| 0.84 \| 0.2776 \| 1.1380 \| 0.8400 \| 0.8280 \| 0.1359 \| 0.0460 \| \| 63.7964 \| 87.0 \| 2175 \| 65.8228 \| 0.84 \| 0.2781 \| 1.1366 \| 0.8400 \| 0.8280 \| 0.1468 \| 0.0460 \| \| 63.7964 \| 88.0 \| 2200 \| 65.8229 \| 0.84 \| 0.2832 \| 1.1367 \| 0.8400 \| 0.8280 \| 0.1455 \| 0.0476 \| \| 63.7964 \| 89.0 \| 2225 \| 65.8271 \| 0.84 \| 0.2792 \| 1.1376 \| 0.8400 \| 0.8280 \| 0.1598 \| 0.0467 \| \| 63.7964 \| 90.0 \| 2250 \| 65.8234 \| 0.84 \| 0.2830 \| 1.1352 \| 0.8400 \| 0.8280 \| 0.1427 \| 0.0474 \| \| 63.7964 \| 91.0 \| 2275 \| 65.8309 \| 0.84 \| 0.2804 \| 1.1352 \| 0.8400 \| 0.8280 \| 0.1426 \| 0.0467 \| \| 63.7964 \| 92.0 \| 2300 \| 65.8305 \| 0.84 \| 0.2796 \| 1.1345 \| 0.8400 \| 0.8280 \| 0.1438 \| 0.0466 \| \| 63.7964 \| 93.0 \| 2325 \| 65.8155 \| 0.84 \| 0.2808 \| 1.1347 \| 0.8400 \| 0.8280 \| 0.1499 \| 0.0471 \| \| 63.7964 \| 94.0 \| 2350 \| 65.8218 \| 0.84 \| 0.2803 \| 1.1336 \| 0.8400 \| 0.8280 \| 0.1487 \| 0.0473 \| \| 63.7964 \| 95.0 \| 2375 \| 65.8152 \| 0.84 \| 0.2812 \| 1.1334 \| 0.8400 \| 0.8280 \| 0.1441 \| 0.0466 \| \| 63.7964 \| 96.0 \| 2400 \| 65.8230 \| 0.84 \| 0.2801 \| 1.1344 \| 0.8400 \| 0.8280 \| 0.1488 \| 0.0472 \| \| 63.7964 \| 97.0 \| 2425 \| 65.8206 \| 0.84 \| 0.2808 \| 1.1328 \| 0.8400 \| 0.8280 \| 0.1490 \| 0.0472 \| \| 63.7964 \| 98.0 \| 2450 \| 65.8221 \| 0.84 \| 0.2807 \| 1.1332 \| 0.8400 \| 0.8280 \| 0.1438 \| 0.0474 \| \| 63.7964 \| 99.0 \| 2475 \| 65.8207 \| 0.84 \| 0.2809 \| 1.1326 \| 0.8400 \| 0.8280 \| 0.1446 \| 0.0472 \| \| 63.7613 \| 100.0 \| 2500 \| 65.8239 \| 0.84 \| 0.2807 \| 1.1327 \| 0.8400 \| 0.8280 \| 0.1437 \| 0.0472 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
annazhong/vit-base-patch16-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7730 - Accuracy: 0.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 150 - eval_batch_size: 150 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 600 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.8264 \| 0.1143 \| \| No log \| 2.0 \| 2 \| 1.7730 \| 0.2 \| \| No log \| 3.0 \| 3 \| 1.8143 \| 0.2 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]
jordyvl/18-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 18-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6385 - Accuracy: 0.795 - Brier Loss: 0.4484 - Nll: 0.9250 - F1 Micro: 0.795 - F1 Macro: 0.7709 - Ece: 0.4225 - Aurc: 0.0567 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.8736 \| 0.105 \| 1.0144 \| 8.6059 \| 0.1050 \| 0.0844 \| 0.3169 \| 0.8967 \| \| No log \| 2.0 \| 14 \| 1.2559 \| 0.155 \| 0.8899 \| 7.1587 \| 0.155 \| 0.1259 \| 0.2459 \| 0.7824 \| \| No log \| 3.0 \| 21 \| 1.0441 \| 0.33 \| 0.8123 \| 5.3633 \| 0.33 \| 0.2575 \| 0.2995 \| 0.5173 \| \| No log \| 4.0 \| 28 \| 0.9169 \| 0.525 \| 0.6852 \| 3.4671 \| 0.525 \| 0.4253 \| 0.3387 \| 0.2892 \| \| No log \| 5.0 \| 35 \| 0.8589 \| 0.615 \| 0.6269 \| 3.1119 \| 0.615 \| 0.5500 \| 0.3683 \| 0.2124 \| \| No log \| 6.0 \| 42 \| 0.7954 \| 0.675 \| 0.5756 \| 2.2578 \| 0.675 \| 0.5752 \| 0.3626 \| 0.1550 \| \| No log \| 7.0 \| 49 \| 0.7664 \| 0.685 \| 0.5143 \| 1.8811 \| 0.685 \| 0.6073 \| 0.3254 \| 0.1390 \| \| No log \| 8.0 \| 56 \| 0.7305 \| 0.76 \| 0.4895 \| 1.5449 \| 0.76 \| 0.6768 \| 0.3695 \| 0.1016 \| \| No log \| 9.0 \| 63 \| 0.7056 \| 0.765 \| 0.4721 \| 1.3575 \| 0.765 \| 0.6991 \| 0.3828 \| 0.0927 \| \| No log \| 10.0 \| 70 \| 0.6961 \| 0.77 \| 0.4380 \| 1.2662 \| 0.7700 \| 0.7509 \| 0.3549 \| 0.0803 \| \| No log \| 11.0 \| 77 \| 0.6772 \| 0.81 \| 0.4508 \| 1.3169 \| 0.81 \| 0.7915 \| 0.4175 \| 0.0629 \| \| No log \| 12.0 \| 84 \| 0.6766 \| 0.785 \| 0.4491 \| 1.2979 \| 0.785 \| 0.7650 \| 0.3839 \| 0.0800 \| \| No log \| 13.0 \| 91 \| 0.6754 \| 0.785 \| 0.4382 \| 1.2395 \| 0.785 \| 0.7794 \| 0.3609 \| 0.0689 \| \| No log \| 14.0 \| 98 \| 0.6768 \| 0.8 \| 0.4472 \| 1.2218 \| 0.8000 \| 0.7837 \| 0.3910 \| 0.0640 \| \| No log \| 15.0 \| 105 \| 0.6793 \| 0.81 \| 0.4663 \| 1.2698 \| 0.81 \| 0.7856 \| 0.4293 \| 0.0672 \| \| No log \| 16.0 \| 112 \| 0.6784 \| 0.795 \| 0.4726 \| 1.3043 \| 0.795 \| 0.7728 \| 0.4232 \| 0.0669 \| \| No log \| 17.0 \| 119 \| 0.6638 \| 0.805 \| 0.4372 \| 1.2746 \| 0.805 \| 0.7747 \| 0.3956 \| 0.0677 \| \| No log \| 18.0 \| 126 \| 0.6588 \| 0.8 \| 0.4297 \| 1.4466 \| 0.8000 \| 0.7762 \| 0.3866 \| 0.0686 \| \| No log \| 19.0 \| 133 \| 0.6588 \| 0.81 \| 0.4588 \| 1.2093 \| 0.81 \| 0.7912 \| 0.4029 \| 0.0702 \| \| No log \| 20.0 \| 140 \| 0.6587 \| 0.81 \| 0.4534 \| 1.0697 \| 0.81 \| 0.7980 \| 0.4197 \| 0.0641 \| \| No log \| 21.0 \| 147 \| 0.6527 \| 0.815 \| 0.4529 \| 1.1527 \| 0.815 \| 0.7942 \| 0.4196 \| 0.0598 \| \| No log \| 22.0 \| 154 \| 0.6608 \| 0.78 \| 0.4559 \| 1.2039 \| 0.78 \| 0.7581 \| 0.3612 \| 0.0725 \| \| No log \| 23.0 \| 161 \| 0.6558 \| 0.8 \| 0.4547 \| 1.0687 \| 0.8000 \| 0.7644 \| 0.3964 \| 0.0584 \| \| No log \| 24.0 \| 168 \| 0.6584 \| 0.8 \| 0.4491 \| 1.2869 \| 0.8000 \| 0.7735 \| 0.3810 \| 0.0687 \| \| No log \| 25.0 \| 175 \| 0.6493 \| 0.805 \| 0.4497 \| 0.9981 \| 0.805 \| 0.7887 \| 0.4162 \| 0.0570 \| \| No log \| 26.0 \| 182 \| 0.6425 \| 0.795 \| 0.4424 \| 1.1317 \| 0.795 \| 0.7790 \| 0.3974 \| 0.0596 \| \| No log \| 27.0 \| 189 \| 0.6518 \| 0.8 \| 0.4552 \| 0.9743 \| 0.8000 \| 0.7715 \| 0.4122 \| 0.0592 \| \| No log \| 28.0 \| 196 \| 0.6526 \| 0.805 \| 0.4630 \| 1.1343 \| 0.805 \| 0.7941 \| 0.4171 \| 0.0672 \| \| No log \| 29.0 \| 203 \| 0.6515 \| 0.8 \| 0.4531 \| 1.0062 \| 0.8000 \| 0.7681 \| 0.3970 \| 0.0566 \| \| No log \| 30.0 \| 210 \| 0.6459 \| 0.795 \| 0.4534 \| 1.0893 \| 0.795 \| 0.7853 \| 0.3972 \| 0.0600 \| \| No log \| 31.0 \| 217 \| 0.6423 \| 0.81 \| 0.4483 \| 0.9035 \| 0.81 \| 0.7927 \| 0.4297 \| 0.0536 \| \| No log \| 32.0 \| 224 \| 0.6454 \| 0.8 \| 0.4517 \| 1.1025 \| 0.8000 \| 0.7688 \| 0.3923 \| 0.0599 \| \| No log \| 33.0 \| 231 \| 0.6417 \| 0.805 \| 0.4476 \| 0.9658 \| 0.805 \| 0.7767 \| 0.4136 \| 0.0563 \| \| No log \| 34.0 \| 238 \| 0.6399 \| 0.815 \| 0.4462 \| 0.8565 \| 0.815 \| 0.7940 \| 0.4234 \| 0.0550 \| \| No log \| 35.0 \| 245 \| 0.6430 \| 0.81 \| 0.4505 \| 1.0491 \| 0.81 \| 0.7855 \| 0.4279 \| 0.0629 \| \| No log \| 36.0 \| 252 \| 0.6440 \| 0.815 \| 0.4481 \| 1.0288 \| 0.815 \| 0.7813 \| 0.4132 \| 0.0539 \| \| No log \| 37.0 \| 259 \| 0.6396 \| 0.82 \| 0.4493 \| 0.9477 \| 0.82 \| 0.8125 \| 0.4266 \| 0.0525 \| \| No log \| 38.0 \| 266 \| 0.6410 \| 0.815 \| 0.4462 \| 1.0462 \| 0.815 \| 0.7971 \| 0.4157 \| 0.0522 \| \| No log \| 39.0 \| 273 \| 0.6360 \| 0.8 \| 0.4399 \| 0.9645 \| 0.8000 \| 0.7779 \| 0.3974 \| 0.0566 \| \| No log \| 40.0 \| 280 \| 0.6376 \| 0.805 \| 0.4412 \| 0.8777 \| 0.805 \| 0.7772 \| 0.4104 \| 0.0544 \| \| No log \| 41.0 \| 287 \| 0.6411 \| 0.795 \| 0.4475 \| 0.9240 \| 0.795 \| 0.7780 \| 0.4062 \| 0.0583 \| \| No log \| 42.0 \| 294 \| 0.6398 \| 0.795 \| 0.4509 \| 0.9279 \| 0.795 \| 0.7650 \| 0.4068 \| 0.0577 \| \| No log \| 43.0 \| 301 \| 0.6430 \| 0.79 \| 0.4567 \| 0.9279 \| 0.79 \| 0.7683 \| 0.4073 \| 0.0590 \| \| No log \| 44.0 \| 308 \| 0.6401 \| 0.8 \| 0.4495 \| 0.9915 \| 0.8000 \| 0.7744 \| 0.4200 \| 0.0565 \| \| No log \| 45.0 \| 315 \| 0.6364 \| 0.795 \| 0.4448 \| 0.9245 \| 0.795 \| 0.7729 \| 0.4115 \| 0.0568 \| \| No log \| 46.0 \| 322 \| 0.6391 \| 0.79 \| 0.4472 \| 1.0060 \| 0.79 \| 0.7633 \| 0.4044 \| 0.0561 \| \| No log \| 47.0 \| 329 \| 0.6376 \| 0.795 \| 0.4470 \| 0.9530 \| 0.795 \| 0.7693 \| 0.3989 \| 0.0578 \| \| No log \| 48.0 \| 336 \| 0.6383 \| 0.8 \| 0.4476 \| 0.9992 \| 0.8000 \| 0.7804 \| 0.4084 \| 0.0579 \| \| No log \| 49.0 \| 343 \| 0.6353 \| 0.8 \| 0.4424 \| 0.8500 \| 0.8000 \| 0.7756 \| 0.4055 \| 0.0546 \| \| No log \| 50.0 \| 350 \| 0.6381 \| 0.795 \| 0.4470 \| 0.9931 \| 0.795 \| 0.7691 \| 0.4170 \| 0.0573 \| \| No log \| 51.0 \| 357 \| 0.6374 \| 0.795 \| 0.4477 \| 0.9729 \| 0.795 \| 0.7630 \| 0.4076 \| 0.0563 \| \| No log \| 52.0 \| 364 \| 0.6377 \| 0.8 \| 0.4481 \| 0.9846 \| 0.8000 \| 0.7759 \| 0.4212 \| 0.0555 \| \| No log \| 53.0 \| 371 \| 0.6378 \| 0.795 \| 0.4485 \| 0.9379 \| 0.795 \| 0.7733 \| 0.4052 \| 0.0565 \| \| No log \| 54.0 \| 378 \| 0.6385 \| 0.79 \| 0.4477 \| 0.9900 \| 0.79 \| 0.7684 \| 0.4165 \| 0.0571 \| \| No log \| 55.0 \| 385 \| 0.6371 \| 0.81 \| 0.4466 \| 0.9178 \| 0.81 \| 0.7867 \| 0.4149 \| 0.0546 \| \| No log \| 56.0 \| 392 \| 0.6373 \| 0.795 \| 0.4460 \| 0.9254 \| 0.795 \| 0.7692 \| 0.4081 \| 0.0568 \| \| No log \| 57.0 \| 399 \| 0.6376 \| 0.79 \| 0.4476 \| 0.9194 \| 0.79 \| 0.7596 \| 0.3996 \| 0.0568 \| \| No log \| 58.0 \| 406 \| 0.6380 \| 0.79 \| 0.4477 \| 0.9259 \| 0.79 \| 0.7619 \| 0.4024 \| 0.0575 \| \| No log \| 59.0 \| 413 \| 0.6377 \| 0.8 \| 0.4474 \| 0.9100 \| 0.8000 \| 0.7806 \| 0.4096 \| 0.0569 \| \| No log \| 60.0 \| 420 \| 0.6378 \| 0.8 \| 0.4481 \| 0.9189 \| 0.8000 \| 0.7806 \| 0.4076 \| 0.0566 \| \| No log \| 61.0 \| 427 \| 0.6378 \| 0.795 \| 0.4478 \| 0.9860 \| 0.795 \| 0.7709 \| 0.3994 \| 0.0566 \| \| No log \| 62.0 \| 434 \| 0.6380 \| 0.795 \| 0.4480 \| 0.9189 \| 0.795 \| 0.7692 \| 0.4070 \| 0.0564 \| \| No log \| 63.0 \| 441 \| 0.6381 \| 0.8 \| 0.4482 \| 0.9195 \| 0.8000 \| 0.7806 \| 0.4047 \| 0.0568 \| \| No log \| 64.0 \| 448 \| 0.6379 \| 0.8 \| 0.4480 \| 0.9223 \| 0.8000 \| 0.7806 \| 0.4224 \| 0.0563 \| \| No log \| 65.0 \| 455 \| 0.6382 \| 0.8 \| 0.4481 \| 0.9196 \| 0.8000 \| 0.7806 \| 0.4113 \| 0.0569 \| \| No log \| 66.0 \| 462 \| 0.6381 \| 0.8 \| 0.4484 \| 0.9200 \| 0.8000 \| 0.7806 \| 0.4308 \| 0.0566 \| \| No log \| 67.0 \| 469 \| 0.6379 \| 0.8 \| 0.4479 \| 0.9198 \| 0.8000 \| 0.7806 \| 0.4186 \| 0.0566 \| \| No log \| 68.0 \| 476 \| 0.6378 \| 0.8 \| 0.4476 \| 0.9167 \| 0.8000 \| 0.7806 \| 0.4166 \| 0.0569 \| \| No log \| 69.0 \| 483 \| 0.6380 \| 0.8 \| 0.4481 \| 0.9179 \| 0.8000 \| 0.7806 \| 0.4254 \| 0.0566 \| \| No log \| 70.0 \| 490 \| 0.6384 \| 0.795 \| 0.4486 \| 0.9225 \| 0.795 \| 0.7709 \| 0.4158 \| 0.0566 \| \| No log \| 71.0 \| 497 \| 0.6380 \| 0.795 \| 0.4476 \| 0.9211 \| 0.795 \| 0.7709 \| 0.4215 \| 0.0568 \| \| 0.5133 \| 72.0 \| 504 \| 0.6381 \| 0.795 \| 0.4480 \| 0.9232 \| 0.795 \| 0.7709 \| 0.4151 \| 0.0566 \| \| 0.5133 \| 73.0 \| 511 \| 0.6380 \| 0.795 \| 0.4479 \| 0.9242 \| 0.795 \| 0.7709 \| 0.4218 \| 0.0564 \| \| 0.5133 \| 74.0 \| 518 \| 0.6380 \| 0.795 \| 0.4478 \| 0.9231 \| 0.795 \| 0.7709 \| 0.4151 \| 0.0566 \| \| 0.5133 \| 75.0 \| 525 \| 0.6382 \| 0.795 \| 0.4484 \| 0.9245 \| 0.795 \| 0.7709 \| 0.4156 \| 0.0565 \| \| 0.5133 \| 76.0 \| 532 \| 0.6382 \| 0.795 \| 0.4481 \| 0.9216 \| 0.795 \| 0.7709 \| 0.4153 \| 0.0567 \| \| 0.5133 \| 77.0 \| 539 \| 0.6382 \| 0.795 \| 0.4481 \| 0.9231 \| 0.795 \| 0.7709 \| 0.4222 \| 0.0567 \| \| 0.5133 \| 78.0 \| 546 \| 0.6382 \| 0.795 \| 0.4481 \| 0.9210 \| 0.795 \| 0.7709 \| 0.4220 \| 0.0565 \| \| 0.5133 \| 79.0 \| 553 \| 0.6382 \| 0.795 \| 0.4480 \| 0.9220 \| 0.795 \| 0.7709 \| 0.4220 \| 0.0565 \| \| 0.5133 \| 80.0 \| 560 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9220 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 81.0 \| 567 \| 0.6383 \| 0.795 \| 0.4483 \| 0.9218 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 82.0 \| 574 \| 0.6382 \| 0.795 \| 0.4480 \| 0.9220 \| 0.795 \| 0.7709 \| 0.4221 \| 0.0568 \| \| 0.5133 \| 83.0 \| 581 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9240 \| 0.795 \| 0.7709 \| 0.4157 \| 0.0566 \| \| 0.5133 \| 84.0 \| 588 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9262 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0566 \| \| 0.5133 \| 85.0 \| 595 \| 0.6382 \| 0.795 \| 0.4481 \| 0.9235 \| 0.795 \| 0.7709 \| 0.4221 \| 0.0566 \| \| 0.5133 \| 86.0 \| 602 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9236 \| 0.795 \| 0.7709 \| 0.4225 \| 0.0566 \| \| 0.5133 \| 87.0 \| 609 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9235 \| 0.795 \| 0.7709 \| 0.4225 \| 0.0567 \| \| 0.5133 \| 88.0 \| 616 \| 0.6384 \| 0.795 \| 0.4483 \| 0.9250 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0566 \| \| 0.5133 \| 89.0 \| 623 \| 0.6384 \| 0.795 \| 0.4483 \| 0.9244 \| 0.795 \| 0.7709 \| 0.4223 \| 0.0567 \| \| 0.5133 \| 90.0 \| 630 \| 0.6384 \| 0.795 \| 0.4483 \| 0.9251 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 91.0 \| 637 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9246 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 92.0 \| 644 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9256 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 93.0 \| 651 \| 0.6385 \| 0.795 \| 0.4484 \| 0.9252 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 94.0 \| 658 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9245 \| 0.795 \| 0.7709 \| 0.4223 \| 0.0565 \| \| 0.5133 \| 95.0 \| 665 \| 0.6385 \| 0.795 \| 0.4484 \| 0.9254 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 96.0 \| 672 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9242 \| 0.795 \| 0.7709 \| 0.4225 \| 0.0566 \| \| 0.5133 \| 97.0 \| 679 \| 0.6384 \| 0.795 \| 0.4484 \| 0.9242 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 98.0 \| 686 \| 0.6385 \| 0.795 \| 0.4484 \| 0.9249 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0567 \| \| 0.5133 \| 99.0 \| 693 \| 0.6385 \| 0.795 \| 0.4484 \| 0.9252 \| 0.795 \| 0.7709 \| 0.4224 \| 0.0566 \| \| 0.5133 \| 100.0 \| 700 \| 0.6385 \| 0.795 \| 0.4484 \| 0.9250 \| 0.795 \| 0.7709 \| 0.4225 \| 0.0567 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/18-tiny_tobacco3482	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 18-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0652 - Accuracy: 0.29 - Brier Loss: 0.8849 - Nll: 4.7897 - F1 Micro: 0.29 - F1 Macro: 0.1634 - Ece: 0.2953 - Aurc: 0.5243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.0888 \| 0.1 \| 0.8987 \| 8.8684 \| 0.1000 \| 0.036 \| 0.1707 \| 0.9181 \| \| No log \| 2.0 \| 50 \| 0.0822 \| 0.07 \| 0.8987 \| 7.4095 \| 0.07 \| 0.0264 \| 0.1526 \| 0.9367 \| \| No log \| 3.0 \| 75 \| 0.0815 \| 0.17 \| 0.8985 \| 7.9603 \| 0.17 \| 0.0549 \| 0.2145 \| 0.8165 \| \| No log \| 4.0 \| 100 \| 0.0811 \| 0.175 \| 0.8981 \| 8.1220 \| 0.175 \| 0.0636 \| 0.2224 \| 0.6841 \| \| No log \| 5.0 \| 125 \| 0.0807 \| 0.18 \| 0.8980 \| 8.0205 \| 0.18 \| 0.0595 \| 0.2162 \| 0.6466 \| \| No log \| 6.0 \| 150 \| 0.0802 \| 0.16 \| 0.8979 \| 8.0022 \| 0.16 \| 0.0588 \| 0.2146 \| 0.6987 \| \| No log \| 7.0 \| 175 \| 0.0795 \| 0.16 \| 0.8978 \| 8.1389 \| 0.16 \| 0.1003 \| 0.2087 \| 0.8300 \| \| No log \| 8.0 \| 200 \| 0.0786 \| 0.165 \| 0.8973 \| 7.2103 \| 0.165 \| 0.0840 \| 0.2019 \| 0.7778 \| \| No log \| 9.0 \| 225 \| 0.0777 \| 0.135 \| 0.8971 \| 6.5106 \| 0.135 \| 0.0644 \| 0.2023 \| 0.9632 \| \| No log \| 10.0 \| 250 \| 0.0768 \| 0.12 \| 0.8971 \| 7.0176 \| 0.12 \| 0.0555 \| 0.1928 \| 0.9684 \| \| No log \| 11.0 \| 275 \| 0.0758 \| 0.08 \| 0.8962 \| 6.4528 \| 0.08 \| 0.0356 \| 0.1609 \| 0.9735 \| \| No log \| 12.0 \| 300 \| 0.0747 \| 0.08 \| 0.8954 \| 6.9898 \| 0.08 \| 0.0365 \| 0.1608 \| 0.9594 \| \| No log \| 13.0 \| 325 \| 0.0736 \| 0.14 \| 0.8941 \| 6.5287 \| 0.14 \| 0.0645 \| 0.2064 \| 0.6977 \| \| No log \| 14.0 \| 350 \| 0.0725 \| 0.17 \| 0.8937 \| 7.0519 \| 0.17 \| 0.0891 \| 0.2278 \| 0.6074 \| \| No log \| 15.0 \| 375 \| 0.0712 \| 0.17 \| 0.8919 \| 6.3769 \| 0.17 \| 0.0888 \| 0.2218 \| 0.6154 \| \| No log \| 16.0 \| 400 \| 0.0702 \| 0.18 \| 0.8912 \| 6.2503 \| 0.18 \| 0.1086 \| 0.2297 \| 0.6140 \| \| No log \| 17.0 \| 425 \| 0.0691 \| 0.16 \| 0.8909 \| 6.5096 \| 0.16 \| 0.1064 \| 0.2081 \| 0.6497 \| \| No log \| 18.0 \| 450 \| 0.0686 \| 0.175 \| 0.8904 \| 6.2506 \| 0.175 \| 0.1196 \| 0.2252 \| 0.6744 \| \| No log \| 19.0 \| 475 \| 0.0681 \| 0.16 \| 0.8891 \| 5.3019 \| 0.16 \| 0.1000 \| 0.2185 \| 0.7715 \| \| 0.0776 \| 20.0 \| 500 \| 0.0678 \| 0.155 \| 0.8887 \| 5.3003 \| 0.155 \| 0.0980 \| 0.2208 \| 0.7213 \| \| 0.0776 \| 21.0 \| 525 \| 0.0677 \| 0.155 \| 0.8891 \| 5.7776 \| 0.155 \| 0.0944 \| 0.2223 \| 0.7075 \| \| 0.0776 \| 22.0 \| 550 \| 0.0671 \| 0.185 \| 0.8885 \| 5.4219 \| 0.185 \| 0.0998 \| 0.2333 \| 0.6805 \| \| 0.0776 \| 23.0 \| 575 \| 0.0669 \| 0.17 \| 0.8887 \| 5.4554 \| 0.17 \| 0.0813 \| 0.2304 \| 0.6676 \| \| 0.0776 \| 24.0 \| 600 \| 0.0667 \| 0.175 \| 0.8877 \| 5.4877 \| 0.175 \| 0.0791 \| 0.2294 \| 0.6991 \| \| 0.0776 \| 25.0 \| 625 \| 0.0665 \| 0.18 \| 0.8876 \| 5.3499 \| 0.18 \| 0.0846 \| 0.2342 \| 0.6774 \| \| 0.0776 \| 26.0 \| 650 \| 0.0665 \| 0.18 \| 0.8870 \| 4.9329 \| 0.18 \| 0.0888 \| 0.2320 \| 0.6823 \| \| 0.0776 \| 27.0 \| 675 \| 0.0663 \| 0.21 \| 0.8872 \| 4.7787 \| 0.2100 \| 0.1204 \| 0.2521 \| 0.6698 \| \| 0.0776 \| 28.0 \| 700 \| 0.0665 \| 0.21 \| 0.8870 \| 4.8102 \| 0.2100 \| 0.1177 \| 0.2481 \| 0.6893 \| \| 0.0776 \| 29.0 \| 725 \| 0.0663 \| 0.2 \| 0.8866 \| 4.8474 \| 0.2000 \| 0.1124 \| 0.2433 \| 0.6616 \| \| 0.0776 \| 30.0 \| 750 \| 0.0663 \| 0.21 \| 0.8862 \| 4.6460 \| 0.2100 \| 0.1060 \| 0.2514 \| 0.6663 \| \| 0.0776 \| 31.0 \| 775 \| 0.0661 \| 0.215 \| 0.8874 \| 5.0331 \| 0.2150 \| 0.1219 \| 0.2585 \| 0.6564 \| \| 0.0776 \| 32.0 \| 800 \| 0.0661 \| 0.23 \| 0.8871 \| 4.9313 \| 0.23 \| 0.1299 \| 0.2600 \| 0.6596 \| \| 0.0776 \| 33.0 \| 825 \| 0.0661 \| 0.245 \| 0.8870 \| 5.2183 \| 0.245 \| 0.1400 \| 0.2729 \| 0.6216 \| \| 0.0776 \| 34.0 \| 850 \| 0.0660 \| 0.215 \| 0.8869 \| 5.0987 \| 0.2150 \| 0.1130 \| 0.2473 \| 0.6745 \| \| 0.0776 \| 35.0 \| 875 \| 0.0659 \| 0.245 \| 0.8872 \| 5.1536 \| 0.245 \| 0.1404 \| 0.2684 \| 0.6753 \| \| 0.0776 \| 36.0 \| 900 \| 0.0661 \| 0.23 \| 0.8865 \| 5.0364 \| 0.23 \| 0.1219 \| 0.2603 \| 0.7257 \| \| 0.0776 \| 37.0 \| 925 \| 0.0659 \| 0.25 \| 0.8866 \| 5.0315 \| 0.25 \| 0.1289 \| 0.2709 \| 0.6743 \| \| 0.0776 \| 38.0 \| 950 \| 0.0658 \| 0.245 \| 0.8865 \| 5.1304 \| 0.245 \| 0.1348 \| 0.2708 \| 0.6383 \| \| 0.0776 \| 39.0 \| 975 \| 0.0659 \| 0.255 \| 0.8865 \| 4.8854 \| 0.255 \| 0.1343 \| 0.2762 \| 0.6336 \| \| 0.0623 \| 40.0 \| 1000 \| 0.0658 \| 0.245 \| 0.8866 \| 4.8705 \| 0.245 \| 0.1287 \| 0.2709 \| 0.6663 \| \| 0.0623 \| 41.0 \| 1025 \| 0.0656 \| 0.245 \| 0.8872 \| 5.2534 \| 0.245 \| 0.1307 \| 0.2662 \| 0.6369 \| \| 0.0623 \| 42.0 \| 1050 \| 0.0658 \| 0.235 \| 0.8866 \| 5.1467 \| 0.235 \| 0.1220 \| 0.2630 \| 0.6834 \| \| 0.0623 \| 43.0 \| 1075 \| 0.0656 \| 0.235 \| 0.8868 \| 5.2909 \| 0.235 \| 0.1201 \| 0.2629 \| 0.6663 \| \| 0.0623 \| 44.0 \| 1100 \| 0.0655 \| 0.24 \| 0.8870 \| 5.3291 \| 0.24 \| 0.1315 \| 0.2600 \| 0.5948 \| \| 0.0623 \| 45.0 \| 1125 \| 0.0656 \| 0.25 \| 0.8867 \| 5.0194 \| 0.25 \| 0.1397 \| 0.2663 \| 0.6091 \| \| 0.0623 \| 46.0 \| 1150 \| 0.0656 \| 0.255 \| 0.8863 \| 5.0523 \| 0.255 \| 0.1303 \| 0.2729 \| 0.5687 \| \| 0.0623 \| 47.0 \| 1175 \| 0.0655 \| 0.25 \| 0.8863 \| 4.9527 \| 0.25 \| 0.1293 \| 0.2679 \| 0.5724 \| \| 0.0623 \| 48.0 \| 1200 \| 0.0654 \| 0.25 \| 0.8863 \| 5.0401 \| 0.25 \| 0.1307 \| 0.2588 \| 0.5783 \| \| 0.0623 \| 49.0 \| 1225 \| 0.0655 \| 0.25 \| 0.8862 \| 5.0198 \| 0.25 \| 0.1301 \| 0.2781 \| 0.6005 \| \| 0.0623 \| 50.0 \| 1250 \| 0.0653 \| 0.255 \| 0.8858 \| 5.0534 \| 0.255 \| 0.1311 \| 0.2732 \| 0.5601 \| \| 0.0623 \| 51.0 \| 1275 \| 0.0653 \| 0.26 \| 0.8861 \| 4.9558 \| 0.26 \| 0.1329 \| 0.2779 \| 0.5458 \| \| 0.0623 \| 52.0 \| 1300 \| 0.0653 \| 0.275 \| 0.8855 \| 4.8780 \| 0.275 \| 0.1497 \| 0.2863 \| 0.5410 \| \| 0.0623 \| 53.0 \| 1325 \| 0.0653 \| 0.27 \| 0.8854 \| 4.8854 \| 0.27 \| 0.1453 \| 0.2857 \| 0.5276 \| \| 0.0623 \| 54.0 \| 1350 \| 0.0652 \| 0.26 \| 0.8853 \| 4.9379 \| 0.26 \| 0.1322 \| 0.2803 \| 0.5457 \| \| 0.0623 \| 55.0 \| 1375 \| 0.0653 \| 0.26 \| 0.8852 \| 4.9101 \| 0.26 \| 0.1388 \| 0.2778 \| 0.5414 \| \| 0.0623 \| 56.0 \| 1400 \| 0.0653 \| 0.275 \| 0.8849 \| 4.8107 \| 0.275 \| 0.1541 \| 0.2881 \| 0.5204 \| \| 0.0623 \| 57.0 \| 1425 \| 0.0652 \| 0.26 \| 0.8850 \| 4.7712 \| 0.26 \| 0.1402 \| 0.2709 \| 0.5323 \| \| 0.0623 \| 58.0 \| 1450 \| 0.0652 \| 0.275 \| 0.8850 \| 4.8164 \| 0.275 \| 0.1618 \| 0.2885 \| 0.5288 \| \| 0.0623 \| 59.0 \| 1475 \| 0.0652 \| 0.27 \| 0.8848 \| 4.7627 \| 0.27 \| 0.1402 \| 0.2830 \| 0.5194 \| \| 0.0599 \| 60.0 \| 1500 \| 0.0652 \| 0.28 \| 0.8851 \| 4.8057 \| 0.28 \| 0.1624 \| 0.2889 \| 0.5304 \| \| 0.0599 \| 61.0 \| 1525 \| 0.0652 \| 0.26 \| 0.8851 \| 4.8892 \| 0.26 \| 0.1351 \| 0.2823 \| 0.5372 \| \| 0.0599 \| 62.0 \| 1550 \| 0.0651 \| 0.28 \| 0.8852 \| 4.8944 \| 0.28 \| 0.1625 \| 0.2889 \| 0.5320 \| \| 0.0599 \| 63.0 \| 1575 \| 0.0652 \| 0.275 \| 0.8849 \| 4.8180 \| 0.275 \| 0.1484 \| 0.2858 \| 0.5207 \| \| 0.0599 \| 64.0 \| 1600 \| 0.0652 \| 0.265 \| 0.8849 \| 4.8034 \| 0.265 \| 0.1366 \| 0.2807 \| 0.5431 \| \| 0.0599 \| 65.0 \| 1625 \| 0.0651 \| 0.28 \| 0.8852 \| 4.7696 \| 0.28 \| 0.1587 \| 0.2888 \| 0.5327 \| \| 0.0599 \| 66.0 \| 1650 \| 0.0652 \| 0.28 \| 0.8850 \| 4.8120 \| 0.28 \| 0.1561 \| 0.2886 \| 0.5134 \| \| 0.0599 \| 67.0 \| 1675 \| 0.0651 \| 0.28 \| 0.8850 \| 4.7193 \| 0.28 \| 0.1623 \| 0.2867 \| 0.5339 \| \| 0.0599 \| 68.0 \| 1700 \| 0.0652 \| 0.255 \| 0.8848 \| 4.7659 \| 0.255 \| 0.1287 \| 0.2726 \| 0.5406 \| \| 0.0599 \| 69.0 \| 1725 \| 0.0651 \| 0.275 \| 0.8849 \| 4.7354 \| 0.275 \| 0.1479 \| 0.2880 \| 0.5181 \| \| 0.0599 \| 70.0 \| 1750 \| 0.0651 \| 0.275 \| 0.8850 \| 4.8271 \| 0.275 \| 0.1543 \| 0.2893 \| 0.5313 \| \| 0.0599 \| 71.0 \| 1775 \| 0.0651 \| 0.275 \| 0.8849 \| 4.7387 \| 0.275 \| 0.1549 \| 0.2839 \| 0.5454 \| \| 0.0599 \| 72.0 \| 1800 \| 0.0652 \| 0.275 \| 0.8851 \| 4.7695 \| 0.275 \| 0.1475 \| 0.2849 \| 0.5313 \| \| 0.0599 \| 73.0 \| 1825 \| 0.0652 \| 0.275 \| 0.8848 \| 4.7794 \| 0.275 \| 0.1418 \| 0.2845 \| 0.5159 \| \| 0.0599 \| 74.0 \| 1850 \| 0.0652 \| 0.265 \| 0.8848 \| 4.7832 \| 0.265 \| 0.1334 \| 0.2827 \| 0.5350 \| \| 0.0599 \| 75.0 \| 1875 \| 0.0652 \| 0.27 \| 0.8849 \| 4.7822 \| 0.27 \| 0.1388 \| 0.2822 \| 0.5478 \| \| 0.0599 \| 76.0 \| 1900 \| 0.0652 \| 0.26 \| 0.8850 \| 4.7813 \| 0.26 \| 0.1293 \| 0.2720 \| 0.5386 \| \| 0.0599 \| 77.0 \| 1925 \| 0.0652 \| 0.265 \| 0.8849 \| 4.7949 \| 0.265 \| 0.1365 \| 0.2818 \| 0.5456 \| \| 0.0599 \| 78.0 \| 1950 \| 0.0652 \| 0.28 \| 0.8850 \| 4.7960 \| 0.28 \| 0.1509 \| 0.2878 \| 0.5361 \| \| 0.0599 \| 79.0 \| 1975 \| 0.0652 \| 0.265 \| 0.8849 \| 4.7800 \| 0.265 \| 0.1353 \| 0.2803 \| 0.5407 \| \| 0.059 \| 80.0 \| 2000 \| 0.0652 \| 0.265 \| 0.8849 \| 4.8926 \| 0.265 \| 0.1429 \| 0.2782 \| 0.5381 \| \| 0.059 \| 81.0 \| 2025 \| 0.0653 \| 0.27 \| 0.8850 \| 4.7975 \| 0.27 \| 0.1388 \| 0.2847 \| 0.5436 \| \| 0.059 \| 82.0 \| 2050 \| 0.0652 \| 0.275 \| 0.8849 \| 4.7902 \| 0.275 \| 0.1505 \| 0.2849 \| 0.5342 \| \| 0.059 \| 83.0 \| 2075 \| 0.0652 \| 0.265 \| 0.8850 \| 4.7813 \| 0.265 \| 0.1435 \| 0.2805 \| 0.5388 \| \| 0.059 \| 84.0 \| 2100 \| 0.0652 \| 0.265 \| 0.8849 \| 4.7866 \| 0.265 \| 0.1457 \| 0.2873 \| 0.5358 \| \| 0.059 \| 85.0 \| 2125 \| 0.0652 \| 0.275 \| 0.8850 \| 4.7877 \| 0.275 \| 0.1514 \| 0.2872 \| 0.5395 \| \| 0.059 \| 86.0 \| 2150 \| 0.0652 \| 0.265 \| 0.8849 \| 4.7870 \| 0.265 \| 0.1453 \| 0.2862 \| 0.5424 \| \| 0.059 \| 87.0 \| 2175 \| 0.0652 \| 0.275 \| 0.8849 \| 4.7887 \| 0.275 \| 0.1517 \| 0.2926 \| 0.5312 \| \| 0.059 \| 88.0 \| 2200 \| 0.0652 \| 0.27 \| 0.8849 \| 4.7862 \| 0.27 \| 0.1494 \| 0.2809 \| 0.5357 \| \| 0.059 \| 89.0 \| 2225 \| 0.0652 \| 0.275 \| 0.8849 \| 4.7911 \| 0.275 \| 0.1515 \| 0.2871 \| 0.5317 \| \| 0.059 \| 90.0 \| 2250 \| 0.0652 \| 0.28 \| 0.8849 \| 4.7825 \| 0.28 \| 0.1540 \| 0.2887 \| 0.5305 \| \| 0.059 \| 91.0 \| 2275 \| 0.0652 \| 0.28 \| 0.8849 \| 4.7892 \| 0.28 \| 0.1547 \| 0.2900 \| 0.5339 \| \| 0.059 \| 92.0 \| 2300 \| 0.0652 \| 0.29 \| 0.8848 \| 4.7848 \| 0.29 \| 0.1642 \| 0.2932 \| 0.5252 \| \| 0.059 \| 93.0 \| 2325 \| 0.0652 \| 0.29 \| 0.8848 \| 4.7823 \| 0.29 \| 0.1634 \| 0.2953 \| 0.5243 \| \| 0.059 \| 94.0 \| 2350 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7869 \| 0.29 \| 0.1631 \| 0.2932 \| 0.5239 \| \| 0.059 \| 95.0 \| 2375 \| 0.0652 \| 0.285 \| 0.8849 \| 4.7902 \| 0.285 \| 0.1615 \| 0.2893 \| 0.5254 \| \| 0.059 \| 96.0 \| 2400 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7894 \| 0.29 \| 0.1634 \| 0.2909 \| 0.5256 \| \| 0.059 \| 97.0 \| 2425 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7915 \| 0.29 \| 0.1642 \| 0.2932 \| 0.5244 \| \| 0.059 \| 98.0 \| 2450 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7902 \| 0.29 \| 0.1642 \| 0.2909 \| 0.5244 \| \| 0.059 \| 99.0 \| 2475 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7889 \| 0.29 \| 0.1634 \| 0.2953 \| 0.5241 \| \| 0.0586 \| 100.0 \| 2500 \| 0.0652 \| 0.29 \| 0.8849 \| 4.7897 \| 0.29 \| 0.1634 \| 0.2953 \| 0.5243 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/18-tiny_tobacco3482_kd_NKD_t1.0_g1.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 18-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0957 - Accuracy: 0.805 - Brier Loss: 0.2927 - Nll: 1.1753 - F1 Micro: 0.805 - F1 Macro: 0.7833 - Ece: 0.1572 - Aurc: 0.0655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 4.7898 \| 0.1 \| 1.0292 \| 9.4902 \| 0.1000 \| 0.0772 \| 0.3220 \| 0.9001 \| \| No log \| 2.0 \| 14 \| 3.9970 \| 0.1 \| 0.9420 \| 10.0981 \| 0.1000 \| 0.1071 \| 0.2441 \| 0.8581 \| \| No log \| 3.0 \| 21 \| 3.6641 \| 0.075 \| 0.8956 \| 9.5324 \| 0.075 \| 0.0777 \| 0.1896 \| 0.9137 \| \| No log \| 4.0 \| 28 \| 3.6014 \| 0.18 \| 0.8691 \| 9.6679 \| 0.18 \| 0.0781 \| 0.2345 \| 0.5824 \| \| No log \| 5.0 \| 35 \| 3.5833 \| 0.23 \| 0.8347 \| 9.6569 \| 0.23 \| 0.1572 \| 0.2618 \| 0.5205 \| \| No log \| 6.0 \| 42 \| 3.5576 \| 0.44 \| 0.7860 \| 5.9410 \| 0.44 \| 0.2946 \| 0.3475 \| 0.3232 \| \| No log \| 7.0 \| 49 \| 3.5400 \| 0.575 \| 0.7404 \| 4.2387 \| 0.575 \| 0.4638 \| 0.4007 \| 0.2294 \| \| No log \| 8.0 \| 56 \| 3.5319 \| 0.545 \| 0.7181 \| 4.5958 \| 0.545 \| 0.4482 \| 0.3502 \| 0.2374 \| \| No log \| 9.0 \| 63 \| 3.5405 \| 0.52 \| 0.7002 \| 3.9862 \| 0.52 \| 0.4101 \| 0.3148 \| 0.2506 \| \| No log \| 10.0 \| 70 \| 3.5341 \| 0.61 \| 0.6897 \| 3.2707 \| 0.61 \| 0.5118 \| 0.3775 \| 0.2235 \| \| No log \| 11.0 \| 77 \| 3.5259 \| 0.66 \| 0.6771 \| 2.6882 \| 0.66 \| 0.5201 \| 0.4365 \| 0.1420 \| \| No log \| 12.0 \| 84 \| 3.5215 \| 0.66 \| 0.6463 \| 2.4544 \| 0.66 \| 0.5387 \| 0.3750 \| 0.1664 \| \| No log \| 13.0 \| 91 \| 3.5363 \| 0.58 \| 0.6232 \| 2.3149 \| 0.58 \| 0.5090 \| 0.3285 \| 0.1858 \| \| No log \| 14.0 \| 98 \| 3.5161 \| 0.675 \| 0.6008 \| 2.6144 \| 0.675 \| 0.5411 \| 0.3690 \| 0.1237 \| \| No log \| 15.0 \| 105 \| 3.5073 \| 0.67 \| 0.5845 \| 2.1229 \| 0.67 \| 0.5577 \| 0.3405 \| 0.1350 \| \| No log \| 16.0 \| 112 \| 3.5272 \| 0.67 \| 0.5338 \| 2.4215 \| 0.67 \| 0.5603 \| 0.3154 \| 0.1325 \| \| No log \| 17.0 \| 119 \| 3.5332 \| 0.695 \| 0.5367 \| 2.1675 \| 0.695 \| 0.6056 \| 0.3140 \| 0.1071 \| \| No log \| 18.0 \| 126 \| 3.5659 \| 0.655 \| 0.4841 \| 1.9565 \| 0.655 \| 0.5559 \| 0.2600 \| 0.1365 \| \| No log \| 19.0 \| 133 \| 3.5438 \| 0.69 \| 0.4817 \| 1.8201 \| 0.69 \| 0.5735 \| 0.2574 \| 0.1202 \| \| No log \| 20.0 \| 140 \| 3.5019 \| 0.74 \| 0.4725 \| 1.6346 \| 0.74 \| 0.6486 \| 0.2939 \| 0.0931 \| \| No log \| 21.0 \| 147 \| 3.5236 \| 0.755 \| 0.4407 \| 1.3134 \| 0.755 \| 0.6811 \| 0.2762 \| 0.0820 \| \| No log \| 22.0 \| 154 \| 3.5303 \| 0.755 \| 0.4143 \| 1.2834 \| 0.755 \| 0.6843 \| 0.2434 \| 0.0806 \| \| No log \| 23.0 \| 161 \| 3.5541 \| 0.77 \| 0.4034 \| 1.4417 \| 0.7700 \| 0.6891 \| 0.2382 \| 0.0842 \| \| No log \| 24.0 \| 168 \| 3.5675 \| 0.765 \| 0.3853 \| 1.6692 \| 0.765 \| 0.7072 \| 0.2309 \| 0.0807 \| \| No log \| 25.0 \| 175 \| 3.5411 \| 0.745 \| 0.3914 \| 1.2777 \| 0.745 \| 0.6720 \| 0.2271 \| 0.0784 \| \| No log \| 26.0 \| 182 \| 3.5877 \| 0.75 \| 0.3710 \| 1.4838 \| 0.75 \| 0.6717 \| 0.2082 \| 0.0789 \| \| No log \| 27.0 \| 189 \| 3.6026 \| 0.77 \| 0.3483 \| 1.4211 \| 0.7700 \| 0.7018 \| 0.2089 \| 0.0694 \| \| No log \| 28.0 \| 196 \| 3.6374 \| 0.78 \| 0.3365 \| 1.3205 \| 0.78 \| 0.7181 \| 0.1953 \| 0.0694 \| \| No log \| 29.0 \| 203 \| 3.7319 \| 0.775 \| 0.3538 \| 1.2749 \| 0.775 \| 0.7012 \| 0.2149 \| 0.0814 \| \| No log \| 30.0 \| 210 \| 3.6359 \| 0.805 \| 0.3291 \| 1.3272 \| 0.805 \| 0.7761 \| 0.1991 \| 0.0637 \| \| No log \| 31.0 \| 217 \| 3.7160 \| 0.785 \| 0.3337 \| 1.2632 \| 0.785 \| 0.7445 \| 0.1727 \| 0.0757 \| \| No log \| 32.0 \| 224 \| 3.6810 \| 0.8 \| 0.3234 \| 1.3720 \| 0.8000 \| 0.7636 \| 0.1999 \| 0.0649 \| \| No log \| 33.0 \| 231 \| 3.7139 \| 0.82 \| 0.3221 \| 1.2150 \| 0.82 \| 0.7919 \| 0.2051 \| 0.0677 \| \| No log \| 34.0 \| 238 \| 3.7286 \| 0.795 \| 0.3130 \| 1.0622 \| 0.795 \| 0.7575 \| 0.1919 \| 0.0639 \| \| No log \| 35.0 \| 245 \| 3.7807 \| 0.795 \| 0.3154 \| 1.0146 \| 0.795 \| 0.7672 \| 0.1565 \| 0.0714 \| \| No log \| 36.0 \| 252 \| 3.6802 \| 0.815 \| 0.3131 \| 1.0083 \| 0.815 \| 0.7933 \| 0.2051 \| 0.0626 \| \| No log \| 37.0 \| 259 \| 3.7369 \| 0.81 \| 0.3168 \| 1.0017 \| 0.81 \| 0.7862 \| 0.1792 \| 0.0690 \| \| No log \| 38.0 \| 266 \| 3.7638 \| 0.82 \| 0.2971 \| 1.3357 \| 0.82 \| 0.7977 \| 0.1913 \| 0.0628 \| \| No log \| 39.0 \| 273 \| 3.7415 \| 0.825 \| 0.2954 \| 1.0423 \| 0.825 \| 0.8072 \| 0.1893 \| 0.0599 \| \| No log \| 40.0 \| 280 \| 3.8005 \| 0.785 \| 0.3140 \| 1.0817 \| 0.785 \| 0.7453 \| 0.1694 \| 0.0684 \| \| No log \| 41.0 \| 287 \| 3.7901 \| 0.82 \| 0.3127 \| 1.0853 \| 0.82 \| 0.7993 \| 0.1789 \| 0.0673 \| \| No log \| 42.0 \| 294 \| 3.7811 \| 0.825 \| 0.3019 \| 1.2712 \| 0.825 \| 0.8020 \| 0.1644 \| 0.0644 \| \| No log \| 43.0 \| 301 \| 3.7689 \| 0.81 \| 0.3110 \| 0.8553 \| 0.81 \| 0.7932 \| 0.1785 \| 0.0645 \| \| No log \| 44.0 \| 308 \| 3.7796 \| 0.82 \| 0.2919 \| 1.2589 \| 0.82 \| 0.7972 \| 0.1875 \| 0.0643 \| \| No log \| 45.0 \| 315 \| 3.8005 \| 0.805 \| 0.3036 \| 1.1993 \| 0.805 \| 0.7789 \| 0.1840 \| 0.0660 \| \| No log \| 46.0 \| 322 \| 3.7811 \| 0.82 \| 0.2909 \| 1.0962 \| 0.82 \| 0.8004 \| 0.1735 \| 0.0618 \| \| No log \| 47.0 \| 329 \| 3.8145 \| 0.8 \| 0.3040 \| 1.1968 \| 0.8000 \| 0.7759 \| 0.1795 \| 0.0671 \| \| No log \| 48.0 \| 336 \| 3.7969 \| 0.835 \| 0.2816 \| 1.1019 \| 0.835 \| 0.8118 \| 0.1624 \| 0.0603 \| \| No log \| 49.0 \| 343 \| 3.8020 \| 0.815 \| 0.2855 \| 1.0383 \| 0.815 \| 0.7978 \| 0.1556 \| 0.0639 \| \| No log \| 50.0 \| 350 \| 3.8049 \| 0.815 \| 0.2884 \| 1.1121 \| 0.815 \| 0.7935 \| 0.1608 \| 0.0616 \| \| No log \| 51.0 \| 357 \| 3.8048 \| 0.81 \| 0.2873 \| 1.1173 \| 0.81 \| 0.7898 \| 0.1574 \| 0.0632 \| \| No log \| 52.0 \| 364 \| 3.8581 \| 0.8 \| 0.2923 \| 1.1257 \| 0.8000 \| 0.7767 \| 0.1436 \| 0.0664 \| \| No log \| 53.0 \| 371 \| 3.8565 \| 0.79 \| 0.2984 \| 1.0513 \| 0.79 \| 0.7670 \| 0.1622 \| 0.0668 \| \| No log \| 54.0 \| 378 \| 3.8787 \| 0.805 \| 0.2901 \| 1.0619 \| 0.805 \| 0.7874 \| 0.1335 \| 0.0655 \| \| No log \| 55.0 \| 385 \| 3.8777 \| 0.805 \| 0.2940 \| 1.0378 \| 0.805 \| 0.7883 \| 0.1450 \| 0.0647 \| \| No log \| 56.0 \| 392 \| 3.8743 \| 0.805 \| 0.2906 \| 1.1702 \| 0.805 \| 0.7849 \| 0.1610 \| 0.0634 \| \| No log \| 57.0 \| 399 \| 3.9082 \| 0.795 \| 0.2959 \| 1.0951 \| 0.795 \| 0.7711 \| 0.1761 \| 0.0662 \| \| No log \| 58.0 \| 406 \| 3.8894 \| 0.8 \| 0.2898 \| 1.0979 \| 0.8000 \| 0.7816 \| 0.1774 \| 0.0638 \| \| No log \| 59.0 \| 413 \| 3.9005 \| 0.825 \| 0.2914 \| 1.2358 \| 0.825 \| 0.8088 \| 0.1687 \| 0.0637 \| \| No log \| 60.0 \| 420 \| 3.9115 \| 0.815 \| 0.2863 \| 1.0318 \| 0.815 \| 0.7928 \| 0.1672 \| 0.0640 \| \| No log \| 61.0 \| 427 \| 3.9172 \| 0.805 \| 0.2956 \| 1.1397 \| 0.805 \| 0.7884 \| 0.1646 \| 0.0667 \| \| No log \| 62.0 \| 434 \| 3.8993 \| 0.82 \| 0.2862 \| 1.2349 \| 0.82 \| 0.8001 \| 0.1544 \| 0.0645 \| \| No log \| 63.0 \| 441 \| 3.9334 \| 0.825 \| 0.2896 \| 1.1718 \| 0.825 \| 0.8061 \| 0.1662 \| 0.0646 \| \| No log \| 64.0 \| 448 \| 3.9179 \| 0.815 \| 0.2861 \| 1.1727 \| 0.815 \| 0.7966 \| 0.1592 \| 0.0650 \| \| No log \| 65.0 \| 455 \| 3.9489 \| 0.8 \| 0.2981 \| 1.1681 \| 0.8000 \| 0.7805 \| 0.1522 \| 0.0674 \| \| No log \| 66.0 \| 462 \| 3.9372 \| 0.81 \| 0.2855 \| 1.1041 \| 0.81 \| 0.7870 \| 0.1709 \| 0.0647 \| \| No log \| 67.0 \| 469 \| 3.9651 \| 0.8 \| 0.2935 \| 1.1723 \| 0.8000 \| 0.7816 \| 0.1492 \| 0.0667 \| \| No log \| 68.0 \| 476 \| 3.9600 \| 0.815 \| 0.2903 \| 1.1687 \| 0.815 \| 0.7950 \| 0.1466 \| 0.0650 \| \| No log \| 69.0 \| 483 \| 3.9695 \| 0.82 \| 0.2908 \| 1.1251 \| 0.82 \| 0.8026 \| 0.1532 \| 0.0654 \| \| No log \| 70.0 \| 490 \| 3.9817 \| 0.805 \| 0.2915 \| 1.1879 \| 0.805 \| 0.7861 \| 0.1537 \| 0.0657 \| \| No log \| 71.0 \| 497 \| 3.9838 \| 0.81 \| 0.2899 \| 1.1688 \| 0.81 \| 0.7892 \| 0.1538 \| 0.0648 \| \| 3.4085 \| 72.0 \| 504 \| 3.9960 \| 0.805 \| 0.2910 \| 1.1702 \| 0.805 \| 0.7904 \| 0.1568 \| 0.0657 \| \| 3.4085 \| 73.0 \| 511 \| 4.0046 \| 0.8 \| 0.2931 \| 1.1743 \| 0.8000 \| 0.7800 \| 0.1529 \| 0.0658 \| \| 3.4085 \| 74.0 \| 518 \| 4.0115 \| 0.815 \| 0.2917 \| 1.1718 \| 0.815 \| 0.7968 \| 0.1589 \| 0.0647 \| \| 3.4085 \| 75.0 \| 525 \| 4.0205 \| 0.805 \| 0.2920 \| 1.1719 \| 0.805 \| 0.7833 \| 0.1575 \| 0.0654 \| \| 3.4085 \| 76.0 \| 532 \| 4.0272 \| 0.805 \| 0.2919 \| 1.1725 \| 0.805 \| 0.7833 \| 0.1547 \| 0.0659 \| \| 3.4085 \| 77.0 \| 539 \| 4.0323 \| 0.81 \| 0.2923 \| 1.1720 \| 0.81 \| 0.7892 \| 0.1547 \| 0.0653 \| \| 3.4085 \| 78.0 \| 546 \| 4.0364 \| 0.81 \| 0.2907 \| 1.1715 \| 0.81 \| 0.7892 \| 0.1607 \| 0.0650 \| \| 3.4085 \| 79.0 \| 553 \| 4.0405 \| 0.81 \| 0.2910 \| 1.1716 \| 0.81 \| 0.7892 \| 0.1451 \| 0.0650 \| \| 3.4085 \| 80.0 \| 560 \| 4.0476 \| 0.81 \| 0.2917 \| 1.1743 \| 0.81 \| 0.7892 \| 0.1453 \| 0.0650 \| \| 3.4085 \| 81.0 \| 567 \| 4.0529 \| 0.805 \| 0.2921 \| 1.1736 \| 0.805 \| 0.7833 \| 0.1573 \| 0.0654 \| \| 3.4085 \| 82.0 \| 574 \| 4.0570 \| 0.805 \| 0.2919 \| 1.1741 \| 0.805 \| 0.7861 \| 0.1717 \| 0.0655 \| \| 3.4085 \| 83.0 \| 581 \| 4.0601 \| 0.81 \| 0.2918 \| 1.1727 \| 0.81 \| 0.7892 \| 0.1508 \| 0.0650 \| \| 3.4085 \| 84.0 \| 588 \| 4.0643 \| 0.81 \| 0.2919 \| 1.1743 \| 0.81 \| 0.7892 \| 0.1507 \| 0.0652 \| \| 3.4085 \| 85.0 \| 595 \| 4.0678 \| 0.81 \| 0.2922 \| 1.1744 \| 0.81 \| 0.7892 \| 0.1552 \| 0.0651 \| \| 3.4085 \| 86.0 \| 602 \| 4.0743 \| 0.81 \| 0.2925 \| 1.1746 \| 0.81 \| 0.7892 \| 0.1526 \| 0.0651 \| \| 3.4085 \| 87.0 \| 609 \| 4.0758 \| 0.805 \| 0.2924 \| 1.1753 \| 0.805 \| 0.7833 \| 0.1718 \| 0.0653 \| \| 3.4085 \| 88.0 \| 616 \| 4.0796 \| 0.805 \| 0.2924 \| 1.1758 \| 0.805 \| 0.7833 \| 0.1567 \| 0.0654 \| \| 3.4085 \| 89.0 \| 623 \| 4.0803 \| 0.81 \| 0.2920 \| 1.1742 \| 0.81 \| 0.7892 \| 0.1587 \| 0.0650 \| \| 3.4085 \| 90.0 \| 630 \| 4.0842 \| 0.81 \| 0.2925 \| 1.1744 \| 0.81 \| 0.7892 \| 0.1529 \| 0.0651 \| \| 3.4085 \| 91.0 \| 637 \| 4.0864 \| 0.805 \| 0.2926 \| 1.1752 \| 0.805 \| 0.7833 \| 0.1568 \| 0.0654 \| \| 3.4085 \| 92.0 \| 644 \| 4.0880 \| 0.81 \| 0.2925 \| 1.1757 \| 0.81 \| 0.7892 \| 0.1526 \| 0.0651 \| \| 3.4085 \| 93.0 \| 651 \| 4.0903 \| 0.805 \| 0.2927 \| 1.1752 \| 0.805 \| 0.7833 \| 0.1567 \| 0.0654 \| \| 3.4085 \| 94.0 \| 658 \| 4.0918 \| 0.805 \| 0.2927 \| 1.1750 \| 0.805 \| 0.7833 \| 0.1572 \| 0.0655 \| \| 3.4085 \| 95.0 \| 665 \| 4.0927 \| 0.805 \| 0.2926 \| 1.1750 \| 0.805 \| 0.7833 \| 0.1570 \| 0.0655 \| \| 3.4085 \| 96.0 \| 672 \| 4.0937 \| 0.805 \| 0.2927 \| 1.1751 \| 0.805 \| 0.7833 \| 0.1572 \| 0.0655 \| \| 3.4085 \| 97.0 \| 679 \| 4.0946 \| 0.805 \| 0.2926 \| 1.1750 \| 0.805 \| 0.7833 \| 0.1573 \| 0.0655 \| \| 3.4085 \| 98.0 \| 686 \| 4.0950 \| 0.805 \| 0.2926 \| 1.1752 \| 0.805 \| 0.7833 \| 0.1572 \| 0.0655 \| \| 3.4085 \| 99.0 \| 693 \| 4.0955 \| 0.805 \| 0.2927 \| 1.1753 \| 0.805 \| 0.7833 \| 0.1572 \| 0.0655 \| \| 3.4085 \| 100.0 \| 700 \| 4.0957 \| 0.805 \| 0.2927 \| 1.1753 \| 0.805 \| 0.7833 \| 0.1572 \| 0.0655 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/18-tiny_tobacco3482_hint_	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 18-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 71.6043 - Accuracy: 0.85 - Brier Loss: 0.2638 - Nll: 1.3122 - F1 Micro: 0.85 - F1 Macro: 0.8425 - Ece: 0.1342 - Aurc: 0.0355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 74.9320 \| 0.26 \| 0.8713 \| 4.9021 \| 0.26 \| 0.1920 \| 0.3072 \| 0.7822 \| \| No log \| 2.0 \| 50 \| 74.1171 \| 0.54 \| 0.5982 \| 2.7516 \| 0.54 \| 0.4539 \| 0.2763 \| 0.2591 \| \| No log \| 3.0 \| 75 \| 73.6525 \| 0.685 \| 0.4431 \| 1.5999 \| 0.685 \| 0.6196 \| 0.1890 \| 0.1355 \| \| No log \| 4.0 \| 100 \| 73.3675 \| 0.735 \| 0.3894 \| 1.5393 \| 0.735 \| 0.7201 \| 0.2334 \| 0.1214 \| \| No log \| 5.0 \| 125 \| 73.0199 \| 0.785 \| 0.3130 \| 1.6799 \| 0.785 \| 0.7426 \| 0.1738 \| 0.0705 \| \| No log \| 6.0 \| 150 \| 72.7837 \| 0.8 \| 0.3068 \| 1.5290 \| 0.8000 \| 0.7711 \| 0.1531 \| 0.0582 \| \| No log \| 7.0 \| 175 \| 72.7218 \| 0.76 \| 0.3913 \| 1.8944 \| 0.76 \| 0.7334 \| 0.1998 \| 0.0987 \| \| No log \| 8.0 \| 200 \| 72.7351 \| 0.695 \| 0.4805 \| 1.4948 \| 0.695 \| 0.6565 \| 0.2585 \| 0.1329 \| \| No log \| 9.0 \| 225 \| 72.2520 \| 0.79 \| 0.3279 \| 1.5198 \| 0.79 \| 0.7440 \| 0.1878 \| 0.0704 \| \| No log \| 10.0 \| 250 \| 72.3792 \| 0.785 \| 0.3370 \| 1.7982 \| 0.785 \| 0.7687 \| 0.1655 \| 0.0757 \| \| No log \| 11.0 \| 275 \| 72.2408 \| 0.765 \| 0.3542 \| 1.6304 \| 0.765 \| 0.7364 \| 0.1838 \| 0.0660 \| \| No log \| 12.0 \| 300 \| 72.1003 \| 0.76 \| 0.3664 \| 1.4090 \| 0.76 \| 0.7247 \| 0.1832 \| 0.0817 \| \| No log \| 13.0 \| 325 \| 72.4671 \| 0.77 \| 0.3886 \| 1.7820 \| 0.7700 \| 0.7380 \| 0.1985 \| 0.0833 \| \| No log \| 14.0 \| 350 \| 72.1519 \| 0.785 \| 0.3633 \| 1.5939 \| 0.785 \| 0.7563 \| 0.1814 \| 0.0687 \| \| No log \| 15.0 \| 375 \| 72.2206 \| 0.775 \| 0.3723 \| 1.6025 \| 0.775 \| 0.7406 \| 0.1824 \| 0.0800 \| \| No log \| 16.0 \| 400 \| 72.0554 \| 0.805 \| 0.3080 \| 1.4448 \| 0.805 \| 0.7520 \| 0.1577 \| 0.0485 \| \| No log \| 17.0 \| 425 \| 72.3130 \| 0.8 \| 0.3412 \| 1.7255 \| 0.8000 \| 0.7653 \| 0.1637 \| 0.0694 \| \| No log \| 18.0 \| 450 \| 72.1019 \| 0.815 \| 0.3228 \| 1.4827 \| 0.815 \| 0.7918 \| 0.1753 \| 0.0592 \| \| No log \| 19.0 \| 475 \| 72.2643 \| 0.8 \| 0.3558 \| 1.5960 \| 0.8000 \| 0.7604 \| 0.1677 \| 0.0737 \| \| 71.4928 \| 20.0 \| 500 \| 71.9509 \| 0.78 \| 0.3398 \| 1.5615 \| 0.78 \| 0.7596 \| 0.1683 \| 0.0561 \| \| 71.4928 \| 21.0 \| 525 \| 71.9389 \| 0.82 \| 0.2947 \| 1.5336 \| 0.82 \| 0.8008 \| 0.1561 \| 0.0538 \| \| 71.4928 \| 22.0 \| 550 \| 72.0399 \| 0.83 \| 0.2843 \| 1.4576 \| 0.83 \| 0.7914 \| 0.1347 \| 0.0523 \| \| 71.4928 \| 23.0 \| 575 \| 72.0529 \| 0.815 \| 0.3263 \| 1.3174 \| 0.815 \| 0.7923 \| 0.1677 \| 0.0523 \| \| 71.4928 \| 24.0 \| 600 \| 72.3487 \| 0.775 \| 0.3838 \| 1.5418 \| 0.775 \| 0.7560 \| 0.1880 \| 0.0794 \| \| 71.4928 \| 25.0 \| 625 \| 71.9154 \| 0.825 \| 0.2949 \| 1.2538 \| 0.825 \| 0.7992 \| 0.1471 \| 0.0584 \| \| 71.4928 \| 26.0 \| 650 \| 72.0222 \| 0.815 \| 0.3151 \| 1.5809 \| 0.815 \| 0.7830 \| 0.1601 \| 0.0594 \| \| 71.4928 \| 27.0 \| 675 \| 72.0422 \| 0.815 \| 0.3269 \| 1.5161 \| 0.815 \| 0.7954 \| 0.1606 \| 0.0597 \| \| 71.4928 \| 28.0 \| 700 \| 72.0172 \| 0.845 \| 0.2828 \| 1.3388 \| 0.845 \| 0.8350 \| 0.1447 \| 0.0649 \| \| 71.4928 \| 29.0 \| 725 \| 71.9113 \| 0.84 \| 0.2685 \| 1.2082 \| 0.8400 \| 0.8202 \| 0.1365 \| 0.0562 \| \| 71.4928 \| 30.0 \| 750 \| 71.9516 \| 0.84 \| 0.2856 \| 1.2664 \| 0.8400 \| 0.8359 \| 0.1415 \| 0.0563 \| \| 71.4928 \| 31.0 \| 775 \| 71.8583 \| 0.835 \| 0.2782 \| 1.2979 \| 0.835 \| 0.8277 \| 0.1447 \| 0.0545 \| \| 71.4928 \| 32.0 \| 800 \| 71.9071 \| 0.84 \| 0.2766 \| 1.2772 \| 0.8400 \| 0.8359 \| 0.1378 \| 0.0546 \| \| 71.4928 \| 33.0 \| 825 \| 71.8580 \| 0.85 \| 0.2699 \| 1.2985 \| 0.85 \| 0.8482 \| 0.1351 \| 0.0525 \| \| 71.4928 \| 34.0 \| 850 \| 71.8499 \| 0.835 \| 0.2872 \| 1.3022 \| 0.835 \| 0.8292 \| 0.1462 \| 0.0532 \| \| 71.4928 \| 35.0 \| 875 \| 72.0085 \| 0.84 \| 0.2897 \| 1.3042 \| 0.8400 \| 0.8323 \| 0.1420 \| 0.0616 \| \| 71.4928 \| 36.0 \| 900 \| 71.8423 \| 0.82 \| 0.2929 \| 1.2266 \| 0.82 \| 0.8056 \| 0.1543 \| 0.0521 \| \| 71.4928 \| 37.0 \| 925 \| 71.7886 \| 0.845 \| 0.2807 \| 1.2181 \| 0.845 \| 0.8254 \| 0.1332 \| 0.0400 \| \| 71.4928 \| 38.0 \| 950 \| 71.8857 \| 0.83 \| 0.2877 \| 1.4036 \| 0.83 \| 0.8166 \| 0.1490 \| 0.0480 \| \| 71.4928 \| 39.0 \| 975 \| 71.9388 \| 0.83 \| 0.2877 \| 1.3374 \| 0.83 \| 0.8119 \| 0.1459 \| 0.0451 \| \| 70.1673 \| 40.0 \| 1000 \| 72.0368 \| 0.8 \| 0.3368 \| 1.7112 \| 0.8000 \| 0.7897 \| 0.1741 \| 0.0578 \| \| 70.1673 \| 41.0 \| 1025 \| 72.0295 \| 0.8 \| 0.3208 \| 1.5473 \| 0.8000 \| 0.7862 \| 0.1587 \| 0.0622 \| \| 70.1673 \| 42.0 \| 1050 \| 71.7048 \| 0.86 \| 0.2547 \| 1.3240 \| 0.8600 \| 0.8374 \| 0.1257 \| 0.0408 \| \| 70.1673 \| 43.0 \| 1075 \| 71.7541 \| 0.835 \| 0.2680 \| 1.4095 \| 0.835 \| 0.8178 \| 0.1418 \| 0.0445 \| \| 70.1673 \| 44.0 \| 1100 \| 71.7746 \| 0.845 \| 0.2721 \| 1.5529 \| 0.845 \| 0.8383 \| 0.1412 \| 0.0405 \| \| 70.1673 \| 45.0 \| 1125 \| 71.7661 \| 0.83 \| 0.2908 \| 1.5315 \| 0.83 \| 0.8104 \| 0.1415 \| 0.0437 \| \| 70.1673 \| 46.0 \| 1150 \| 71.7563 \| 0.84 \| 0.2787 \| 1.4088 \| 0.8400 \| 0.8238 \| 0.1360 \| 0.0416 \| \| 70.1673 \| 47.0 \| 1175 \| 71.7670 \| 0.84 \| 0.2709 \| 1.2801 \| 0.8400 \| 0.8303 \| 0.1322 \| 0.0401 \| \| 70.1673 \| 48.0 \| 1200 \| 71.7458 \| 0.84 \| 0.2699 \| 1.4180 \| 0.8400 \| 0.8265 \| 0.1433 \| 0.0397 \| \| 70.1673 \| 49.0 \| 1225 \| 71.7226 \| 0.84 \| 0.2653 \| 1.4126 \| 0.8400 \| 0.8265 \| 0.1282 \| 0.0390 \| \| 70.1673 \| 50.0 \| 1250 \| 71.7163 \| 0.85 \| 0.2608 \| 1.4227 \| 0.85 \| 0.8402 \| 0.1339 \| 0.0394 \| \| 70.1673 \| 51.0 \| 1275 \| 71.7044 \| 0.845 \| 0.2612 \| 1.4130 \| 0.845 \| 0.8371 \| 0.1314 \| 0.0387 \| \| 70.1673 \| 52.0 \| 1300 \| 71.6821 \| 0.85 \| 0.2545 \| 1.4880 \| 0.85 \| 0.8392 \| 0.1286 \| 0.0383 \| \| 70.1673 \| 53.0 \| 1325 \| 71.6764 \| 0.845 \| 0.2598 \| 1.3776 \| 0.845 \| 0.8301 \| 0.1299 \| 0.0377 \| \| 70.1673 \| 54.0 \| 1350 \| 71.6750 \| 0.855 \| 0.2590 \| 1.3404 \| 0.855 \| 0.8479 \| 0.1361 \| 0.0383 \| \| 70.1673 \| 55.0 \| 1375 \| 71.7192 \| 0.855 \| 0.2543 \| 1.4804 \| 0.855 \| 0.8482 \| 0.1346 \| 0.0388 \| \| 70.1673 \| 56.0 \| 1400 \| 71.6907 \| 0.85 \| 0.2552 \| 1.4102 \| 0.85 \| 0.8389 \| 0.1274 \| 0.0377 \| \| 70.1673 \| 57.0 \| 1425 \| 71.6778 \| 0.85 \| 0.2572 \| 1.4026 \| 0.85 \| 0.8392 \| 0.1319 \| 0.0389 \| \| 70.1673 \| 58.0 \| 1450 \| 71.6735 \| 0.85 \| 0.2559 \| 1.4062 \| 0.85 \| 0.8394 \| 0.1288 \| 0.0386 \| \| 70.1673 \| 59.0 \| 1475 \| 71.6938 \| 0.855 \| 0.2549 \| 1.4710 \| 0.855 \| 0.8482 \| 0.1309 \| 0.0387 \| \| 69.9715 \| 60.0 \| 1500 \| 71.6799 \| 0.85 \| 0.2599 \| 1.3989 \| 0.85 \| 0.8389 \| 0.1309 \| 0.0384 \| \| 69.9715 \| 61.0 \| 1525 \| 71.6753 \| 0.865 \| 0.2539 \| 1.3555 \| 0.865 \| 0.8619 \| 0.1201 \| 0.0374 \| \| 69.9715 \| 62.0 \| 1550 \| 71.6657 \| 0.855 \| 0.2562 \| 1.4005 \| 0.855 \| 0.8453 \| 0.1301 \| 0.0365 \| \| 69.9715 \| 63.0 \| 1575 \| 71.6941 \| 0.855 \| 0.2569 \| 1.4083 \| 0.855 \| 0.8453 \| 0.1297 \| 0.0369 \| \| 69.9715 \| 64.0 \| 1600 \| 71.6430 \| 0.85 \| 0.2567 \| 1.3933 \| 0.85 \| 0.8395 \| 0.1239 \| 0.0370 \| \| 69.9715 \| 65.0 \| 1625 \| 71.6666 \| 0.85 \| 0.2582 \| 1.4014 \| 0.85 \| 0.8395 \| 0.1357 \| 0.0375 \| \| 69.9715 \| 66.0 \| 1650 \| 71.6550 \| 0.85 \| 0.2578 \| 1.3849 \| 0.85 \| 0.8395 \| 0.1253 \| 0.0370 \| \| 69.9715 \| 67.0 \| 1675 \| 71.6321 \| 0.855 \| 0.2573 \| 1.3932 \| 0.855 \| 0.8466 \| 0.1276 \| 0.0362 \| \| 69.9715 \| 68.0 \| 1700 \| 71.6237 \| 0.855 \| 0.2576 \| 1.3976 \| 0.855 \| 0.8453 \| 0.1231 \| 0.0374 \| \| 69.9715 \| 69.0 \| 1725 \| 71.6287 \| 0.85 \| 0.2589 \| 1.3914 \| 0.85 \| 0.8403 \| 0.1299 \| 0.0366 \| \| 69.9715 \| 70.0 \| 1750 \| 71.6325 \| 0.85 \| 0.2580 \| 1.3907 \| 0.85 \| 0.8425 \| 0.1321 \| 0.0365 \| \| 69.9715 \| 71.0 \| 1775 \| 71.6175 \| 0.85 \| 0.2572 \| 1.3914 \| 0.85 \| 0.8412 \| 0.1318 \| 0.0365 \| \| 69.9715 \| 72.0 \| 1800 \| 71.6208 \| 0.85 \| 0.2591 \| 1.3860 \| 0.85 \| 0.8425 \| 0.1325 \| 0.0355 \| \| 69.9715 \| 73.0 \| 1825 \| 71.6157 \| 0.85 \| 0.2600 \| 1.3894 \| 0.85 \| 0.8425 \| 0.1335 \| 0.0361 \| \| 69.9715 \| 74.0 \| 1850 \| 71.6405 \| 0.85 \| 0.2632 \| 1.3335 \| 0.85 \| 0.8425 \| 0.1306 \| 0.0359 \| \| 69.9715 \| 75.0 \| 1875 \| 71.6099 \| 0.85 \| 0.2586 \| 1.3899 \| 0.85 \| 0.8425 \| 0.1283 \| 0.0360 \| \| 69.9715 \| 76.0 \| 1900 \| 71.6058 \| 0.85 \| 0.2599 \| 1.3220 \| 0.85 \| 0.8425 \| 0.1260 \| 0.0357 \| \| 69.9715 \| 77.0 \| 1925 \| 71.6096 \| 0.85 \| 0.2591 \| 1.3859 \| 0.85 \| 0.8425 \| 0.1279 \| 0.0355 \| \| 69.9715 \| 78.0 \| 1950 \| 71.6213 \| 0.85 \| 0.2604 \| 1.3875 \| 0.85 \| 0.8425 \| 0.1284 \| 0.0351 \| \| 69.9715 \| 79.0 \| 1975 \| 71.6240 \| 0.85 \| 0.2610 \| 1.3867 \| 0.85 \| 0.8425 \| 0.1347 \| 0.0356 \| \| 69.8814 \| 80.0 \| 2000 \| 71.6246 \| 0.855 \| 0.2608 \| 1.3183 \| 0.855 \| 0.8483 \| 0.1238 \| 0.0354 \| \| 69.8814 \| 81.0 \| 2025 \| 71.6133 \| 0.85 \| 0.2595 \| 1.3177 \| 0.85 \| 0.8425 \| 0.1268 \| 0.0357 \| \| 69.8814 \| 82.0 \| 2050 \| 71.6120 \| 0.85 \| 0.2593 \| 1.3873 \| 0.85 \| 0.8425 \| 0.1278 \| 0.0353 \| \| 69.8814 \| 83.0 \| 2075 \| 71.6183 \| 0.85 \| 0.2600 \| 1.3220 \| 0.85 \| 0.8425 \| 0.1342 \| 0.0356 \| \| 69.8814 \| 84.0 \| 2100 \| 71.6008 \| 0.85 \| 0.2610 \| 1.3168 \| 0.85 \| 0.8425 \| 0.1359 \| 0.0358 \| \| 69.8814 \| 85.0 \| 2125 \| 71.5986 \| 0.85 \| 0.2621 \| 1.3156 \| 0.85 \| 0.8425 \| 0.1327 \| 0.0355 \| \| 69.8814 \| 86.0 \| 2150 \| 71.6000 \| 0.85 \| 0.2609 \| 1.3167 \| 0.85 \| 0.8425 \| 0.1311 \| 0.0354 \| \| 69.8814 \| 87.0 \| 2175 \| 71.6006 \| 0.855 \| 0.2611 \| 1.3171 \| 0.855 \| 0.8483 \| 0.1251 \| 0.0353 \| \| 69.8814 \| 88.0 \| 2200 \| 71.6033 \| 0.85 \| 0.2620 \| 1.3132 \| 0.85 \| 0.8425 \| 0.1331 \| 0.0357 \| \| 69.8814 \| 89.0 \| 2225 \| 71.6176 \| 0.855 \| 0.2635 \| 1.3142 \| 0.855 \| 0.8483 \| 0.1326 \| 0.0357 \| \| 69.8814 \| 90.0 \| 2250 \| 71.6201 \| 0.855 \| 0.2636 \| 1.3126 \| 0.855 \| 0.8483 \| 0.1282 \| 0.0356 \| \| 69.8814 \| 91.0 \| 2275 \| 71.6128 \| 0.85 \| 0.2629 \| 1.3126 \| 0.85 \| 0.8425 \| 0.1273 \| 0.0356 \| \| 69.8814 \| 92.0 \| 2300 \| 71.6086 \| 0.855 \| 0.2631 \| 1.3147 \| 0.855 \| 0.8515 \| 0.1261 \| 0.0358 \| \| 69.8814 \| 93.0 \| 2325 \| 71.6010 \| 0.85 \| 0.2638 \| 1.3122 \| 0.85 \| 0.8425 \| 0.1292 \| 0.0356 \| \| 69.8814 \| 94.0 \| 2350 \| 71.6053 \| 0.85 \| 0.2636 \| 1.3125 \| 0.85 \| 0.8425 \| 0.1269 \| 0.0354 \| \| 69.8814 \| 95.0 \| 2375 \| 71.6004 \| 0.85 \| 0.2640 \| 1.3128 \| 0.85 \| 0.8425 \| 0.1346 \| 0.0356 \| \| 69.8814 \| 96.0 \| 2400 \| 71.6035 \| 0.85 \| 0.2644 \| 1.3128 \| 0.85 \| 0.8425 \| 0.1346 \| 0.0356 \| \| 69.8814 \| 97.0 \| 2425 \| 71.6027 \| 0.85 \| 0.2639 \| 1.3117 \| 0.85 \| 0.8425 \| 0.1343 \| 0.0355 \| \| 69.8814 \| 98.0 \| 2450 \| 71.6039 \| 0.85 \| 0.2639 \| 1.3117 \| 0.85 \| 0.8425 \| 0.1343 \| 0.0354 \| \| 69.8814 \| 99.0 \| 2475 \| 71.6018 \| 0.85 \| 0.2640 \| 1.3122 \| 0.85 \| 0.8425 \| 0.1342 \| 0.0356 \| \| 69.8448 \| 100.0 \| 2500 \| 71.6043 \| 0.85 \| 0.2638 \| 1.3122 \| 0.85 \| 0.8425 \| 0.1342 \| 0.0355 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
marcoi/vit-base-patch16-224-finetuned-flower	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3	[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]
jordyvl/81-tiny_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 81-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 230.0462 - Accuracy: 0.845 - Brier Loss: 0.2451 - Nll: 1.1250 - F1 Micro: 0.845 - F1 Macro: 0.8350 - Ece: 0.1115 - Aurc: 0.0383 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 238.2967 \| 0.275 \| 0.8789 \| 6.2995 \| 0.275 \| 0.1849 \| 0.2898 \| 0.4790 \| \| No log \| 2.0 \| 100 \| 236.7866 \| 0.41 \| 0.7214 \| 3.0130 \| 0.41 \| 0.2599 \| 0.2544 \| 0.3985 \| \| No log \| 3.0 \| 150 \| 235.3687 \| 0.59 \| 0.5690 \| 2.4483 \| 0.59 \| 0.4507 \| 0.2464 \| 0.2336 \| \| No log \| 4.0 \| 200 \| 234.7639 \| 0.65 \| 0.4658 \| 2.3318 \| 0.65 \| 0.4996 \| 0.2365 \| 0.1474 \| \| No log \| 5.0 \| 250 \| 234.0798 \| 0.675 \| 0.4692 \| 2.3351 \| 0.675 \| 0.5158 \| 0.2359 \| 0.1335 \| \| No log \| 6.0 \| 300 \| 234.2002 \| 0.68 \| 0.4267 \| 2.1570 \| 0.68 \| 0.5724 \| 0.1990 \| 0.1124 \| \| No log \| 7.0 \| 350 \| 234.1154 \| 0.665 \| 0.4874 \| 2.9269 \| 0.665 \| 0.5631 \| 0.2249 \| 0.1259 \| \| No log \| 8.0 \| 400 \| 233.2390 \| 0.77 \| 0.3224 \| 1.8874 \| 0.7700 \| 0.6850 \| 0.1754 \| 0.0695 \| \| No log \| 9.0 \| 450 \| 233.1396 \| 0.755 \| 0.3583 \| 2.1139 \| 0.755 \| 0.7060 \| 0.1573 \| 0.0802 \| \| 234.3515 \| 10.0 \| 500 \| 233.5895 \| 0.705 \| 0.4653 \| 3.2740 \| 0.705 \| 0.6813 \| 0.2183 \| 0.1180 \| \| 234.3515 \| 11.0 \| 550 \| 233.0481 \| 0.745 \| 0.3803 \| 2.7330 \| 0.745 \| 0.6697 \| 0.1730 \| 0.0936 \| \| 234.3515 \| 12.0 \| 600 \| 232.9339 \| 0.78 \| 0.3319 \| 1.8094 \| 0.78 \| 0.7416 \| 0.1700 \| 0.0660 \| \| 234.3515 \| 13.0 \| 650 \| 233.0181 \| 0.755 \| 0.3182 \| 1.9630 \| 0.755 \| 0.7596 \| 0.1772 \| 0.0823 \| \| 234.3515 \| 14.0 \| 700 \| 232.4934 \| 0.815 \| 0.2659 \| 1.3869 \| 0.815 \| 0.7749 \| 0.1512 \| 0.0511 \| \| 234.3515 \| 15.0 \| 750 \| 232.6922 \| 0.79 \| 0.3361 \| 1.8793 \| 0.79 \| 0.7671 \| 0.1633 \| 0.0926 \| \| 234.3515 \| 16.0 \| 800 \| 232.3391 \| 0.815 \| 0.2785 \| 1.5488 \| 0.815 \| 0.8038 \| 0.1476 \| 0.0549 \| \| 234.3515 \| 17.0 \| 850 \| 232.5412 \| 0.775 \| 0.3100 \| 2.0192 \| 0.775 \| 0.7675 \| 0.1617 \| 0.0502 \| \| 234.3515 \| 18.0 \| 900 \| 232.5307 \| 0.785 \| 0.3211 \| 1.6793 \| 0.785 \| 0.7541 \| 0.1679 \| 0.0676 \| \| 234.3515 \| 19.0 \| 950 \| 232.3055 \| 0.825 \| 0.2862 \| 1.5993 \| 0.825 \| 0.8204 \| 0.1443 \| 0.0620 \| \| 231.365 \| 20.0 \| 1000 \| 232.5636 \| 0.78 \| 0.3431 \| 2.6048 \| 0.78 \| 0.7835 \| 0.1658 \| 0.0847 \| \| 231.365 \| 21.0 \| 1050 \| 231.8748 \| 0.845 \| 0.2737 \| 1.4332 \| 0.845 \| 0.8454 \| 0.1616 \| 0.0602 \| \| 231.365 \| 22.0 \| 1100 \| 232.1032 \| 0.825 \| 0.2557 \| 1.6240 \| 0.825 \| 0.8290 \| 0.1422 \| 0.0442 \| \| 231.365 \| 23.0 \| 1150 \| 231.7278 \| 0.835 \| 0.2740 \| 1.6260 \| 0.835 \| 0.8246 \| 0.1541 \| 0.0613 \| \| 231.365 \| 24.0 \| 1200 \| 231.9350 \| 0.84 \| 0.2636 \| 1.6028 \| 0.8400 \| 0.8410 \| 0.1472 \| 0.0471 \| \| 231.365 \| 25.0 \| 1250 \| 231.6054 \| 0.82 \| 0.2753 \| 1.3835 \| 0.82 \| 0.8090 \| 0.1526 \| 0.0478 \| \| 231.365 \| 26.0 \| 1300 \| 231.6516 \| 0.84 \| 0.2737 \| 1.3849 \| 0.8400 \| 0.8354 \| 0.1492 \| 0.0543 \| \| 231.365 \| 27.0 \| 1350 \| 231.5453 \| 0.84 \| 0.2536 \| 1.1384 \| 0.8400 \| 0.8265 \| 0.1363 \| 0.0431 \| \| 231.365 \| 28.0 \| 1400 \| 231.4833 \| 0.85 \| 0.2292 \| 1.1206 \| 0.85 \| 0.8350 \| 0.1250 \| 0.0362 \| \| 231.365 \| 29.0 \| 1450 \| 231.3722 \| 0.815 \| 0.2856 \| 1.1706 \| 0.815 \| 0.8038 \| 0.1606 \| 0.0486 \| \| 230.3328 \| 30.0 \| 1500 \| 231.3517 \| 0.84 \| 0.2608 \| 1.3387 \| 0.8400 \| 0.8382 \| 0.1366 \| 0.0483 \| \| 230.3328 \| 31.0 \| 1550 \| 231.3705 \| 0.815 \| 0.2724 \| 1.2558 \| 0.815 \| 0.7992 \| 0.1448 \| 0.0463 \| \| 230.3328 \| 32.0 \| 1600 \| 231.4319 \| 0.84 \| 0.2588 \| 1.0691 \| 0.8400 \| 0.8301 \| 0.1317 \| 0.0435 \| \| 230.3328 \| 33.0 \| 1650 \| 231.2119 \| 0.86 \| 0.2323 \| 1.1693 \| 0.8600 \| 0.8609 \| 0.1229 \| 0.0470 \| \| 230.3328 \| 34.0 \| 1700 \| 231.2836 \| 0.83 \| 0.2477 \| 1.1294 \| 0.83 \| 0.8201 \| 0.1398 \| 0.0406 \| \| 230.3328 \| 35.0 \| 1750 \| 231.2669 \| 0.845 \| 0.2569 \| 1.1508 \| 0.845 \| 0.8369 \| 0.1357 \| 0.0449 \| \| 230.3328 \| 36.0 \| 1800 \| 231.0634 \| 0.85 \| 0.2422 \| 1.0830 \| 0.85 \| 0.8404 \| 0.1372 \| 0.0419 \| \| 230.3328 \| 37.0 \| 1850 \| 231.1141 \| 0.85 \| 0.2398 \| 1.0879 \| 0.85 \| 0.8455 \| 0.1347 \| 0.0437 \| \| 230.3328 \| 38.0 \| 1900 \| 231.0520 \| 0.815 \| 0.2626 \| 1.2325 \| 0.815 \| 0.8077 \| 0.1304 \| 0.0478 \| \| 230.3328 \| 39.0 \| 1950 \| 230.9089 \| 0.83 \| 0.2507 \| 1.1245 \| 0.83 \| 0.8258 \| 0.1347 \| 0.0487 \| \| 229.6409 \| 40.0 \| 2000 \| 231.0532 \| 0.86 \| 0.2207 \| 1.1258 \| 0.8600 \| 0.8464 \| 0.1206 \| 0.0428 \| \| 229.6409 \| 41.0 \| 2050 \| 230.9307 \| 0.855 \| 0.2350 \| 1.1326 \| 0.855 \| 0.8498 \| 0.1284 \| 0.0401 \| \| 229.6409 \| 42.0 \| 2100 \| 230.8493 \| 0.86 \| 0.2306 \| 1.1075 \| 0.8600 \| 0.8548 \| 0.1441 \| 0.0417 \| \| 229.6409 \| 43.0 \| 2150 \| 230.7516 \| 0.87 \| 0.2198 \| 1.0312 \| 0.87 \| 0.8587 \| 0.1184 \| 0.0404 \| \| 229.6409 \| 44.0 \| 2200 \| 230.8540 \| 0.85 \| 0.2485 \| 1.1724 \| 0.85 \| 0.8444 \| 0.1425 \| 0.0451 \| \| 229.6409 \| 45.0 \| 2250 \| 230.7995 \| 0.86 \| 0.2284 \| 1.1183 \| 0.8600 \| 0.8490 \| 0.1284 \| 0.0368 \| \| 229.6409 \| 46.0 \| 2300 \| 230.7162 \| 0.825 \| 0.2701 \| 1.1206 \| 0.825 \| 0.8159 \| 0.1390 \| 0.0437 \| \| 229.6409 \| 47.0 \| 2350 \| 230.5593 \| 0.855 \| 0.2341 \| 1.2242 \| 0.855 \| 0.8459 \| 0.1226 \| 0.0392 \| \| 229.6409 \| 48.0 \| 2400 \| 230.6472 \| 0.86 \| 0.2377 \| 1.0233 \| 0.8600 \| 0.8558 \| 0.1319 \| 0.0354 \| \| 229.6409 \| 49.0 \| 2450 \| 230.7080 \| 0.84 \| 0.2548 \| 1.1208 \| 0.8400 \| 0.8294 \| 0.1484 \| 0.0426 \| \| 229.142 \| 50.0 \| 2500 \| 230.5862 \| 0.845 \| 0.2543 \| 1.2129 \| 0.845 \| 0.8322 \| 0.1358 \| 0.0415 \| \| 229.142 \| 51.0 \| 2550 \| 230.6550 \| 0.845 \| 0.2462 \| 1.0937 \| 0.845 \| 0.8333 \| 0.1306 \| 0.0411 \| \| 229.142 \| 52.0 \| 2600 \| 230.5789 \| 0.835 \| 0.2595 \| 1.1393 \| 0.835 \| 0.8249 \| 0.1369 \| 0.0428 \| \| 229.142 \| 53.0 \| 2650 \| 230.5895 \| 0.85 \| 0.2519 \| 1.0185 \| 0.85 \| 0.8447 \| 0.1263 \| 0.0439 \| \| 229.142 \| 54.0 \| 2700 \| 230.4955 \| 0.86 \| 0.2402 \| 1.0837 \| 0.8600 \| 0.8590 \| 0.1382 \| 0.0394 \| \| 229.142 \| 55.0 \| 2750 \| 230.4579 \| 0.84 \| 0.2560 \| 1.1514 \| 0.8400 \| 0.8312 \| 0.1439 \| 0.0431 \| \| 229.142 \| 56.0 \| 2800 \| 230.5190 \| 0.845 \| 0.2527 \| 1.2868 \| 0.845 \| 0.8334 \| 0.1342 \| 0.0406 \| \| 229.142 \| 57.0 \| 2850 \| 230.4989 \| 0.84 \| 0.2536 \| 1.0785 \| 0.8400 \| 0.8278 \| 0.1377 \| 0.0397 \| \| 229.142 \| 58.0 \| 2900 \| 230.4445 \| 0.84 \| 0.2416 \| 1.2104 \| 0.8400 \| 0.8340 \| 0.1339 \| 0.0383 \| \| 229.142 \| 59.0 \| 2950 \| 230.3159 \| 0.87 \| 0.2211 \| 1.1138 \| 0.87 \| 0.8630 \| 0.1171 \| 0.0394 \| \| 228.7442 \| 60.0 \| 3000 \| 230.3405 \| 0.835 \| 0.2603 \| 1.1327 \| 0.835 \| 0.8273 \| 0.1422 \| 0.0416 \| \| 228.7442 \| 61.0 \| 3050 \| 230.3892 \| 0.845 \| 0.2395 \| 1.1619 \| 0.845 \| 0.8308 \| 0.1263 \| 0.0377 \| \| 228.7442 \| 62.0 \| 3100 \| 230.3728 \| 0.84 \| 0.2478 \| 1.0053 \| 0.8400 \| 0.8291 \| 0.1208 \| 0.0389 \| \| 228.7442 \| 63.0 \| 3150 \| 230.2486 \| 0.845 \| 0.2545 \| 1.1989 \| 0.845 \| 0.8438 \| 0.1364 \| 0.0434 \| \| 228.7442 \| 64.0 \| 3200 \| 230.2353 \| 0.84 \| 0.2512 \| 1.1260 \| 0.8400 \| 0.8330 \| 0.1191 \| 0.0430 \| \| 228.7442 \| 65.0 \| 3250 \| 230.2041 \| 0.845 \| 0.2429 \| 1.1529 \| 0.845 \| 0.8384 \| 0.1343 \| 0.0401 \| \| 228.7442 \| 66.0 \| 3300 \| 230.2439 \| 0.845 \| 0.2511 \| 1.1125 \| 0.845 \| 0.8366 \| 0.1367 \| 0.0430 \| \| 228.7442 \| 67.0 \| 3350 \| 230.1510 \| 0.85 \| 0.2471 \| 1.0528 \| 0.85 \| 0.8457 \| 0.1414 \| 0.0402 \| \| 228.7442 \| 68.0 \| 3400 \| 230.2274 \| 0.85 \| 0.2455 \| 1.1150 \| 0.85 \| 0.8397 \| 0.1311 \| 0.0427 \| \| 228.7442 \| 69.0 \| 3450 \| 230.2165 \| 0.845 \| 0.2524 \| 1.0517 \| 0.845 \| 0.8421 \| 0.1312 \| 0.0410 \| \| 228.4757 \| 70.0 \| 3500 \| 230.1976 \| 0.835 \| 0.2600 \| 1.0845 \| 0.835 \| 0.8258 \| 0.1353 \| 0.0410 \| \| 228.4757 \| 71.0 \| 3550 \| 230.1062 \| 0.85 \| 0.2487 \| 1.1447 \| 0.85 \| 0.8410 \| 0.1297 \| 0.0427 \| \| 228.4757 \| 72.0 \| 3600 \| 229.9867 \| 0.835 \| 0.2584 \| 1.0641 \| 0.835 \| 0.8273 \| 0.1236 \| 0.0440 \| \| 228.4757 \| 73.0 \| 3650 \| 230.1918 \| 0.845 \| 0.2411 \| 1.1521 \| 0.845 \| 0.8363 \| 0.1373 \| 0.0389 \| \| 228.4757 \| 74.0 \| 3700 \| 230.0781 \| 0.85 \| 0.2524 \| 1.0980 \| 0.85 \| 0.8390 \| 0.1298 \| 0.0409 \| \| 228.4757 \| 75.0 \| 3750 \| 230.1432 \| 0.835 \| 0.2554 \| 1.0967 \| 0.835 \| 0.8230 \| 0.1227 \| 0.0407 \| \| 228.4757 \| 76.0 \| 3800 \| 230.1512 \| 0.84 \| 0.2535 \| 1.0945 \| 0.8400 \| 0.8295 \| 0.1321 \| 0.0422 \| \| 228.4757 \| 77.0 \| 3850 \| 230.0682 \| 0.84 \| 0.2502 \| 1.0301 \| 0.8400 \| 0.8370 \| 0.1312 \| 0.0403 \| \| 228.4757 \| 78.0 \| 3900 \| 230.0357 \| 0.835 \| 0.2521 \| 1.1572 \| 0.835 \| 0.8293 \| 0.1244 \| 0.0412 \| \| 228.4757 \| 79.0 \| 3950 \| 230.1252 \| 0.845 \| 0.2509 \| 1.0961 \| 0.845 \| 0.8381 \| 0.1273 \| 0.0409 \| \| 228.2815 \| 80.0 \| 4000 \| 230.0584 \| 0.845 \| 0.2539 \| 1.0795 \| 0.845 \| 0.8363 \| 0.1235 \| 0.0432 \| \| 228.2815 \| 81.0 \| 4050 \| 229.9967 \| 0.85 \| 0.2427 \| 1.1156 \| 0.85 \| 0.8382 \| 0.1184 \| 0.0394 \| \| 228.2815 \| 82.0 \| 4100 \| 230.0755 \| 0.84 \| 0.2563 \| 1.0833 \| 0.8400 \| 0.8302 \| 0.1295 \| 0.0406 \| \| 228.2815 \| 83.0 \| 4150 \| 230.0798 \| 0.845 \| 0.2477 \| 1.1713 \| 0.845 \| 0.8385 \| 0.1259 \| 0.0427 \| \| 228.2815 \| 84.0 \| 4200 \| 230.0299 \| 0.84 \| 0.2477 \| 1.0907 \| 0.8400 \| 0.8260 \| 0.1213 \| 0.0383 \| \| 228.2815 \| 85.0 \| 4250 \| 230.0568 \| 0.845 \| 0.2483 \| 1.0763 \| 0.845 \| 0.8350 \| 0.1238 \| 0.0409 \| \| 228.2815 \| 86.0 \| 4300 \| 230.0743 \| 0.85 \| 0.2464 \| 1.0549 \| 0.85 \| 0.8418 \| 0.1271 \| 0.0398 \| \| 228.2815 \| 87.0 \| 4350 \| 230.0061 \| 0.845 \| 0.2505 \| 1.1585 \| 0.845 \| 0.8350 \| 0.1312 \| 0.0375 \| \| 228.2815 \| 88.0 \| 4400 \| 229.9674 \| 0.845 \| 0.2478 \| 1.0763 \| 0.845 \| 0.8346 \| 0.1394 \| 0.0410 \| \| 228.2815 \| 89.0 \| 4450 \| 229.9697 \| 0.85 \| 0.2451 \| 1.0833 \| 0.85 \| 0.8406 \| 0.1324 \| 0.0364 \| \| 228.1298 \| 90.0 \| 4500 \| 230.0305 \| 0.845 \| 0.2496 \| 1.1008 \| 0.845 \| 0.8350 \| 0.1308 \| 0.0395 \| \| 228.1298 \| 91.0 \| 4550 \| 229.9740 \| 0.845 \| 0.2495 \| 1.0605 \| 0.845 \| 0.8350 \| 0.1309 \| 0.0413 \| \| 228.1298 \| 92.0 \| 4600 \| 229.9962 \| 0.85 \| 0.2497 \| 1.1193 \| 0.85 \| 0.8408 \| 0.1294 \| 0.0399 \| \| 228.1298 \| 93.0 \| 4650 \| 229.9740 \| 0.85 \| 0.2491 \| 1.0496 \| 0.85 \| 0.8383 \| 0.1270 \| 0.0390 \| \| 228.1298 \| 94.0 \| 4700 \| 229.9698 \| 0.84 \| 0.2516 \| 1.0644 \| 0.8400 \| 0.8295 \| 0.1430 \| 0.0398 \| \| 228.1298 \| 95.0 \| 4750 \| 229.9247 \| 0.845 \| 0.2516 \| 1.1705 \| 0.845 \| 0.8350 \| 0.1227 \| 0.0371 \| \| 228.1298 \| 96.0 \| 4800 \| 230.0451 \| 0.84 \| 0.2507 \| 1.0970 \| 0.8400 \| 0.8285 \| 0.1368 \| 0.0391 \| \| 228.1298 \| 97.0 \| 4850 \| 229.9402 \| 0.85 \| 0.2495 \| 1.1427 \| 0.85 \| 0.8430 \| 0.1379 \| 0.0413 \| \| 228.1298 \| 98.0 \| 4900 \| 230.0130 \| 0.84 \| 0.2532 \| 1.0964 \| 0.8400 \| 0.8285 \| 0.1412 \| 0.0389 \| \| 228.1298 \| 99.0 \| 4950 \| 230.0200 \| 0.845 \| 0.2482 \| 1.0916 \| 0.845 \| 0.8317 \| 0.1190 \| 0.0392 \| \| 228.0583 \| 100.0 \| 5000 \| 230.0462 \| 0.845 \| 0.2451 \| 1.1250 \| 0.845 \| 0.8350 \| 0.1115 \| 0.0383 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/60-tiny_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 60-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 230.9055 - Accuracy: 0.855 - Brier Loss: 0.2199 - Nll: 1.0336 - F1 Micro: 0.855 - F1 Macro: 0.8458 - Ece: 0.1335 - Aurc: 0.0336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 238.9631 \| 0.285 \| 0.8828 \| 6.5847 \| 0.285 \| 0.1874 \| 0.2964 \| 0.5019 \| \| No log \| 2.0 \| 100 \| 237.5668 \| 0.385 \| 0.7452 \| 3.0464 \| 0.3850 \| 0.2403 \| 0.2776 \| 0.4072 \| \| No log \| 3.0 \| 150 \| 236.0767 \| 0.615 \| 0.5834 \| 2.4900 \| 0.615 \| 0.4713 \| 0.2629 \| 0.2189 \| \| No log \| 4.0 \| 200 \| 235.7085 \| 0.635 \| 0.5104 \| 2.9259 \| 0.635 \| 0.4933 \| 0.2605 \| 0.1700 \| \| No log \| 5.0 \| 250 \| 234.8680 \| 0.66 \| 0.4494 \| 2.2358 \| 0.66 \| 0.4974 \| 0.2021 \| 0.1292 \| \| No log \| 6.0 \| 300 \| 235.0645 \| 0.68 \| 0.4615 \| 2.3164 \| 0.68 \| 0.5568 \| 0.2601 \| 0.1346 \| \| No log \| 7.0 \| 350 \| 234.3820 \| 0.735 \| 0.3741 \| 1.8624 \| 0.735 \| 0.6245 \| 0.2265 \| 0.0894 \| \| No log \| 8.0 \| 400 \| 233.9906 \| 0.72 \| 0.3954 \| 1.7623 \| 0.72 \| 0.6479 \| 0.1779 \| 0.0980 \| \| No log \| 9.0 \| 450 \| 234.0194 \| 0.755 \| 0.3410 \| 1.6963 \| 0.755 \| 0.6965 \| 0.1730 \| 0.0718 \| \| 234.3621 \| 10.0 \| 500 \| 234.2365 \| 0.705 \| 0.3859 \| 2.4174 \| 0.705 \| 0.6957 \| 0.1759 \| 0.0944 \| \| 234.3621 \| 11.0 \| 550 \| 233.5580 \| 0.76 \| 0.3331 \| 1.5730 \| 0.76 \| 0.7135 \| 0.1817 \| 0.0709 \| \| 234.3621 \| 12.0 \| 600 \| 233.3485 \| 0.815 \| 0.2725 \| 1.6179 \| 0.815 \| 0.7769 \| 0.1835 \| 0.0497 \| \| 234.3621 \| 13.0 \| 650 \| 233.6395 \| 0.805 \| 0.3038 \| 1.7563 \| 0.805 \| 0.7813 \| 0.1976 \| 0.0698 \| \| 234.3621 \| 14.0 \| 700 \| 233.1443 \| 0.805 \| 0.2928 \| 1.4722 \| 0.805 \| 0.7779 \| 0.1513 \| 0.0546 \| \| 234.3621 \| 15.0 \| 750 \| 233.1237 \| 0.83 \| 0.2569 \| 1.7889 \| 0.83 \| 0.8231 \| 0.1361 \| 0.0460 \| \| 234.3621 \| 16.0 \| 800 \| 232.9007 \| 0.825 \| 0.2675 \| 1.7492 \| 0.825 \| 0.8125 \| 0.1742 \| 0.0558 \| \| 234.3621 \| 17.0 \| 850 \| 233.0284 \| 0.79 \| 0.2861 \| 1.4243 \| 0.79 \| 0.7698 \| 0.1418 \| 0.0520 \| \| 234.3621 \| 18.0 \| 900 \| 232.9831 \| 0.79 \| 0.3009 \| 1.4085 \| 0.79 \| 0.7440 \| 0.1962 \| 0.0643 \| \| 234.3621 \| 19.0 \| 950 \| 232.9837 \| 0.825 \| 0.2618 \| 1.4839 \| 0.825 \| 0.7949 \| 0.1444 \| 0.0457 \| \| 231.5126 \| 20.0 \| 1000 \| 232.9143 \| 0.825 \| 0.2599 \| 1.5299 \| 0.825 \| 0.8086 \| 0.1570 \| 0.0463 \| \| 231.5126 \| 21.0 \| 1050 \| 232.5251 \| 0.835 \| 0.2495 \| 1.2311 \| 0.835 \| 0.8279 \| 0.1511 \| 0.0472 \| \| 231.5126 \| 22.0 \| 1100 \| 232.6748 \| 0.855 \| 0.2165 \| 1.2547 \| 0.855 \| 0.8448 \| 0.1450 \| 0.0299 \| \| 231.5126 \| 23.0 \| 1150 \| 232.6610 \| 0.83 \| 0.2450 \| 1.2944 \| 0.83 \| 0.8113 \| 0.1545 \| 0.0403 \| \| 231.5126 \| 24.0 \| 1200 \| 232.7660 \| 0.83 \| 0.2480 \| 1.4783 \| 0.83 \| 0.8101 \| 0.1645 \| 0.0397 \| \| 231.5126 \| 25.0 \| 1250 \| 232.5843 \| 0.855 \| 0.2336 \| 1.1227 \| 0.855 \| 0.8303 \| 0.1672 \| 0.0397 \| \| 231.5126 \| 26.0 \| 1300 \| 232.3482 \| 0.84 \| 0.2321 \| 1.1720 \| 0.8400 \| 0.8346 \| 0.1527 \| 0.0380 \| \| 231.5126 \| 27.0 \| 1350 \| 232.3758 \| 0.84 \| 0.2353 \| 1.1684 \| 0.8400 \| 0.8327 \| 0.1520 \| 0.0372 \| \| 231.5126 \| 28.0 \| 1400 \| 232.3022 \| 0.82 \| 0.2460 \| 1.0888 \| 0.82 \| 0.7920 \| 0.1746 \| 0.0392 \| \| 231.5126 \| 29.0 \| 1450 \| 232.1077 \| 0.845 \| 0.2355 \| 1.3720 \| 0.845 \| 0.8306 \| 0.1314 \| 0.0372 \| \| 230.5624 \| 30.0 \| 1500 \| 232.3631 \| 0.825 \| 0.2443 \| 1.3202 \| 0.825 \| 0.7971 \| 0.1404 \| 0.0373 \| \| 230.5624 \| 31.0 \| 1550 \| 232.1099 \| 0.84 \| 0.2509 \| 1.2935 \| 0.8400 \| 0.8199 \| 0.1552 \| 0.0453 \| \| 230.5624 \| 32.0 \| 1600 \| 232.1548 \| 0.855 \| 0.2275 \| 1.0948 \| 0.855 \| 0.8360 \| 0.1428 \| 0.0347 \| \| 230.5624 \| 33.0 \| 1650 \| 232.0168 \| 0.84 \| 0.2284 \| 1.1303 \| 0.8400 \| 0.8260 \| 0.1460 \| 0.0374 \| \| 230.5624 \| 34.0 \| 1700 \| 232.1122 \| 0.875 \| 0.2154 \| 1.3044 \| 0.875 \| 0.8612 \| 0.1410 \| 0.0307 \| \| 230.5624 \| 35.0 \| 1750 \| 232.0452 \| 0.84 \| 0.2382 \| 1.1732 \| 0.8400 \| 0.8199 \| 0.1218 \| 0.0376 \| \| 230.5624 \| 36.0 \| 1800 \| 231.8549 \| 0.835 \| 0.2390 \| 1.1322 \| 0.835 \| 0.8227 \| 0.1478 \| 0.0375 \| \| 230.5624 \| 37.0 \| 1850 \| 231.8938 \| 0.845 \| 0.2558 \| 1.0115 \| 0.845 \| 0.8261 \| 0.1730 \| 0.0447 \| \| 230.5624 \| 38.0 \| 1900 \| 231.8979 \| 0.85 \| 0.2292 \| 1.1142 \| 0.85 \| 0.8361 \| 0.1359 \| 0.0348 \| \| 230.5624 \| 39.0 \| 1950 \| 231.7455 \| 0.84 \| 0.2217 \| 1.0746 \| 0.8400 \| 0.8185 \| 0.1294 \| 0.0309 \| \| 229.9089 \| 40.0 \| 2000 \| 231.8694 \| 0.85 \| 0.2200 \| 1.0529 \| 0.85 \| 0.8334 \| 0.1435 \| 0.0343 \| \| 229.9089 \| 41.0 \| 2050 \| 231.8044 \| 0.84 \| 0.2317 \| 0.9332 \| 0.8400 \| 0.8204 \| 0.1432 \| 0.0337 \| \| 229.9089 \| 42.0 \| 2100 \| 231.6414 \| 0.855 \| 0.2165 \| 1.1356 \| 0.855 \| 0.8393 \| 0.1357 \| 0.0321 \| \| 229.9089 \| 43.0 \| 2150 \| 231.5806 \| 0.835 \| 0.2378 \| 1.0314 \| 0.835 \| 0.8124 \| 0.1443 \| 0.0382 \| \| 229.9089 \| 44.0 \| 2200 \| 231.6199 \| 0.855 \| 0.2287 \| 1.0907 \| 0.855 \| 0.8463 \| 0.1196 \| 0.0362 \| \| 229.9089 \| 45.0 \| 2250 \| 231.5991 \| 0.85 \| 0.2208 \| 1.0967 \| 0.85 \| 0.8350 \| 0.1321 \| 0.0339 \| \| 229.9089 \| 46.0 \| 2300 \| 231.5103 \| 0.85 \| 0.2249 \| 1.0330 \| 0.85 \| 0.8270 \| 0.1239 \| 0.0332 \| \| 229.9089 \| 47.0 \| 2350 \| 231.4252 \| 0.87 \| 0.2126 \| 1.1054 \| 0.87 \| 0.8618 \| 0.1230 \| 0.0312 \| \| 229.9089 \| 48.0 \| 2400 \| 231.4696 \| 0.86 \| 0.2136 \| 1.0952 \| 0.8600 \| 0.8503 \| 0.1304 \| 0.0302 \| \| 229.9089 \| 49.0 \| 2450 \| 231.5416 \| 0.84 \| 0.2329 \| 1.0155 \| 0.8400 \| 0.8256 \| 0.1381 \| 0.0356 \| \| 229.4364 \| 50.0 \| 2500 \| 231.4932 \| 0.84 \| 0.2215 \| 1.1382 \| 0.8400 \| 0.8177 \| 0.1557 \| 0.0319 \| \| 229.4364 \| 51.0 \| 2550 \| 231.4270 \| 0.84 \| 0.2312 \| 1.0191 \| 0.8400 \| 0.8253 \| 0.1376 \| 0.0371 \| \| 229.4364 \| 52.0 \| 2600 \| 231.3520 \| 0.85 \| 0.2233 \| 1.2815 \| 0.85 \| 0.8289 \| 0.1444 \| 0.0334 \| \| 229.4364 \| 53.0 \| 2650 \| 231.3922 \| 0.86 \| 0.2223 \| 1.0950 \| 0.8600 \| 0.8423 \| 0.1309 \| 0.0334 \| \| 229.4364 \| 54.0 \| 2700 \| 231.3504 \| 0.855 \| 0.2171 \| 1.0165 \| 0.855 \| 0.8422 \| 0.1304 \| 0.0313 \| \| 229.4364 \| 55.0 \| 2750 \| 231.2676 \| 0.87 \| 0.2129 \| 1.0704 \| 0.87 \| 0.8598 \| 0.1334 \| 0.0320 \| \| 229.4364 \| 56.0 \| 2800 \| 231.2823 \| 0.84 \| 0.2390 \| 1.0982 \| 0.8400 \| 0.8226 \| 0.1187 \| 0.0372 \| \| 229.4364 \| 57.0 \| 2850 \| 231.2740 \| 0.85 \| 0.2251 \| 0.9521 \| 0.85 \| 0.8271 \| 0.1388 \| 0.0320 \| \| 229.4364 \| 58.0 \| 2900 \| 231.2784 \| 0.85 \| 0.2284 \| 1.0194 \| 0.85 \| 0.8320 \| 0.1306 \| 0.0360 \| \| 229.4364 \| 59.0 \| 2950 \| 231.2078 \| 0.84 \| 0.2265 \| 1.0036 \| 0.8400 \| 0.8253 \| 0.1359 \| 0.0366 \| \| 229.043 \| 60.0 \| 3000 \| 231.2086 \| 0.85 \| 0.2380 \| 1.0247 \| 0.85 \| 0.8391 \| 0.1395 \| 0.0374 \| \| 229.043 \| 61.0 \| 3050 \| 231.2673 \| 0.845 \| 0.2410 \| 1.0205 \| 0.845 \| 0.8272 \| 0.1466 \| 0.0389 \| \| 229.043 \| 62.0 \| 3100 \| 231.1900 \| 0.855 \| 0.2219 \| 1.0835 \| 0.855 \| 0.8449 \| 0.1351 \| 0.0346 \| \| 229.043 \| 63.0 \| 3150 \| 231.0561 \| 0.845 \| 0.2417 \| 0.9740 \| 0.845 \| 0.8332 \| 0.1503 \| 0.0405 \| \| 229.043 \| 64.0 \| 3200 \| 231.1282 \| 0.845 \| 0.2387 \| 1.1105 \| 0.845 \| 0.8270 \| 0.1198 \| 0.0379 \| \| 229.043 \| 65.0 \| 3250 \| 231.0782 \| 0.85 \| 0.2334 \| 0.9838 \| 0.85 \| 0.8403 \| 0.1282 \| 0.0356 \| \| 229.043 \| 66.0 \| 3300 \| 231.0704 \| 0.84 \| 0.2442 \| 1.0380 \| 0.8400 \| 0.8275 \| 0.1543 \| 0.0410 \| \| 229.043 \| 67.0 \| 3350 \| 231.0450 \| 0.85 \| 0.2246 \| 1.0023 \| 0.85 \| 0.8394 \| 0.1212 \| 0.0353 \| \| 229.043 \| 68.0 \| 3400 \| 231.1017 \| 0.85 \| 0.2257 \| 1.0437 \| 0.85 \| 0.8334 \| 0.1232 \| 0.0350 \| \| 229.043 \| 69.0 \| 3450 \| 231.0068 \| 0.85 \| 0.2321 \| 1.0075 \| 0.85 \| 0.8445 \| 0.1271 \| 0.0361 \| \| 228.7748 \| 70.0 \| 3500 \| 231.0666 \| 0.85 \| 0.2274 \| 1.0133 \| 0.85 \| 0.8382 \| 0.1334 \| 0.0361 \| \| 228.7748 \| 71.0 \| 3550 \| 230.9450 \| 0.85 \| 0.2417 \| 1.0738 \| 0.85 \| 0.8356 \| 0.1294 \| 0.0380 \| \| 228.7748 \| 72.0 \| 3600 \| 230.7952 \| 0.85 \| 0.2379 \| 0.9779 \| 0.85 \| 0.8391 \| 0.1309 \| 0.0393 \| \| 228.7748 \| 73.0 \| 3650 \| 231.0920 \| 0.86 \| 0.2188 \| 1.0154 \| 0.8600 \| 0.8538 \| 0.1230 \| 0.0335 \| \| 228.7748 \| 74.0 \| 3700 \| 230.9152 \| 0.855 \| 0.2408 \| 1.1637 \| 0.855 \| 0.8486 \| 0.1490 \| 0.0400 \| \| 228.7748 \| 75.0 \| 3750 \| 230.9537 \| 0.85 \| 0.2195 \| 1.0135 \| 0.85 \| 0.8301 \| 0.1131 \| 0.0321 \| \| 228.7748 \| 76.0 \| 3800 \| 230.9977 \| 0.855 \| 0.2208 \| 1.0136 \| 0.855 \| 0.8484 \| 0.1296 \| 0.0334 \| \| 228.7748 \| 77.0 \| 3850 \| 230.9619 \| 0.855 \| 0.2348 \| 1.0158 \| 0.855 \| 0.8526 \| 0.1346 \| 0.0371 \| \| 228.7748 \| 78.0 \| 3900 \| 230.9416 \| 0.84 \| 0.2315 \| 1.0372 \| 0.8400 \| 0.8219 \| 0.1290 \| 0.0353 \| \| 228.7748 \| 79.0 \| 3950 \| 231.0093 \| 0.85 \| 0.2196 \| 1.0981 \| 0.85 \| 0.8318 \| 0.1380 \| 0.0335 \| \| 228.5779 \| 80.0 \| 4000 \| 230.9455 \| 0.845 \| 0.2290 \| 1.0193 \| 0.845 \| 0.8332 \| 0.1459 \| 0.0350 \| \| 228.5779 \| 81.0 \| 4050 \| 230.8672 \| 0.845 \| 0.2184 \| 1.0164 \| 0.845 \| 0.8309 \| 0.1560 \| 0.0322 \| \| 228.5779 \| 82.0 \| 4100 \| 230.9410 \| 0.855 \| 0.2282 \| 1.0116 \| 0.855 \| 0.8486 \| 0.1309 \| 0.0349 \| \| 228.5779 \| 83.0 \| 4150 \| 230.9393 \| 0.855 \| 0.2258 \| 1.0168 \| 0.855 \| 0.8498 \| 0.1227 \| 0.0355 \| \| 228.5779 \| 84.0 \| 4200 \| 230.8770 \| 0.845 \| 0.2204 \| 1.0105 \| 0.845 \| 0.8303 \| 0.1259 \| 0.0315 \| \| 228.5779 \| 85.0 \| 4250 \| 230.9236 \| 0.845 \| 0.2271 \| 1.0079 \| 0.845 \| 0.8332 \| 0.1274 \| 0.0353 \| \| 228.5779 \| 86.0 \| 4300 \| 230.9332 \| 0.845 \| 0.2237 \| 1.0410 \| 0.845 \| 0.8288 \| 0.1480 \| 0.0340 \| \| 228.5779 \| 87.0 \| 4350 \| 230.8607 \| 0.845 \| 0.2273 \| 1.0343 \| 0.845 \| 0.8313 \| 0.1070 \| 0.0338 \| \| 228.5779 \| 88.0 \| 4400 \| 230.7832 \| 0.85 \| 0.2434 \| 1.1399 \| 0.85 \| 0.8432 \| 0.1483 \| 0.0407 \| \| 228.5779 \| 89.0 \| 4450 \| 230.8136 \| 0.85 \| 0.2263 \| 1.0002 \| 0.85 \| 0.8367 \| 0.1283 \| 0.0343 \| \| 228.4285 \| 90.0 \| 4500 \| 230.9154 \| 0.86 \| 0.2243 \| 1.0189 \| 0.8600 \| 0.8552 \| 0.1319 \| 0.0332 \| \| 228.4285 \| 91.0 \| 4550 \| 230.8125 \| 0.86 \| 0.2320 \| 0.9955 \| 0.8600 \| 0.8552 \| 0.1437 \| 0.0361 \| \| 228.4285 \| 92.0 \| 4600 \| 230.8634 \| 0.855 \| 0.2238 \| 1.0341 \| 0.855 \| 0.8477 \| 0.1252 \| 0.0329 \| \| 228.4285 \| 93.0 \| 4650 \| 230.8527 \| 0.84 \| 0.2326 \| 0.9819 \| 0.8400 \| 0.8242 \| 0.1248 \| 0.0362 \| \| 228.4285 \| 94.0 \| 4700 \| 230.8176 \| 0.845 \| 0.2347 \| 1.0080 \| 0.845 \| 0.8332 \| 0.1356 \| 0.0369 \| \| 228.4285 \| 95.0 \| 4750 \| 230.7853 \| 0.85 \| 0.2286 \| 1.0163 \| 0.85 \| 0.8445 \| 0.1176 \| 0.0356 \| \| 228.4285 \| 96.0 \| 4800 \| 230.9345 \| 0.855 \| 0.2218 \| 1.0151 \| 0.855 \| 0.8458 \| 0.1252 \| 0.0328 \| \| 228.4285 \| 97.0 \| 4850 \| 230.8020 \| 0.855 \| 0.2300 \| 1.0166 \| 0.855 \| 0.8462 \| 0.1424 \| 0.0349 \| \| 228.4285 \| 98.0 \| 4900 \| 230.8873 \| 0.855 \| 0.2240 \| 1.0244 \| 0.855 \| 0.8477 \| 0.1309 \| 0.0343 \| \| 228.4285 \| 99.0 \| 4950 \| 230.8796 \| 0.855 \| 0.2276 \| 1.0084 \| 0.855 \| 0.8498 \| 0.1254 \| 0.0344 \| \| 228.3542 \| 100.0 \| 5000 \| 230.9055 \| 0.855 \| 0.2199 \| 1.0336 \| 0.855 \| 0.8458 \| 0.1335 \| 0.0336 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/39-tiny_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 39-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 239.0946 - Accuracy: 0.835 - Brier Loss: 0.3084 - Nll: 1.2679 - F1 Micro: 0.835 - F1 Macro: 0.8047 - Ece: 0.2467 - Aurc: 0.0448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 246.9268 \| 0.22 \| 0.8877 \| 7.2845 \| 0.22 \| 0.1442 \| 0.2508 \| 0.5734 \| \| No log \| 2.0 \| 100 \| 245.5493 \| 0.355 \| 0.7851 \| 4.2469 \| 0.3550 \| 0.2408 \| 0.2761 \| 0.4302 \| \| No log \| 3.0 \| 150 \| 244.0258 \| 0.58 \| 0.6569 \| 2.7197 \| 0.58 \| 0.4698 \| 0.3335 \| 0.2529 \| \| No log \| 4.0 \| 200 \| 243.8010 \| 0.615 \| 0.5800 \| 3.1495 \| 0.615 \| 0.4701 \| 0.2865 \| 0.1927 \| \| No log \| 5.0 \| 250 \| 242.9670 \| 0.64 \| 0.5193 \| 2.2510 \| 0.64 \| 0.4843 \| 0.2774 \| 0.1631 \| \| No log \| 6.0 \| 300 \| 243.1274 \| 0.62 \| 0.5595 \| 2.8430 \| 0.62 \| 0.4881 \| 0.2980 \| 0.1558 \| \| No log \| 7.0 \| 350 \| 242.1450 \| 0.675 \| 0.4620 \| 2.2479 \| 0.675 \| 0.5389 \| 0.2322 \| 0.1266 \| \| No log \| 8.0 \| 400 \| 242.5806 \| 0.63 \| 0.5222 \| 2.6597 \| 0.63 \| 0.5539 \| 0.2649 \| 0.1584 \| \| No log \| 9.0 \| 450 \| 241.9880 \| 0.755 \| 0.4313 \| 1.8660 \| 0.755 \| 0.6923 \| 0.2780 \| 0.1028 \| \| 241.4671 \| 10.0 \| 500 \| 241.9459 \| 0.73 \| 0.4274 \| 1.9762 \| 0.7300 \| 0.6550 \| 0.2539 \| 0.0843 \| \| 241.4671 \| 11.0 \| 550 \| 241.4543 \| 0.74 \| 0.4410 \| 1.7736 \| 0.74 \| 0.6867 \| 0.2589 \| 0.1197 \| \| 241.4671 \| 12.0 \| 600 \| 241.5311 \| 0.79 \| 0.3750 \| 1.6579 \| 0.79 \| 0.7193 \| 0.2754 \| 0.0582 \| \| 241.4671 \| 13.0 \| 650 \| 241.6211 \| 0.77 \| 0.4231 \| 1.4441 \| 0.7700 \| 0.7350 \| 0.3134 \| 0.0761 \| \| 241.4671 \| 14.0 \| 700 \| 241.2984 \| 0.795 \| 0.3898 \| 1.5615 \| 0.795 \| 0.7650 \| 0.2879 \| 0.0721 \| \| 241.4671 \| 15.0 \| 750 \| 241.2752 \| 0.81 \| 0.3641 \| 1.5167 \| 0.81 \| 0.7692 \| 0.2956 \| 0.0515 \| \| 241.4671 \| 16.0 \| 800 \| 240.9899 \| 0.78 \| 0.3821 \| 1.7296 \| 0.78 \| 0.7434 \| 0.2935 \| 0.0688 \| \| 241.4671 \| 17.0 \| 850 \| 241.3208 \| 0.81 \| 0.3850 \| 1.6487 \| 0.81 \| 0.7628 \| 0.2946 \| 0.0657 \| \| 241.4671 \| 18.0 \| 900 \| 241.0431 \| 0.82 \| 0.3678 \| 1.5667 \| 0.82 \| 0.7701 \| 0.3109 \| 0.0504 \| \| 241.4671 \| 19.0 \| 950 \| 240.9436 \| 0.815 \| 0.3615 \| 1.1826 \| 0.815 \| 0.7624 \| 0.3019 \| 0.0557 \| \| 238.6853 \| 20.0 \| 1000 \| 240.7993 \| 0.83 \| 0.3475 \| 1.2078 \| 0.83 \| 0.7887 \| 0.3062 \| 0.0541 \| \| 238.6853 \| 21.0 \| 1050 \| 240.6124 \| 0.825 \| 0.3577 \| 1.1927 \| 0.825 \| 0.7956 \| 0.2883 \| 0.0560 \| \| 238.6853 \| 22.0 \| 1100 \| 240.7105 \| 0.815 \| 0.3535 \| 1.4564 \| 0.815 \| 0.7550 \| 0.2814 \| 0.0553 \| \| 238.6853 \| 23.0 \| 1150 \| 240.6886 \| 0.815 \| 0.3584 \| 1.1556 \| 0.815 \| 0.7815 \| 0.2993 \| 0.0564 \| \| 238.6853 \| 24.0 \| 1200 \| 240.5978 \| 0.84 \| 0.3451 \| 1.1351 \| 0.8400 \| 0.8002 \| 0.2937 \| 0.0485 \| \| 238.6853 \| 25.0 \| 1250 \| 240.3825 \| 0.815 \| 0.3453 \| 1.2408 \| 0.815 \| 0.7918 \| 0.2853 \| 0.0525 \| \| 238.6853 \| 26.0 \| 1300 \| 240.2633 \| 0.83 \| 0.3431 \| 1.3299 \| 0.83 \| 0.7883 \| 0.2715 \| 0.0562 \| \| 238.6853 \| 27.0 \| 1350 \| 240.5535 \| 0.82 \| 0.3475 \| 1.4406 \| 0.82 \| 0.7874 \| 0.2897 \| 0.0536 \| \| 238.6853 \| 28.0 \| 1400 \| 240.3554 \| 0.835 \| 0.3447 \| 1.2483 \| 0.835 \| 0.7947 \| 0.3078 \| 0.0464 \| \| 238.6853 \| 29.0 \| 1450 \| 240.2271 \| 0.82 \| 0.3274 \| 1.5224 \| 0.82 \| 0.7862 \| 0.2691 \| 0.0467 \| \| 237.7411 \| 30.0 \| 1500 \| 240.2261 \| 0.825 \| 0.3388 \| 1.2921 \| 0.825 \| 0.7939 \| 0.2913 \| 0.0485 \| \| 237.7411 \| 31.0 \| 1550 \| 240.4772 \| 0.83 \| 0.3474 \| 1.3063 \| 0.83 \| 0.7823 \| 0.2995 \| 0.0516 \| \| 237.7411 \| 32.0 \| 1600 \| 240.2594 \| 0.85 \| 0.3509 \| 1.3993 \| 0.85 \| 0.8184 \| 0.3142 \| 0.0496 \| \| 237.7411 \| 33.0 \| 1650 \| 239.9501 \| 0.805 \| 0.3413 \| 1.2018 \| 0.805 \| 0.7630 \| 0.2619 \| 0.0592 \| \| 237.7411 \| 34.0 \| 1700 \| 240.2149 \| 0.86 \| 0.3409 \| 1.2633 \| 0.8600 \| 0.8490 \| 0.3229 \| 0.0467 \| \| 237.7411 \| 35.0 \| 1750 \| 240.0215 \| 0.82 \| 0.3264 \| 1.1435 \| 0.82 \| 0.7705 \| 0.2538 \| 0.0499 \| \| 237.7411 \| 36.0 \| 1800 \| 239.9490 \| 0.85 \| 0.3204 \| 1.4364 \| 0.85 \| 0.8147 \| 0.2752 \| 0.0432 \| \| 237.7411 \| 37.0 \| 1850 \| 239.9360 \| 0.825 \| 0.3262 \| 1.1487 \| 0.825 \| 0.7921 \| 0.2706 \| 0.0482 \| \| 237.7411 \| 38.0 \| 1900 \| 240.0647 \| 0.855 \| 0.3322 \| 1.1655 \| 0.855 \| 0.8339 \| 0.3053 \| 0.0460 \| \| 237.7411 \| 39.0 \| 1950 \| 239.7761 \| 0.825 \| 0.3237 \| 1.2252 \| 0.825 \| 0.7862 \| 0.2732 \| 0.0499 \| \| 237.1023 \| 40.0 \| 2000 \| 239.9426 \| 0.83 \| 0.3220 \| 1.2096 \| 0.83 \| 0.7906 \| 0.2748 \| 0.0457 \| \| 237.1023 \| 41.0 \| 2050 \| 239.8765 \| 0.835 \| 0.3241 \| 1.3547 \| 0.835 \| 0.7929 \| 0.2661 \| 0.0462 \| \| 237.1023 \| 42.0 \| 2100 \| 239.8423 \| 0.835 \| 0.3329 \| 1.2423 \| 0.835 \| 0.8000 \| 0.2955 \| 0.0527 \| \| 237.1023 \| 43.0 \| 2150 \| 239.6674 \| 0.83 \| 0.3215 \| 1.1217 \| 0.83 \| 0.8069 \| 0.2597 \| 0.0486 \| \| 237.1023 \| 44.0 \| 2200 \| 239.7237 \| 0.81 \| 0.3206 \| 1.1322 \| 0.81 \| 0.7724 \| 0.2500 \| 0.0473 \| \| 237.1023 \| 45.0 \| 2250 \| 239.7981 \| 0.82 \| 0.3263 \| 1.4167 \| 0.82 \| 0.7754 \| 0.2786 \| 0.0485 \| \| 237.1023 \| 46.0 \| 2300 \| 239.4825 \| 0.83 \| 0.3149 \| 1.2610 \| 0.83 \| 0.7879 \| 0.2436 \| 0.0487 \| \| 237.1023 \| 47.0 \| 2350 \| 239.6016 \| 0.83 \| 0.3195 \| 1.2602 \| 0.83 \| 0.7903 \| 0.2631 \| 0.0529 \| \| 237.1023 \| 48.0 \| 2400 \| 239.6693 \| 0.84 \| 0.3248 \| 1.2935 \| 0.8400 \| 0.7992 \| 0.2581 \| 0.0505 \| \| 237.1023 \| 49.0 \| 2450 \| 239.6932 \| 0.845 \| 0.3235 \| 1.1698 \| 0.845 \| 0.8024 \| 0.2600 \| 0.0459 \| \| 236.6301 \| 50.0 \| 2500 \| 239.5297 \| 0.845 \| 0.3151 \| 1.4464 \| 0.845 \| 0.8057 \| 0.2775 \| 0.0467 \| \| 236.6301 \| 51.0 \| 2550 \| 239.6262 \| 0.83 \| 0.3236 \| 1.2198 \| 0.83 \| 0.7879 \| 0.2680 \| 0.0466 \| \| 236.6301 \| 52.0 \| 2600 \| 239.4326 \| 0.855 \| 0.3111 \| 1.2320 \| 0.855 \| 0.8338 \| 0.2785 \| 0.0452 \| \| 236.6301 \| 53.0 \| 2650 \| 239.5918 \| 0.825 \| 0.3171 \| 1.2295 \| 0.825 \| 0.7966 \| 0.2517 \| 0.0440 \| \| 236.6301 \| 54.0 \| 2700 \| 239.5557 \| 0.845 \| 0.3215 \| 1.1543 \| 0.845 \| 0.8027 \| 0.2723 \| 0.0453 \| \| 236.6301 \| 55.0 \| 2750 \| 239.4767 \| 0.83 \| 0.3113 \| 1.0283 \| 0.83 \| 0.8107 \| 0.2457 \| 0.0446 \| \| 236.6301 \| 56.0 \| 2800 \| 239.3879 \| 0.85 \| 0.3112 \| 1.2353 \| 0.85 \| 0.8146 \| 0.2547 \| 0.0449 \| \| 236.6301 \| 57.0 \| 2850 \| 239.3733 \| 0.85 \| 0.3104 \| 1.3730 \| 0.85 \| 0.8236 \| 0.2593 \| 0.0451 \| \| 236.6301 \| 58.0 \| 2900 \| 239.4988 \| 0.835 \| 0.3174 \| 1.3295 \| 0.835 \| 0.7984 \| 0.2572 \| 0.0468 \| \| 236.6301 \| 59.0 \| 2950 \| 239.3514 \| 0.815 \| 0.3103 \| 1.2168 \| 0.815 \| 0.7746 \| 0.2382 \| 0.0453 \| \| 236.2404 \| 60.0 \| 3000 \| 239.3031 \| 0.835 \| 0.3083 \| 1.2398 \| 0.835 \| 0.7944 \| 0.2577 \| 0.0479 \| \| 236.2404 \| 61.0 \| 3050 \| 239.3242 \| 0.855 \| 0.3095 \| 1.2921 \| 0.855 \| 0.8307 \| 0.2739 \| 0.0438 \| \| 236.2404 \| 62.0 \| 3100 \| 239.3217 \| 0.815 \| 0.3135 \| 1.2385 \| 0.815 \| 0.7799 \| 0.2559 \| 0.0481 \| \| 236.2404 \| 63.0 \| 3150 \| 239.2695 \| 0.835 \| 0.3102 \| 1.0724 \| 0.835 \| 0.8068 \| 0.2276 \| 0.0471 \| \| 236.2404 \| 64.0 \| 3200 \| 239.3596 \| 0.83 \| 0.3124 \| 1.1534 \| 0.83 \| 0.7911 \| 0.2440 \| 0.0442 \| \| 236.2404 \| 65.0 \| 3250 \| 239.2498 \| 0.825 \| 0.3097 \| 1.2191 \| 0.825 \| 0.7804 \| 0.2450 \| 0.0459 \| \| 236.2404 \| 66.0 \| 3300 \| 239.1448 \| 0.82 \| 0.3020 \| 1.0196 \| 0.82 \| 0.7825 \| 0.2559 \| 0.0452 \| \| 236.2404 \| 67.0 \| 3350 \| 239.1730 \| 0.825 \| 0.3017 \| 1.1825 \| 0.825 \| 0.8065 \| 0.2494 \| 0.0444 \| \| 236.2404 \| 68.0 \| 3400 \| 239.3466 \| 0.835 \| 0.3159 \| 1.2392 \| 0.835 \| 0.7973 \| 0.2617 \| 0.0448 \| \| 236.2404 \| 69.0 \| 3450 \| 239.1867 \| 0.825 \| 0.3058 \| 1.1611 \| 0.825 \| 0.7940 \| 0.2596 \| 0.0450 \| \| 235.9626 \| 70.0 \| 3500 \| 239.2792 \| 0.84 \| 0.3106 \| 1.1812 \| 0.8400 \| 0.8134 \| 0.2590 \| 0.0444 \| \| 235.9626 \| 71.0 \| 3550 \| 239.0603 \| 0.82 \| 0.3055 \| 1.1710 \| 0.82 \| 0.7895 \| 0.2251 \| 0.0466 \| \| 235.9626 \| 72.0 \| 3600 \| 238.9947 \| 0.815 \| 0.3069 \| 1.1766 \| 0.815 \| 0.7905 \| 0.2315 \| 0.0485 \| \| 235.9626 \| 73.0 \| 3650 \| 239.2322 \| 0.83 \| 0.3108 \| 1.1724 \| 0.83 \| 0.8015 \| 0.2595 \| 0.0450 \| \| 235.9626 \| 74.0 \| 3700 \| 239.0134 \| 0.825 \| 0.3087 \| 1.2661 \| 0.825 \| 0.7854 \| 0.2352 \| 0.0467 \| \| 235.9626 \| 75.0 \| 3750 \| 239.1055 \| 0.825 \| 0.3090 \| 1.1748 \| 0.825 \| 0.7946 \| 0.2483 \| 0.0458 \| \| 235.9626 \| 76.0 \| 3800 \| 239.0925 \| 0.825 \| 0.3133 \| 1.1843 \| 0.825 \| 0.7918 \| 0.2466 \| 0.0490 \| \| 235.9626 \| 77.0 \| 3850 \| 239.1586 \| 0.835 \| 0.3115 \| 1.1877 \| 0.835 \| 0.8140 \| 0.2498 \| 0.0455 \| \| 235.9626 \| 78.0 \| 3900 \| 239.1394 \| 0.83 \| 0.3103 \| 1.2698 \| 0.83 \| 0.7897 \| 0.2424 \| 0.0467 \| \| 235.9626 \| 79.0 \| 3950 \| 239.2314 \| 0.83 \| 0.3121 \| 1.2519 \| 0.83 \| 0.7938 \| 0.2378 \| 0.0453 \| \| 235.7667 \| 80.0 \| 4000 \| 239.1433 \| 0.83 \| 0.3076 \| 1.1725 \| 0.83 \| 0.7924 \| 0.2412 \| 0.0459 \| \| 235.7667 \| 81.0 \| 4050 \| 239.0533 \| 0.83 \| 0.3026 \| 1.2551 \| 0.83 \| 0.7994 \| 0.2661 \| 0.0448 \| \| 235.7667 \| 82.0 \| 4100 \| 239.0847 \| 0.825 \| 0.3123 \| 1.1798 \| 0.825 \| 0.7964 \| 0.2611 \| 0.0483 \| \| 235.7667 \| 83.0 \| 4150 \| 239.1199 \| 0.835 \| 0.3089 \| 1.2642 \| 0.835 \| 0.8047 \| 0.2551 \| 0.0471 \| \| 235.7667 \| 84.0 \| 4200 \| 239.0148 \| 0.835 \| 0.3055 \| 1.1264 \| 0.835 \| 0.7975 \| 0.2341 \| 0.0439 \| \| 235.7667 \| 85.0 \| 4250 \| 239.0657 \| 0.825 \| 0.3107 \| 1.1645 \| 0.825 \| 0.7924 \| 0.2363 \| 0.0452 \| \| 235.7667 \| 86.0 \| 4300 \| 239.1023 \| 0.83 \| 0.3134 \| 1.2785 \| 0.83 \| 0.7949 \| 0.2353 \| 0.0462 \| \| 235.7667 \| 87.0 \| 4350 \| 239.0670 \| 0.83 \| 0.3028 \| 1.2849 \| 0.83 \| 0.7927 \| 0.2295 \| 0.0428 \| \| 235.7667 \| 88.0 \| 4400 \| 239.0111 \| 0.82 \| 0.3097 \| 1.1759 \| 0.82 \| 0.7865 \| 0.2447 \| 0.0462 \| \| 235.7667 \| 89.0 \| 4450 \| 238.9677 \| 0.845 \| 0.2995 \| 1.1189 \| 0.845 \| 0.8231 \| 0.2402 \| 0.0423 \| \| 235.6149 \| 90.0 \| 4500 \| 239.1255 \| 0.83 \| 0.3063 \| 1.1282 \| 0.83 \| 0.8028 \| 0.2695 \| 0.0431 \| \| 235.6149 \| 91.0 \| 4550 \| 238.9880 \| 0.815 \| 0.3102 \| 1.1766 \| 0.815 \| 0.7851 \| 0.2388 \| 0.0486 \| \| 235.6149 \| 92.0 \| 4600 \| 239.0212 \| 0.84 \| 0.3079 \| 1.1941 \| 0.8400 \| 0.8121 \| 0.2627 \| 0.0442 \| \| 235.6149 \| 93.0 \| 4650 \| 238.9948 \| 0.82 \| 0.3077 \| 1.1702 \| 0.82 \| 0.7874 \| 0.2533 \| 0.0468 \| \| 235.6149 \| 94.0 \| 4700 \| 239.0357 \| 0.82 \| 0.3103 \| 1.1823 \| 0.82 \| 0.7878 \| 0.2639 \| 0.0471 \| \| 235.6149 \| 95.0 \| 4750 \| 238.9936 \| 0.81 \| 0.2995 \| 1.1251 \| 0.81 \| 0.7738 \| 0.2519 \| 0.0447 \| \| 235.6149 \| 96.0 \| 4800 \| 239.1319 \| 0.835 \| 0.3138 \| 1.1957 \| 0.835 \| 0.8045 \| 0.2806 \| 0.0455 \| \| 235.6149 \| 97.0 \| 4850 \| 239.0160 \| 0.815 \| 0.3061 \| 1.1852 \| 0.815 \| 0.7756 \| 0.2281 \| 0.0453 \| \| 235.6149 \| 98.0 \| 4900 \| 239.1168 \| 0.83 \| 0.3152 \| 1.2637 \| 0.83 \| 0.7997 \| 0.2509 \| 0.0474 \| \| 235.6149 \| 99.0 \| 4950 \| 239.0691 \| 0.83 \| 0.3085 \| 1.1799 \| 0.83 \| 0.7998 \| 0.2524 \| 0.0439 \| \| 235.5384 \| 100.0 \| 5000 \| 239.0946 \| 0.835 \| 0.3084 \| 1.2679 \| 0.835 \| 0.8047 \| 0.2467 \| 0.0448 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/18-tiny_tobacco3482_og_simkd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 18-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 269.3729 - Accuracy: 0.36 - Brier Loss: 0.8320 - Nll: 4.3971 - F1 Micro: 0.36 - F1 Macro: 0.2725 - Ece: 0.3539 - Aurc: 0.6748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 278.3639 \| 0.075 \| 0.8950 \| 9.1030 \| 0.075 \| 0.0452 \| 0.1624 \| 0.8470 \| \| No log \| 2.0 \| 100 \| 276.7339 \| 0.095 \| 0.8890 \| 8.6785 \| 0.095 \| 0.0820 \| 0.1806 \| 0.9095 \| \| No log \| 3.0 \| 150 \| 275.0460 \| 0.16 \| 0.8755 \| 6.5857 \| 0.16 \| 0.0866 \| 0.2296 \| 0.8951 \| \| No log \| 4.0 \| 200 \| 274.4719 \| 0.33 \| 0.8662 \| 6.6801 \| 0.33 \| 0.1980 \| 0.3166 \| 0.5931 \| \| No log \| 5.0 \| 250 \| 273.5627 \| 0.23 \| 0.8660 \| 6.4504 \| 0.23 \| 0.1193 \| 0.2606 \| 0.7477 \| \| No log \| 6.0 \| 300 \| 273.5152 \| 0.22 \| 0.8622 \| 6.1576 \| 0.22 \| 0.1323 \| 0.2578 \| 0.6395 \| \| No log \| 7.0 \| 350 \| 273.0874 \| 0.2 \| 0.8584 \| 5.8986 \| 0.2000 \| 0.1249 \| 0.2508 \| 0.8129 \| \| No log \| 8.0 \| 400 \| 273.7433 \| 0.19 \| 0.8764 \| 6.0516 \| 0.19 \| 0.1554 \| 0.2550 \| 0.8545 \| \| No log \| 9.0 \| 450 \| 272.4757 \| 0.185 \| 0.8533 \| 5.9072 \| 0.185 \| 0.1340 \| 0.2529 \| 0.7360 \| \| 273.9531 \| 10.0 \| 500 \| 272.7899 \| 0.14 \| 0.8669 \| 6.3132 \| 0.14 \| 0.1080 \| 0.2373 \| 0.8882 \| \| 273.9531 \| 11.0 \| 550 \| 271.7198 \| 0.245 \| 0.8484 \| 4.7561 \| 0.245 \| 0.1370 \| 0.2741 \| 0.7203 \| \| 273.9531 \| 12.0 \| 600 \| 272.7391 \| 0.095 \| 0.8761 \| 6.3636 \| 0.095 \| 0.0930 \| 0.2120 \| 0.9142 \| \| 273.9531 \| 13.0 \| 650 \| 271.8800 \| 0.24 \| 0.8566 \| 4.9415 \| 0.24 \| 0.1926 \| 0.2825 \| 0.7850 \| \| 273.9531 \| 14.0 \| 700 \| 271.4349 \| 0.305 \| 0.8408 \| 4.6971 \| 0.305 \| 0.1935 \| 0.3087 \| 0.7274 \| \| 273.9531 \| 15.0 \| 750 \| 271.7440 \| 0.24 \| 0.8476 \| 3.9346 \| 0.24 \| 0.1835 \| 0.2902 \| 0.8098 \| \| 273.9531 \| 16.0 \| 800 \| 271.1944 \| 0.28 \| 0.8464 \| 4.1470 \| 0.28 \| 0.2074 \| 0.3074 \| 0.7919 \| \| 273.9531 \| 17.0 \| 850 \| 271.2062 \| 0.34 \| 0.8363 \| 4.8972 \| 0.34 \| 0.2136 \| 0.3237 \| 0.6403 \| \| 273.9531 \| 18.0 \| 900 \| 271.6591 \| 0.295 \| 0.8548 \| 4.7423 \| 0.295 \| 0.1990 \| 0.3066 \| 0.7590 \| \| 273.9531 \| 19.0 \| 950 \| 271.5129 \| 0.255 \| 0.8571 \| 4.8864 \| 0.255 \| 0.1898 \| 0.2901 \| 0.7929 \| \| 270.6992 \| 20.0 \| 1000 \| 271.4151 \| 0.265 \| 0.8543 \| 5.2793 \| 0.265 \| 0.1924 \| 0.2928 \| 0.7842 \| \| 270.6992 \| 21.0 \| 1050 \| 271.0936 \| 0.35 \| 0.8449 \| 4.4171 \| 0.35 \| 0.2347 \| 0.3389 \| 0.7230 \| \| 270.6992 \| 22.0 \| 1100 \| 271.2365 \| 0.285 \| 0.8504 \| 4.7910 \| 0.285 \| 0.2020 \| 0.3147 \| 0.7840 \| \| 270.6992 \| 23.0 \| 1150 \| 271.3472 \| 0.26 \| 0.8496 \| 4.1785 \| 0.26 \| 0.1800 \| 0.2922 \| 0.7983 \| \| 270.6992 \| 24.0 \| 1200 \| 271.1550 \| 0.335 \| 0.8466 \| 4.8743 \| 0.335 \| 0.2518 \| 0.3381 \| 0.7148 \| \| 270.6992 \| 25.0 \| 1250 \| 270.7837 \| 0.34 \| 0.8455 \| 4.7405 \| 0.34 \| 0.2392 \| 0.3420 \| 0.7592 \| \| 270.6992 \| 26.0 \| 1300 \| 271.2034 \| 0.325 \| 0.8485 \| 5.0231 \| 0.325 \| 0.2418 \| 0.3302 \| 0.7411 \| \| 270.6992 \| 27.0 \| 1350 \| 270.5752 \| 0.385 \| 0.8334 \| 4.5239 \| 0.3850 \| 0.2866 \| 0.3535 \| 0.6197 \| \| 270.6992 \| 28.0 \| 1400 \| 270.6892 \| 0.35 \| 0.8407 \| 4.5651 \| 0.35 \| 0.2593 \| 0.3428 \| 0.6812 \| \| 270.6992 \| 29.0 \| 1450 \| 270.7246 \| 0.36 \| 0.8411 \| 4.6054 \| 0.36 \| 0.2699 \| 0.3365 \| 0.6772 \| \| 269.5806 \| 30.0 \| 1500 \| 270.6962 \| 0.32 \| 0.8453 \| 4.3997 \| 0.32 \| 0.2170 \| 0.3145 \| 0.7129 \| \| 269.5806 \| 31.0 \| 1550 \| 270.8489 \| 0.325 \| 0.8478 \| 4.8399 \| 0.325 \| 0.2367 \| 0.3180 \| 0.6999 \| \| 269.5806 \| 32.0 \| 1600 \| 270.5093 \| 0.36 \| 0.8370 \| 4.6472 \| 0.36 \| 0.2765 \| 0.3456 \| 0.6472 \| \| 269.5806 \| 33.0 \| 1650 \| 270.5440 \| 0.35 \| 0.8407 \| 4.6948 \| 0.35 \| 0.2532 \| 0.3292 \| 0.6436 \| \| 269.5806 \| 34.0 \| 1700 \| 270.6743 \| 0.395 \| 0.8387 \| 4.1379 \| 0.395 \| 0.2872 \| 0.3730 \| 0.6805 \| \| 269.5806 \| 35.0 \| 1750 \| 270.6282 \| 0.33 \| 0.8415 \| 4.2499 \| 0.33 \| 0.2445 \| 0.3410 \| 0.7220 \| \| 269.5806 \| 36.0 \| 1800 \| 270.5709 \| 0.31 \| 0.8478 \| 4.8275 \| 0.31 \| 0.2295 \| 0.3063 \| 0.7223 \| \| 269.5806 \| 37.0 \| 1850 \| 270.4933 \| 0.33 \| 0.8449 \| 4.7300 \| 0.33 \| 0.2415 \| 0.3157 \| 0.6997 \| \| 269.5806 \| 38.0 \| 1900 \| 270.4860 \| 0.355 \| 0.8360 \| 4.4343 \| 0.3550 \| 0.2598 \| 0.3373 \| 0.6545 \| \| 269.5806 \| 39.0 \| 1950 \| 269.9668 \| 0.37 \| 0.8306 \| 4.4080 \| 0.37 \| 0.2602 \| 0.3349 \| 0.6056 \| \| 268.8099 \| 40.0 \| 2000 \| 270.3401 \| 0.385 \| 0.8389 \| 5.0506 \| 0.3850 \| 0.2777 \| 0.3567 \| 0.6686 \| \| 268.8099 \| 41.0 \| 2050 \| 270.0377 \| 0.365 \| 0.8320 \| 4.6238 \| 0.3650 \| 0.2466 \| 0.3429 \| 0.6167 \| \| 268.8099 \| 42.0 \| 2100 \| 270.2510 \| 0.355 \| 0.8363 \| 4.7053 \| 0.3550 \| 0.2498 \| 0.3352 \| 0.6564 \| \| 268.8099 \| 43.0 \| 2150 \| 270.0252 \| 0.38 \| 0.8275 \| 4.2261 \| 0.38 \| 0.2690 \| 0.3557 \| 0.5995 \| \| 268.8099 \| 44.0 \| 2200 \| 269.9756 \| 0.36 \| 0.8251 \| 4.2336 \| 0.36 \| 0.2516 \| 0.3226 \| 0.6005 \| \| 268.8099 \| 45.0 \| 2250 \| 270.1610 \| 0.34 \| 0.8383 \| 4.5052 \| 0.34 \| 0.2593 \| 0.3164 \| 0.6854 \| \| 268.8099 \| 46.0 \| 2300 \| 270.0320 \| 0.33 \| 0.8396 \| 4.4659 \| 0.33 \| 0.2458 \| 0.3114 \| 0.7070 \| \| 268.8099 \| 47.0 \| 2350 \| 269.9535 \| 0.335 \| 0.8316 \| 4.6707 \| 0.335 \| 0.2419 \| 0.3203 \| 0.6503 \| \| 268.8099 \| 48.0 \| 2400 \| 270.0645 \| 0.365 \| 0.8320 \| 4.4182 \| 0.3650 \| 0.2517 \| 0.3230 \| 0.6360 \| \| 268.8099 \| 49.0 \| 2450 \| 270.0541 \| 0.375 \| 0.8348 \| 4.4803 \| 0.375 \| 0.2717 \| 0.3504 \| 0.6444 \| \| 268.2443 \| 50.0 \| 2500 \| 269.8891 \| 0.33 \| 0.8374 \| 4.7223 \| 0.33 \| 0.2365 \| 0.3223 \| 0.6716 \| \| 268.2443 \| 51.0 \| 2550 \| 269.8773 \| 0.39 \| 0.8355 \| 4.4693 \| 0.39 \| 0.2637 \| 0.3547 \| 0.6525 \| \| 268.2443 \| 52.0 \| 2600 \| 269.6983 \| 0.39 \| 0.8298 \| 4.5670 \| 0.39 \| 0.2716 \| 0.3624 \| 0.6230 \| \| 268.2443 \| 53.0 \| 2650 \| 270.0016 \| 0.35 \| 0.8327 \| 4.3052 \| 0.35 \| 0.2402 \| 0.3367 \| 0.6602 \| \| 268.2443 \| 54.0 \| 2700 \| 269.7764 \| 0.35 \| 0.8318 \| 4.3783 \| 0.35 \| 0.2432 \| 0.3189 \| 0.6373 \| \| 268.2443 \| 55.0 \| 2750 \| 269.7582 \| 0.36 \| 0.8272 \| 4.4652 \| 0.36 \| 0.2535 \| 0.3130 \| 0.6197 \| \| 268.2443 \| 56.0 \| 2800 \| 269.7654 \| 0.385 \| 0.8253 \| 4.4241 \| 0.3850 \| 0.2751 \| 0.3375 \| 0.6019 \| \| 268.2443 \| 57.0 \| 2850 \| 269.7094 \| 0.375 \| 0.8331 \| 4.5019 \| 0.375 \| 0.2691 \| 0.3413 \| 0.6391 \| \| 268.2443 \| 58.0 \| 2900 \| 269.7442 \| 0.37 \| 0.8323 \| 4.5422 \| 0.37 \| 0.2813 \| 0.3311 \| 0.6468 \| \| 268.2443 \| 59.0 \| 2950 \| 269.7930 \| 0.385 \| 0.8265 \| 4.4502 \| 0.3850 \| 0.2820 \| 0.3331 \| 0.6032 \| \| 267.7843 \| 60.0 \| 3000 \| 269.6804 \| 0.375 \| 0.8270 \| 4.3152 \| 0.375 \| 0.2723 \| 0.3221 \| 0.6005 \| \| 267.7843 \| 61.0 \| 3050 \| 269.7132 \| 0.36 \| 0.8355 \| 4.4928 \| 0.36 \| 0.2625 \| 0.3464 \| 0.6679 \| \| 267.7843 \| 62.0 \| 3100 \| 269.6766 \| 0.375 \| 0.8308 \| 4.5392 \| 0.375 \| 0.2644 \| 0.3272 \| 0.6307 \| \| 267.7843 \| 63.0 \| 3150 \| 269.5538 \| 0.32 \| 0.8345 \| 4.4571 \| 0.32 \| 0.2299 \| 0.3160 \| 0.6791 \| \| 267.7843 \| 64.0 \| 3200 \| 269.5080 \| 0.375 \| 0.8262 \| 4.1608 \| 0.375 \| 0.2581 \| 0.3323 \| 0.6357 \| \| 267.7843 \| 65.0 \| 3250 \| 269.6766 \| 0.355 \| 0.8362 \| 4.4220 \| 0.3550 \| 0.2647 \| 0.3250 \| 0.6591 \| \| 267.7843 \| 66.0 \| 3300 \| 269.4889 \| 0.38 \| 0.8249 \| 4.3636 \| 0.38 \| 0.2786 \| 0.3452 \| 0.6364 \| \| 267.7843 \| 67.0 \| 3350 \| 269.4171 \| 0.4 \| 0.8246 \| 4.2888 \| 0.4000 \| 0.2877 \| 0.3479 \| 0.6114 \| \| 267.7843 \| 68.0 \| 3400 \| 269.6962 \| 0.37 \| 0.8286 \| 4.3171 \| 0.37 \| 0.2614 \| 0.3376 \| 0.6369 \| \| 267.7843 \| 69.0 \| 3450 \| 269.3859 \| 0.425 \| 0.8225 \| 4.2805 \| 0.425 \| 0.3053 \| 0.3649 \| 0.5751 \| \| 267.4508 \| 70.0 \| 3500 \| 269.5482 \| 0.345 \| 0.8377 \| 4.7547 \| 0.345 \| 0.2559 \| 0.3341 \| 0.6728 \| \| 267.4508 \| 71.0 \| 3550 \| 269.3435 \| 0.4 \| 0.8216 \| 4.3030 \| 0.4000 \| 0.2858 \| 0.3391 \| 0.6045 \| \| 267.4508 \| 72.0 \| 3600 \| 269.2284 \| 0.405 \| 0.8198 \| 4.3223 \| 0.405 \| 0.3006 \| 0.3598 \| 0.5984 \| \| 267.4508 \| 73.0 \| 3650 \| 269.5614 \| 0.37 \| 0.8300 \| 4.3579 \| 0.37 \| 0.2645 \| 0.3515 \| 0.6597 \| \| 267.4508 \| 74.0 \| 3700 \| 269.2826 \| 0.39 \| 0.8217 \| 4.3413 \| 0.39 \| 0.2738 \| 0.3317 \| 0.5869 \| \| 267.4508 \| 75.0 \| 3750 \| 269.3375 \| 0.39 \| 0.8226 \| 4.2796 \| 0.39 \| 0.2712 \| 0.3485 \| 0.6140 \| \| 267.4508 \| 76.0 \| 3800 \| 269.2377 \| 0.39 \| 0.8198 \| 4.4825 \| 0.39 \| 0.2771 \| 0.3450 \| 0.5936 \| \| 267.4508 \| 77.0 \| 3850 \| 269.4238 \| 0.375 \| 0.8278 \| 4.5048 \| 0.375 \| 0.2736 \| 0.3349 \| 0.6386 \| \| 267.4508 \| 78.0 \| 3900 \| 269.4522 \| 0.4 \| 0.8283 \| 4.5498 \| 0.4000 \| 0.2870 \| 0.3318 \| 0.6187 \| \| 267.4508 \| 79.0 \| 3950 \| 269.4372 \| 0.355 \| 0.8316 \| 4.5034 \| 0.3550 \| 0.2621 \| 0.3182 \| 0.6581 \| \| 267.2267 \| 80.0 \| 4000 \| 269.4058 \| 0.38 \| 0.8262 \| 4.4096 \| 0.38 \| 0.2834 \| 0.3453 \| 0.6197 \| \| 267.2267 \| 81.0 \| 4050 \| 269.2977 \| 0.37 \| 0.8251 \| 4.4666 \| 0.37 \| 0.2669 \| 0.3374 \| 0.6406 \| \| 267.2267 \| 82.0 \| 4100 \| 269.3194 \| 0.37 \| 0.8286 \| 4.4947 \| 0.37 \| 0.2751 \| 0.3332 \| 0.6326 \| \| 267.2267 \| 83.0 \| 4150 \| 269.2936 \| 0.365 \| 0.8301 \| 4.3184 \| 0.3650 \| 0.2725 \| 0.3358 \| 0.6815 \| \| 267.2267 \| 84.0 \| 4200 \| 269.2045 \| 0.4 \| 0.8242 \| 4.2851 \| 0.4000 \| 0.2947 \| 0.3526 \| 0.6133 \| \| 267.2267 \| 85.0 \| 4250 \| 269.2916 \| 0.38 \| 0.8256 \| 4.4965 \| 0.38 \| 0.2872 \| 0.3190 \| 0.6079 \| \| 267.2267 \| 86.0 \| 4300 \| 269.3817 \| 0.35 \| 0.8327 \| 4.3865 \| 0.35 \| 0.2686 \| 0.3341 \| 0.6660 \| \| 267.2267 \| 87.0 \| 4350 \| 269.2809 \| 0.36 \| 0.8296 \| 4.4521 \| 0.36 \| 0.2664 \| 0.3367 \| 0.6620 \| \| 267.2267 \| 88.0 \| 4400 \| 269.1650 \| 0.405 \| 0.8181 \| 4.2997 \| 0.405 \| 0.2857 \| 0.3527 \| 0.5624 \| \| 267.2267 \| 89.0 \| 4450 \| 269.1071 \| 0.375 \| 0.8297 \| 4.3559 \| 0.375 \| 0.2737 \| 0.3386 \| 0.6423 \| \| 267.0481 \| 90.0 \| 4500 \| 269.3120 \| 0.385 \| 0.8268 \| 4.4102 \| 0.3850 \| 0.2833 \| 0.3438 \| 0.6264 \| \| 267.0481 \| 91.0 \| 4550 \| 269.1590 \| 0.4 \| 0.8243 \| 4.2495 \| 0.4000 \| 0.2964 \| 0.3382 \| 0.5986 \| \| 267.0481 \| 92.0 \| 4600 \| 269.1963 \| 0.4 \| 0.8230 \| 4.4469 \| 0.4000 \| 0.3070 \| 0.3521 \| 0.5969 \| \| 267.0481 \| 93.0 \| 4650 \| 269.1320 \| 0.39 \| 0.8256 \| 4.2528 \| 0.39 \| 0.2856 \| 0.3355 \| 0.6072 \| \| 267.0481 \| 94.0 \| 4700 \| 269.2391 \| 0.4 \| 0.8222 \| 4.3351 \| 0.4000 \| 0.2891 \| 0.3392 \| 0.6019 \| \| 267.0481 \| 95.0 \| 4750 \| 269.2017 \| 0.395 \| 0.8243 \| 4.3717 \| 0.395 \| 0.2931 \| 0.3435 \| 0.6271 \| \| 267.0481 \| 96.0 \| 4800 \| 269.4085 \| 0.37 \| 0.8290 \| 4.3941 \| 0.37 \| 0.2817 \| 0.3255 \| 0.6242 \| \| 267.0481 \| 97.0 \| 4850 \| 269.2195 \| 0.4 \| 0.8231 \| 4.4454 \| 0.4000 \| 0.2889 \| 0.3452 \| 0.5916 \| \| 267.0481 \| 98.0 \| 4900 \| 269.3515 \| 0.375 \| 0.8296 \| 4.3291 \| 0.375 \| 0.2754 \| 0.3370 \| 0.6380 \| \| 267.0481 \| 99.0 \| 4950 \| 269.3496 \| 0.395 \| 0.8244 \| 4.4743 \| 0.395 \| 0.2989 \| 0.3432 \| 0.6100 \| \| 266.9625 \| 100.0 \| 5000 \| 269.3729 \| 0.36 \| 0.8320 \| 4.3971 \| 0.36 \| 0.2725 \| 0.3539 \| 0.6748 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/300-tiny_tobacco3482_kd_CEKD_t2.5_a0.5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 300-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4770 - Accuracy: 0.82 - Brier Loss: 0.2875 - Nll: 1.3922 - F1 Micro: 0.82 - F1 Macro: 0.8020 - Ece: 0.2219 - Aurc: 0.0517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 13 \| 1.7938 \| 0.235 \| 0.8938 \| 7.8599 \| 0.235 \| 0.1394 \| 0.3127 \| 0.7433 \| \| No log \| 2.0 \| 26 \| 1.2738 \| 0.455 \| 0.6913 \| 3.6965 \| 0.455 \| 0.3679 \| 0.2904 \| 0.3352 \| \| No log \| 3.0 \| 39 \| 1.0682 \| 0.555 \| 0.5748 \| 2.0566 \| 0.555 \| 0.4968 \| 0.2475 \| 0.2296 \| \| No log \| 4.0 \| 52 \| 0.8509 \| 0.655 \| 0.4621 \| 1.7782 \| 0.655 \| 0.6085 \| 0.2242 \| 0.1405 \| \| No log \| 5.0 \| 65 \| 0.7670 \| 0.71 \| 0.4142 \| 1.4993 \| 0.7100 \| 0.6560 \| 0.2271 \| 0.1082 \| \| No log \| 6.0 \| 78 \| 0.7285 \| 0.735 \| 0.3857 \| 1.5730 \| 0.735 \| 0.6874 \| 0.2098 \| 0.0996 \| \| No log \| 7.0 \| 91 \| 0.7052 \| 0.72 \| 0.3804 \| 1.4916 \| 0.72 \| 0.6974 \| 0.2249 \| 0.0959 \| \| No log \| 8.0 \| 104 \| 0.7590 \| 0.71 \| 0.3925 \| 1.8047 \| 0.7100 \| 0.6641 \| 0.1956 \| 0.1008 \| \| No log \| 9.0 \| 117 \| 0.7657 \| 0.71 \| 0.4006 \| 1.8296 \| 0.7100 \| 0.7169 \| 0.2330 \| 0.1025 \| \| No log \| 10.0 \| 130 \| 0.6512 \| 0.755 \| 0.3514 \| 1.5899 \| 0.755 \| 0.7256 \| 0.1863 \| 0.0853 \| \| No log \| 11.0 \| 143 \| 0.6615 \| 0.775 \| 0.3638 \| 1.8180 \| 0.775 \| 0.7564 \| 0.2106 \| 0.0911 \| \| No log \| 12.0 \| 156 \| 0.6195 \| 0.785 \| 0.3398 \| 1.6998 \| 0.785 \| 0.7419 \| 0.2337 \| 0.0643 \| \| No log \| 13.0 \| 169 \| 0.6065 \| 0.78 \| 0.3471 \| 1.5917 \| 0.78 \| 0.7550 \| 0.2280 \| 0.0793 \| \| No log \| 14.0 \| 182 \| 0.6314 \| 0.75 \| 0.3486 \| 1.9235 \| 0.75 \| 0.7315 \| 0.2105 \| 0.0755 \| \| No log \| 15.0 \| 195 \| 0.6426 \| 0.745 \| 0.3686 \| 1.8633 \| 0.745 \| 0.7100 \| 0.2099 \| 0.0891 \| \| No log \| 16.0 \| 208 \| 0.5849 \| 0.765 \| 0.3476 \| 1.3466 \| 0.765 \| 0.7505 \| 0.1978 \| 0.0827 \| \| No log \| 17.0 \| 221 \| 0.5604 \| 0.79 \| 0.3311 \| 1.3948 \| 0.79 \| 0.7581 \| 0.2258 \| 0.0705 \| \| No log \| 18.0 \| 234 \| 0.5504 \| 0.78 \| 0.3230 \| 1.4757 \| 0.78 \| 0.7712 \| 0.2104 \| 0.0624 \| \| No log \| 19.0 \| 247 \| 0.5586 \| 0.785 \| 0.3247 \| 1.5297 \| 0.785 \| 0.7642 \| 0.2159 \| 0.0655 \| \| No log \| 20.0 \| 260 \| 0.5879 \| 0.78 \| 0.3366 \| 1.5348 \| 0.78 \| 0.7727 \| 0.2162 \| 0.0716 \| \| No log \| 21.0 \| 273 \| 0.5558 \| 0.805 \| 0.3113 \| 1.5720 \| 0.805 \| 0.7945 \| 0.2161 \| 0.0652 \| \| No log \| 22.0 \| 286 \| 0.5439 \| 0.795 \| 0.3258 \| 1.7373 \| 0.795 \| 0.7883 \| 0.2307 \| 0.0745 \| \| No log \| 23.0 \| 299 \| 0.5155 \| 0.795 \| 0.3094 \| 1.4183 \| 0.795 \| 0.7725 \| 0.2221 \| 0.0625 \| \| No log \| 24.0 \| 312 \| 0.5039 \| 0.81 \| 0.2994 \| 1.4458 \| 0.81 \| 0.7830 \| 0.2114 \| 0.0624 \| \| No log \| 25.0 \| 325 \| 0.5142 \| 0.81 \| 0.3101 \| 1.2798 \| 0.81 \| 0.7928 \| 0.2205 \| 0.0624 \| \| No log \| 26.0 \| 338 \| 0.5007 \| 0.8 \| 0.3100 \| 1.2390 \| 0.8000 \| 0.7730 \| 0.2038 \| 0.0645 \| \| No log \| 27.0 \| 351 \| 0.4779 \| 0.815 \| 0.2865 \| 1.3312 \| 0.815 \| 0.7863 \| 0.2061 \| 0.0518 \| \| No log \| 28.0 \| 364 \| 0.4893 \| 0.825 \| 0.2927 \| 1.3993 \| 0.825 \| 0.8009 \| 0.2219 \| 0.0555 \| \| No log \| 29.0 \| 377 \| 0.4938 \| 0.82 \| 0.2996 \| 1.4038 \| 0.82 \| 0.7888 \| 0.2138 \| 0.0586 \| \| No log \| 30.0 \| 390 \| 0.4668 \| 0.82 \| 0.2795 \| 1.3366 \| 0.82 \| 0.7944 \| 0.2217 \| 0.0495 \| \| No log \| 31.0 \| 403 \| 0.4662 \| 0.8 \| 0.2805 \| 1.1721 \| 0.8000 \| 0.7761 \| 0.2009 \| 0.0494 \| \| No log \| 32.0 \| 416 \| 0.4787 \| 0.82 \| 0.2887 \| 1.3872 \| 0.82 \| 0.8043 \| 0.2161 \| 0.0542 \| \| No log \| 33.0 \| 429 \| 0.4842 \| 0.81 \| 0.2909 \| 1.4774 \| 0.81 \| 0.7854 \| 0.2246 \| 0.0562 \| \| No log \| 34.0 \| 442 \| 0.4899 \| 0.81 \| 0.2979 \| 1.4419 \| 0.81 \| 0.7843 \| 0.2155 \| 0.0607 \| \| No log \| 35.0 \| 455 \| 0.4832 \| 0.815 \| 0.2920 \| 1.3892 \| 0.815 \| 0.7907 \| 0.2296 \| 0.0552 \| \| No log \| 36.0 \| 468 \| 0.4739 \| 0.815 \| 0.2869 \| 1.2603 \| 0.815 \| 0.7932 \| 0.2385 \| 0.0532 \| \| No log \| 37.0 \| 481 \| 0.4747 \| 0.81 \| 0.2877 \| 1.4390 \| 0.81 \| 0.7848 \| 0.2163 \| 0.0526 \| \| No log \| 38.0 \| 494 \| 0.4710 \| 0.815 \| 0.2842 \| 1.3024 \| 0.815 \| 0.7885 \| 0.2153 \| 0.0516 \| \| 0.2992 \| 39.0 \| 507 \| 0.4712 \| 0.81 \| 0.2839 \| 1.3676 \| 0.81 \| 0.7860 \| 0.2282 \| 0.0518 \| \| 0.2992 \| 40.0 \| 520 \| 0.4772 \| 0.815 \| 0.2883 \| 1.3845 \| 0.815 \| 0.7953 \| 0.2216 \| 0.0527 \| \| 0.2992 \| 41.0 \| 533 \| 0.4751 \| 0.82 \| 0.2877 \| 1.3207 \| 0.82 \| 0.8018 \| 0.2177 \| 0.0521 \| \| 0.2992 \| 42.0 \| 546 \| 0.4724 \| 0.82 \| 0.2860 \| 1.3075 \| 0.82 \| 0.8018 \| 0.2183 \| 0.0508 \| \| 0.2992 \| 43.0 \| 559 \| 0.4745 \| 0.82 \| 0.2869 \| 1.3079 \| 0.82 \| 0.8020 \| 0.2184 \| 0.0522 \| \| 0.2992 \| 44.0 \| 572 \| 0.4779 \| 0.815 \| 0.2884 \| 1.4039 \| 0.815 \| 0.7922 \| 0.2142 \| 0.0531 \| \| 0.2992 \| 45.0 \| 585 \| 0.4738 \| 0.82 \| 0.2859 \| 1.3153 \| 0.82 \| 0.8018 \| 0.2079 \| 0.0516 \| \| 0.2992 \| 46.0 \| 598 \| 0.4755 \| 0.815 \| 0.2874 \| 1.3273 \| 0.815 \| 0.7922 \| 0.2279 \| 0.0526 \| \| 0.2992 \| 47.0 \| 611 \| 0.4736 \| 0.82 \| 0.2858 \| 1.3190 \| 0.82 \| 0.8018 \| 0.2182 \| 0.0515 \| \| 0.2992 \| 48.0 \| 624 \| 0.4753 \| 0.82 \| 0.2876 \| 1.3170 \| 0.82 \| 0.8018 \| 0.2274 \| 0.0521 \| \| 0.2992 \| 49.0 \| 637 \| 0.4755 \| 0.82 \| 0.2866 \| 1.4452 \| 0.82 \| 0.8018 \| 0.2245 \| 0.0516 \| \| 0.2992 \| 50.0 \| 650 \| 0.4754 \| 0.815 \| 0.2869 \| 1.2915 \| 0.815 \| 0.7924 \| 0.2336 \| 0.0523 \| \| 0.2992 \| 51.0 \| 663 \| 0.4747 \| 0.82 \| 0.2861 \| 1.3336 \| 0.82 \| 0.8020 \| 0.2309 \| 0.0517 \| \| 0.2992 \| 52.0 \| 676 \| 0.4765 \| 0.815 \| 0.2880 \| 1.3456 \| 0.815 \| 0.7924 \| 0.2137 \| 0.0524 \| \| 0.2992 \| 53.0 \| 689 \| 0.4756 \| 0.82 \| 0.2866 \| 1.3288 \| 0.82 \| 0.8020 \| 0.2236 \| 0.0518 \| \| 0.2992 \| 54.0 \| 702 \| 0.4757 \| 0.82 \| 0.2873 \| 1.3860 \| 0.82 \| 0.8018 \| 0.2085 \| 0.0516 \| \| 0.2992 \| 55.0 \| 715 \| 0.4753 \| 0.815 \| 0.2866 \| 1.3284 \| 0.815 \| 0.7922 \| 0.2100 \| 0.0515 \| \| 0.2992 \| 56.0 \| 728 \| 0.4759 \| 0.82 \| 0.2870 \| 1.3199 \| 0.82 \| 0.8020 \| 0.2240 \| 0.0518 \| \| 0.2992 \| 57.0 \| 741 \| 0.4764 \| 0.82 \| 0.2874 \| 1.3901 \| 0.82 \| 0.8020 \| 0.2241 \| 0.0517 \| \| 0.2992 \| 58.0 \| 754 \| 0.4754 \| 0.815 \| 0.2870 \| 1.3246 \| 0.815 \| 0.7924 \| 0.2260 \| 0.0520 \| \| 0.2992 \| 59.0 \| 767 \| 0.4759 \| 0.815 \| 0.2870 \| 1.3862 \| 0.815 \| 0.7924 \| 0.2176 \| 0.0520 \| \| 0.2992 \| 60.0 \| 780 \| 0.4765 \| 0.815 \| 0.2874 \| 1.3873 \| 0.815 \| 0.7924 \| 0.2266 \| 0.0523 \| \| 0.2992 \| 61.0 \| 793 \| 0.4763 \| 0.82 \| 0.2873 \| 1.3851 \| 0.82 \| 0.8020 \| 0.2161 \| 0.0517 \| \| 0.2992 \| 62.0 \| 806 \| 0.4768 \| 0.815 \| 0.2878 \| 1.3903 \| 0.815 \| 0.7924 \| 0.2128 \| 0.0522 \| \| 0.2992 \| 63.0 \| 819 \| 0.4767 \| 0.82 \| 0.2876 \| 1.3866 \| 0.82 \| 0.8020 \| 0.2120 \| 0.0521 \| \| 0.2992 \| 64.0 \| 832 \| 0.4762 \| 0.82 \| 0.2872 \| 1.3910 \| 0.82 \| 0.8020 \| 0.2157 \| 0.0516 \| \| 0.2992 \| 65.0 \| 845 \| 0.4765 \| 0.82 \| 0.2874 \| 1.3892 \| 0.82 \| 0.8020 \| 0.2178 \| 0.0519 \| \| 0.2992 \| 66.0 \| 858 \| 0.4767 \| 0.82 \| 0.2875 \| 1.3462 \| 0.82 \| 0.8020 \| 0.2180 \| 0.0519 \| \| 0.2992 \| 67.0 \| 871 \| 0.4764 \| 0.82 \| 0.2872 \| 1.3894 \| 0.82 \| 0.8020 \| 0.2252 \| 0.0518 \| \| 0.2992 \| 68.0 \| 884 \| 0.4767 \| 0.82 \| 0.2874 \| 1.3860 \| 0.82 \| 0.8020 \| 0.2118 \| 0.0518 \| \| 0.2992 \| 69.0 \| 897 \| 0.4766 \| 0.82 \| 0.2874 \| 1.3894 \| 0.82 \| 0.8020 \| 0.2180 \| 0.0519 \| \| 0.2992 \| 70.0 \| 910 \| 0.4765 \| 0.82 \| 0.2872 \| 1.3882 \| 0.82 \| 0.8020 \| 0.2280 \| 0.0517 \| \| 0.2992 \| 71.0 \| 923 \| 0.4766 \| 0.82 \| 0.2874 \| 1.3875 \| 0.82 \| 0.8020 \| 0.2177 \| 0.0519 \| \| 0.2992 \| 72.0 \| 936 \| 0.4765 \| 0.82 \| 0.2874 \| 1.3880 \| 0.82 \| 0.8020 \| 0.2148 \| 0.0517 \| \| 0.2992 \| 73.0 \| 949 \| 0.4766 \| 0.82 \| 0.2873 \| 1.3915 \| 0.82 \| 0.8020 \| 0.2109 \| 0.0516 \| \| 0.2992 \| 74.0 \| 962 \| 0.4765 \| 0.82 \| 0.2872 \| 1.3900 \| 0.82 \| 0.8020 \| 0.2110 \| 0.0517 \| \| 0.2992 \| 75.0 \| 975 \| 0.4769 \| 0.82 \| 0.2875 \| 1.3913 \| 0.82 \| 0.8020 \| 0.2251 \| 0.0520 \| \| 0.2992 \| 76.0 \| 988 \| 0.4770 \| 0.82 \| 0.2876 \| 1.3909 \| 0.82 \| 0.8020 \| 0.2196 \| 0.0520 \| \| 0.0695 \| 77.0 \| 1001 \| 0.4768 \| 0.82 \| 0.2875 \| 1.3890 \| 0.82 \| 0.8020 \| 0.2212 \| 0.0517 \| \| 0.0695 \| 78.0 \| 1014 \| 0.4767 \| 0.82 \| 0.2873 \| 1.3935 \| 0.82 \| 0.8020 \| 0.2281 \| 0.0518 \| \| 0.0695 \| 79.0 \| 1027 \| 0.4767 \| 0.82 \| 0.2874 \| 1.3897 \| 0.82 \| 0.8020 \| 0.2282 \| 0.0517 \| \| 0.0695 \| 80.0 \| 1040 \| 0.4770 \| 0.82 \| 0.2876 \| 1.3889 \| 0.82 \| 0.8020 \| 0.2174 \| 0.0518 \| \| 0.0695 \| 81.0 \| 1053 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3935 \| 0.82 \| 0.8020 \| 0.2221 \| 0.0518 \| \| 0.0695 \| 82.0 \| 1066 \| 0.4766 \| 0.82 \| 0.2873 \| 1.3901 \| 0.82 \| 0.8020 \| 0.2283 \| 0.0517 \| \| 0.0695 \| 83.0 \| 1079 \| 0.4768 \| 0.82 \| 0.2874 \| 1.3902 \| 0.82 \| 0.8020 \| 0.2283 \| 0.0517 \| \| 0.0695 \| 84.0 \| 1092 \| 0.4770 \| 0.82 \| 0.2874 \| 1.3917 \| 0.82 \| 0.8020 \| 0.2217 \| 0.0518 \| \| 0.0695 \| 85.0 \| 1105 \| 0.4769 \| 0.82 \| 0.2875 \| 1.3913 \| 0.82 \| 0.8020 \| 0.2283 \| 0.0518 \| \| 0.0695 \| 86.0 \| 1118 \| 0.4769 \| 0.82 \| 0.2874 \| 1.3916 \| 0.82 \| 0.8020 \| 0.2282 \| 0.0517 \| \| 0.0695 \| 87.0 \| 1131 \| 0.4769 \| 0.82 \| 0.2874 \| 1.3912 \| 0.82 \| 0.8020 \| 0.2218 \| 0.0517 \| \| 0.0695 \| 88.0 \| 1144 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3923 \| 0.82 \| 0.8020 \| 0.2218 \| 0.0517 \| \| 0.0695 \| 89.0 \| 1157 \| 0.4768 \| 0.82 \| 0.2874 \| 1.3905 \| 0.82 \| 0.8020 \| 0.2283 \| 0.0518 \| \| 0.0695 \| 90.0 \| 1170 \| 0.4769 \| 0.82 \| 0.2875 \| 1.3924 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 91.0 \| 1183 \| 0.4769 \| 0.82 \| 0.2874 \| 1.3923 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 92.0 \| 1196 \| 0.4768 \| 0.82 \| 0.2874 \| 1.3908 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 93.0 \| 1209 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3909 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0518 \| \| 0.0695 \| 94.0 \| 1222 \| 0.4768 \| 0.82 \| 0.2873 \| 1.3918 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 95.0 \| 1235 \| 0.4769 \| 0.82 \| 0.2874 \| 1.3914 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 96.0 \| 1248 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3917 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 97.0 \| 1261 \| 0.4769 \| 0.82 \| 0.2874 \| 1.3918 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 98.0 \| 1274 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3920 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 99.0 \| 1287 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3922 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| \| 0.0695 \| 100.0 \| 1300 \| 0.4770 \| 0.82 \| 0.2875 \| 1.3922 \| 0.82 \| 0.8020 \| 0.2219 \| 0.0517 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]
jordyvl/300-tiny_tobacco3482_kd	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 300-tiny_tobacco3482_kd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 - Accuracy: 0.79 - Brier Loss: 0.3334 - Nll: 1.0051 - F1 Micro: 0.79 - F1 Macro: 0.7591 - Ece: 0.2152 - Aurc: 0.0601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 13 \| 1.7587 \| 0.225 \| 0.8896 \| 7.6699 \| 0.225 \| 0.1499 \| 0.2894 \| 0.7588 \| \| No log \| 2.0 \| 26 \| 1.1905 \| 0.4 \| 0.7901 \| 3.7933 \| 0.4000 \| 0.2961 \| 0.3234 \| 0.4395 \| \| No log \| 3.0 \| 39 \| 0.9530 \| 0.53 \| 0.6670 \| 2.9907 \| 0.53 \| 0.4066 \| 0.3137 \| 0.2821 \| \| No log \| 4.0 \| 52 \| 0.8046 \| 0.615 \| 0.5821 \| 2.2124 \| 0.615 \| 0.4898 \| 0.3097 \| 0.1862 \| \| No log \| 5.0 \| 65 \| 0.7084 \| 0.685 \| 0.5084 \| 2.1522 \| 0.685 \| 0.6027 \| 0.3191 \| 0.1389 \| \| No log \| 6.0 \| 78 \| 0.7243 \| 0.68 \| 0.4683 \| 2.2673 \| 0.68 \| 0.5904 \| 0.2695 \| 0.1279 \| \| No log \| 7.0 \| 91 \| 0.6734 \| 0.67 \| 0.4675 \| 2.2909 \| 0.67 \| 0.5844 \| 0.2436 \| 0.1510 \| \| No log \| 8.0 \| 104 \| 0.5780 \| 0.7 \| 0.4215 \| 2.0061 \| 0.7 \| 0.6160 \| 0.2418 \| 0.1016 \| \| No log \| 9.0 \| 117 \| 0.6270 \| 0.71 \| 0.4402 \| 1.8620 \| 0.7100 \| 0.6574 \| 0.2485 \| 0.1249 \| \| No log \| 10.0 \| 130 \| 0.5604 \| 0.72 \| 0.4074 \| 1.5914 \| 0.72 \| 0.6430 \| 0.2566 \| 0.0935 \| \| No log \| 11.0 \| 143 \| 0.5814 \| 0.705 \| 0.4079 \| 1.6933 \| 0.705 \| 0.6190 \| 0.2350 \| 0.1035 \| \| No log \| 12.0 \| 156 \| 0.5901 \| 0.71 \| 0.4176 \| 1.7974 \| 0.7100 \| 0.6472 \| 0.2225 \| 0.1058 \| \| No log \| 13.0 \| 169 \| 0.5041 \| 0.71 \| 0.3918 \| 1.8429 \| 0.7100 \| 0.6562 \| 0.2336 \| 0.0958 \| \| No log \| 14.0 \| 182 \| 0.5099 \| 0.72 \| 0.3982 \| 1.6343 \| 0.72 \| 0.6550 \| 0.2202 \| 0.1021 \| \| No log \| 15.0 \| 195 \| 0.4843 \| 0.745 \| 0.3951 \| 1.3599 \| 0.745 \| 0.6719 \| 0.2680 \| 0.0884 \| \| No log \| 16.0 \| 208 \| 0.4529 \| 0.74 \| 0.3776 \| 1.3838 \| 0.74 \| 0.6951 \| 0.2112 \| 0.0839 \| \| No log \| 17.0 \| 221 \| 0.4420 \| 0.745 \| 0.3782 \| 1.4403 \| 0.745 \| 0.6982 \| 0.2285 \| 0.0800 \| \| No log \| 18.0 \| 234 \| 0.4428 \| 0.755 \| 0.3710 \| 1.3696 \| 0.755 \| 0.7298 \| 0.2170 \| 0.0825 \| \| No log \| 19.0 \| 247 \| 0.4306 \| 0.75 \| 0.3794 \| 1.4095 \| 0.75 \| 0.7235 \| 0.2470 \| 0.0862 \| \| No log \| 20.0 \| 260 \| 0.4166 \| 0.74 \| 0.3648 \| 1.2893 \| 0.74 \| 0.6776 \| 0.2312 \| 0.0835 \| \| No log \| 21.0 \| 273 \| 0.3830 \| 0.77 \| 0.3524 \| 1.1764 \| 0.7700 \| 0.7256 \| 0.2535 \| 0.0730 \| \| No log \| 22.0 \| 286 \| 0.3918 \| 0.77 \| 0.3564 \| 1.2293 \| 0.7700 \| 0.7067 \| 0.2372 \| 0.0710 \| \| No log \| 23.0 \| 299 \| 0.4125 \| 0.75 \| 0.3656 \| 1.1419 \| 0.75 \| 0.7109 \| 0.2357 \| 0.0766 \| \| No log \| 24.0 \| 312 \| 0.3771 \| 0.785 \| 0.3543 \| 1.0960 \| 0.785 \| 0.7583 \| 0.2345 \| 0.0712 \| \| No log \| 25.0 \| 325 \| 0.3846 \| 0.745 \| 0.3613 \| 1.0616 \| 0.745 \| 0.7061 \| 0.2060 \| 0.0766 \| \| No log \| 26.0 \| 338 \| 0.3660 \| 0.77 \| 0.3547 \| 1.3094 \| 0.7700 \| 0.7196 \| 0.2515 \| 0.0724 \| \| No log \| 27.0 \| 351 \| 0.3634 \| 0.78 \| 0.3476 \| 1.0645 \| 0.78 \| 0.7479 \| 0.2401 \| 0.0677 \| \| No log \| 28.0 \| 364 \| 0.3715 \| 0.755 \| 0.3522 \| 1.1981 \| 0.755 \| 0.6984 \| 0.2257 \| 0.0709 \| \| No log \| 29.0 \| 377 \| 0.3701 \| 0.765 \| 0.3597 \| 1.1645 \| 0.765 \| 0.7239 \| 0.2631 \| 0.0747 \| \| No log \| 30.0 \| 390 \| 0.3562 \| 0.775 \| 0.3465 \| 1.1094 \| 0.775 \| 0.7140 \| 0.2428 \| 0.0659 \| \| No log \| 31.0 \| 403 \| 0.3811 \| 0.775 \| 0.3499 \| 1.2515 \| 0.775 \| 0.7368 \| 0.2214 \| 0.0694 \| \| No log \| 32.0 \| 416 \| 0.3555 \| 0.77 \| 0.3439 \| 1.1715 \| 0.7700 \| 0.7053 \| 0.2532 \| 0.0705 \| \| No log \| 33.0 \| 429 \| 0.3592 \| 0.775 \| 0.3449 \| 1.1606 \| 0.775 \| 0.7364 \| 0.2336 \| 0.0729 \| \| No log \| 34.0 \| 442 \| 0.3555 \| 0.78 \| 0.3431 \| 1.1054 \| 0.78 \| 0.7373 \| 0.2143 \| 0.0653 \| \| No log \| 35.0 \| 455 \| 0.3454 \| 0.77 \| 0.3415 \| 1.0386 \| 0.7700 \| 0.7333 \| 0.2463 \| 0.0668 \| \| No log \| 36.0 \| 468 \| 0.3403 \| 0.8 \| 0.3394 \| 1.1435 \| 0.8000 \| 0.7664 \| 0.2674 \| 0.0625 \| \| No log \| 37.0 \| 481 \| 0.3390 \| 0.785 \| 0.3379 \| 1.1183 \| 0.785 \| 0.7552 \| 0.2432 \| 0.0633 \| \| No log \| 38.0 \| 494 \| 0.3413 \| 0.79 \| 0.3347 \| 1.1538 \| 0.79 \| 0.7406 \| 0.2239 \| 0.0615 \| \| 0.2994 \| 39.0 \| 507 \| 0.3364 \| 0.795 \| 0.3362 \| 0.9975 \| 0.795 \| 0.7650 \| 0.2334 \| 0.0639 \| \| 0.2994 \| 40.0 \| 520 \| 0.3340 \| 0.79 \| 0.3328 \| 1.0045 \| 0.79 \| 0.7466 \| 0.2711 \| 0.0580 \| \| 0.2994 \| 41.0 \| 533 \| 0.3381 \| 0.77 \| 0.3391 \| 0.9829 \| 0.7700 \| 0.7427 \| 0.2147 \| 0.0675 \| \| 0.2994 \| 42.0 \| 546 \| 0.3297 \| 0.8 \| 0.3319 \| 1.0739 \| 0.8000 \| 0.7685 \| 0.2613 \| 0.0585 \| \| 0.2994 \| 43.0 \| 559 \| 0.3338 \| 0.8 \| 0.3373 \| 1.1507 \| 0.8000 \| 0.7719 \| 0.2491 \| 0.0637 \| \| 0.2994 \| 44.0 \| 572 \| 0.3316 \| 0.79 \| 0.3359 \| 1.1274 \| 0.79 \| 0.7539 \| 0.2469 \| 0.0620 \| \| 0.2994 \| 45.0 \| 585 \| 0.3283 \| 0.79 \| 0.3336 \| 1.0644 \| 0.79 \| 0.7531 \| 0.2636 \| 0.0612 \| \| 0.2994 \| 46.0 \| 598 \| 0.3297 \| 0.8 \| 0.3344 \| 1.1343 \| 0.8000 \| 0.7670 \| 0.2317 \| 0.0600 \| \| 0.2994 \| 47.0 \| 611 \| 0.3293 \| 0.79 \| 0.3318 \| 1.0692 \| 0.79 \| 0.7542 \| 0.2396 \| 0.0616 \| \| 0.2994 \| 48.0 \| 624 \| 0.3339 \| 0.79 \| 0.3357 \| 1.1225 \| 0.79 \| 0.7590 \| 0.2508 \| 0.0617 \| \| 0.2994 \| 49.0 \| 637 \| 0.3290 \| 0.795 \| 0.3343 \| 1.0692 \| 0.795 \| 0.7618 \| 0.2529 \| 0.0604 \| \| 0.2994 \| 50.0 \| 650 \| 0.3298 \| 0.79 \| 0.3348 \| 1.1343 \| 0.79 \| 0.7591 \| 0.2330 \| 0.0609 \| \| 0.2994 \| 51.0 \| 663 \| 0.3305 \| 0.795 \| 0.3330 \| 1.0045 \| 0.795 \| 0.7618 \| 0.2357 \| 0.0607 \| \| 0.2994 \| 52.0 \| 676 \| 0.3299 \| 0.79 \| 0.3339 \| 1.0722 \| 0.79 \| 0.7542 \| 0.2562 \| 0.0614 \| \| 0.2994 \| 53.0 \| 689 \| 0.3280 \| 0.8 \| 0.3325 \| 1.0688 \| 0.8000 \| 0.7685 \| 0.2500 \| 0.0593 \| \| 0.2994 \| 54.0 \| 702 \| 0.3284 \| 0.795 \| 0.3323 \| 1.0175 \| 0.795 \| 0.7618 \| 0.2436 \| 0.0598 \| \| 0.2994 \| 55.0 \| 715 \| 0.3287 \| 0.79 \| 0.3331 \| 1.0750 \| 0.79 \| 0.7591 \| 0.2497 \| 0.0604 \| \| 0.2994 \| 56.0 \| 728 \| 0.3286 \| 0.795 \| 0.3335 \| 1.0115 \| 0.795 \| 0.7618 \| 0.2296 \| 0.0602 \| \| 0.2994 \| 57.0 \| 741 \| 0.3285 \| 0.79 \| 0.3330 \| 1.0648 \| 0.79 \| 0.7591 \| 0.2446 \| 0.0602 \| \| 0.2994 \| 58.0 \| 754 \| 0.3299 \| 0.795 \| 0.3339 \| 1.0193 \| 0.795 \| 0.7618 \| 0.2345 \| 0.0608 \| \| 0.2994 \| 59.0 \| 767 \| 0.3294 \| 0.79 \| 0.3329 \| 1.0139 \| 0.79 \| 0.7591 \| 0.2369 \| 0.0601 \| \| 0.2994 \| 60.0 \| 780 \| 0.3292 \| 0.795 \| 0.3332 \| 1.0118 \| 0.795 \| 0.7618 \| 0.2226 \| 0.0601 \| \| 0.2994 \| 61.0 \| 793 \| 0.3293 \| 0.795 \| 0.3333 \| 1.0716 \| 0.795 \| 0.7618 \| 0.2282 \| 0.0602 \| \| 0.2994 \| 62.0 \| 806 \| 0.3294 \| 0.795 \| 0.3331 \| 1.0107 \| 0.795 \| 0.7618 \| 0.2224 \| 0.0601 \| \| 0.2994 \| 63.0 \| 819 \| 0.3295 \| 0.795 \| 0.3336 \| 1.0144 \| 0.795 \| 0.7618 \| 0.2294 \| 0.0605 \| \| 0.2994 \| 64.0 \| 832 \| 0.3293 \| 0.795 \| 0.3332 \| 1.0104 \| 0.795 \| 0.7618 \| 0.2324 \| 0.0603 \| \| 0.2994 \| 65.0 \| 845 \| 0.3298 \| 0.795 \| 0.3337 \| 1.0114 \| 0.795 \| 0.7618 \| 0.2478 \| 0.0606 \| \| 0.2994 \| 66.0 \| 858 \| 0.3297 \| 0.795 \| 0.3333 \| 1.0076 \| 0.795 \| 0.7618 \| 0.2366 \| 0.0601 \| \| 0.2994 \| 67.0 \| 871 \| 0.3298 \| 0.79 \| 0.3338 \| 1.0120 \| 0.79 \| 0.7591 \| 0.2513 \| 0.0606 \| \| 0.2994 \| 68.0 \| 884 \| 0.3297 \| 0.795 \| 0.3337 \| 1.0110 \| 0.795 \| 0.7618 \| 0.2376 \| 0.0605 \| \| 0.2994 \| 69.0 \| 897 \| 0.3297 \| 0.795 \| 0.3335 \| 1.0115 \| 0.795 \| 0.7618 \| 0.2228 \| 0.0602 \| \| 0.2994 \| 70.0 \| 910 \| 0.3292 \| 0.795 \| 0.3333 \| 1.0089 \| 0.795 \| 0.7618 \| 0.2215 \| 0.0602 \| \| 0.2994 \| 71.0 \| 923 \| 0.3297 \| 0.795 \| 0.3334 \| 1.0083 \| 0.795 \| 0.7618 \| 0.2226 \| 0.0600 \| \| 0.2994 \| 72.0 \| 936 \| 0.3297 \| 0.79 \| 0.3335 \| 1.0072 \| 0.79 \| 0.7591 \| 0.2257 \| 0.0604 \| \| 0.2994 \| 73.0 \| 949 \| 0.3297 \| 0.795 \| 0.3332 \| 1.0060 \| 0.795 \| 0.7618 \| 0.2381 \| 0.0600 \| \| 0.2994 \| 74.0 \| 962 \| 0.3295 \| 0.795 \| 0.3335 \| 1.0082 \| 0.795 \| 0.7618 \| 0.2366 \| 0.0603 \| \| 0.2994 \| 75.0 \| 975 \| 0.3296 \| 0.79 \| 0.3334 \| 1.0089 \| 0.79 \| 0.7591 \| 0.2373 \| 0.0601 \| \| 0.2994 \| 76.0 \| 988 \| 0.3298 \| 0.795 \| 0.3334 \| 1.0098 \| 0.795 \| 0.7618 \| 0.2310 \| 0.0602 \| \| 0.0006 \| 77.0 \| 1001 \| 0.3297 \| 0.79 \| 0.3334 \| 1.0084 \| 0.79 \| 0.7591 \| 0.2228 \| 0.0603 \| \| 0.0006 \| 78.0 \| 1014 \| 0.3297 \| 0.79 \| 0.3333 \| 1.0071 \| 0.79 \| 0.7591 \| 0.2148 \| 0.0600 \| \| 0.0006 \| 79.0 \| 1027 \| 0.3298 \| 0.795 \| 0.3334 \| 1.0059 \| 0.795 \| 0.7618 \| 0.2309 \| 0.0602 \| \| 0.0006 \| 80.0 \| 1040 \| 0.3298 \| 0.795 \| 0.3334 \| 1.0046 \| 0.795 \| 0.7618 \| 0.2309 \| 0.0602 \| \| 0.0006 \| 81.0 \| 1053 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0073 \| 0.79 \| 0.7591 \| 0.2239 \| 0.0602 \| \| 0.0006 \| 82.0 \| 1066 \| 0.3298 \| 0.795 \| 0.3336 \| 1.0072 \| 0.795 \| 0.7618 \| 0.2317 \| 0.0603 \| \| 0.0006 \| 83.0 \| 1079 \| 0.3297 \| 0.795 \| 0.3334 \| 1.0055 \| 0.795 \| 0.7618 \| 0.2224 \| 0.0601 \| \| 0.0006 \| 84.0 \| 1092 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0061 \| 0.79 \| 0.7591 \| 0.2240 \| 0.0601 \| \| 0.0006 \| 85.0 \| 1105 \| 0.3297 \| 0.79 \| 0.3334 \| 1.0052 \| 0.79 \| 0.7591 \| 0.2322 \| 0.0601 \| \| 0.0006 \| 86.0 \| 1118 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0059 \| 0.79 \| 0.7591 \| 0.2323 \| 0.0602 \| \| 0.0006 \| 87.0 \| 1131 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0065 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 88.0 \| 1144 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0056 \| 0.79 \| 0.7591 \| 0.2235 \| 0.0603 \| \| 0.0006 \| 89.0 \| 1157 \| 0.3297 \| 0.79 \| 0.3334 \| 1.0050 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 90.0 \| 1170 \| 0.3297 \| 0.79 \| 0.3334 \| 1.0049 \| 0.79 \| 0.7591 \| 0.2153 \| 0.0602 \| \| 0.0006 \| 91.0 \| 1183 \| 0.3297 \| 0.79 \| 0.3334 \| 1.0059 \| 0.79 \| 0.7591 \| 0.2234 \| 0.0601 \| \| 0.0006 \| 92.0 \| 1196 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0049 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 93.0 \| 1209 \| 0.3299 \| 0.79 \| 0.3335 \| 1.0056 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0601 \| \| 0.0006 \| 94.0 \| 1222 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0049 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 95.0 \| 1235 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0048 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 96.0 \| 1248 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0050 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0601 \| \| 0.0006 \| 97.0 \| 1261 \| 0.3298 \| 0.79 \| 0.3335 \| 1.0053 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 98.0 \| 1274 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0051 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0602 \| \| 0.0006 \| 99.0 \| 1287 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0052 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0601 \| \| 0.0006 \| 100.0 \| 1300 \| 0.3298 \| 0.79 \| 0.3334 \| 1.0051 \| 0.79 \| 0.7591 \| 0.2152 \| 0.0601 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

Taki3d/CrackDetectionLowRes

# CrackDetectionLowRes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9940 - Loss: 0.0183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.0126 | 1.0 | 992 | 0.9879 | 0.0344 | | 0.0788 | 2.0 | 1904 | 0.9933 | 0.0220 | | 0.1336 | 3.0 | 2856 | 0.9933 | 0.0222 | | 0.0066 | 4.0 | 3808 | 0.9933 | 0.0190 | | 0.0528 | 5.0 | 4760 | 0.9940 | 0.0183 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

[ "crack", "noncrack" ]

jordyvl/vit-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# vit-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6948 - Accuracy: 0.85 - Brier Loss: 0.2427 - Nll: 1.2265 - F1 Micro: 0.85 - F1 Macro: 0.8401 - Ece: 0.1325 - Aurc: 0.0510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 6.1834 | 0.09 | 1.0218 | 8.8168 | 0.09 | 0.0691 | 0.3227 | 0.8979 | | No log | 2.0 | 14 | 5.0430 | 0.14 | 0.9049 | 8.5503 | 0.14 | 0.1102 | 0.2495 | 0.7963 | | No log | 3.0 | 21 | 4.6038 | 0.26 | 0.8385 | 6.2625 | 0.26 | 0.1872 | 0.2701 | 0.5801 | | No log | 4.0 | 28 | 4.3342 | 0.475 | 0.7283 | 4.0652 | 0.4750 | 0.3906 | 0.3309 | 0.3230 | | No log | 5.0 | 35 | 4.0844 | 0.575 | 0.6168 | 2.7058 | 0.575 | 0.4691 | 0.2963 | 0.2299 | | No log | 6.0 | 42 | 3.9551 | 0.67 | 0.5721 | 2.2402 | 0.67 | 0.5531 | 0.3412 | 0.1586 | | No log | 7.0 | 49 | 3.8114 | 0.695 | 0.4999 | 1.8621 | 0.695 | 0.5789 | 0.3028 | 0.1342 | | No log | 8.0 | 56 | 3.7627 | 0.77 | 0.4472 | 1.6313 | 0.7700 | 0.7306 | 0.3170 | 0.0894 | | No log | 9.0 | 63 | 3.7510 | 0.75 | 0.4223 | 1.4350 | 0.75 | 0.6574 | 0.2576 | 0.1046 | | No log | 10.0 | 70 | 3.6586 | 0.8 | 0.3576 | 1.5473 | 0.8000 | 0.7615 | 0.2754 | 0.0701 | | No log | 11.0 | 77 | 3.6643 | 0.825 | 0.3327 | 1.4335 | 0.825 | 0.7921 | 0.2555 | 0.0670 | | No log | 12.0 | 84 | 3.6561 | 0.79 | 0.3465 | 1.3859 | 0.79 | 0.7541 | 0.2165 | 0.0833 | | No log | 13.0 | 91 | 3.6518 | 0.805 | 0.3209 | 1.0963 | 0.805 | 0.7716 | 0.1894 | 0.0706 | | No log | 14.0 | 98 | 3.6401 | 0.84 | 0.3045 | 1.1974 | 0.8400 | 0.8290 | 0.2261 | 0.0551 | | No log | 15.0 | 105 | 3.6253 | 0.79 | 0.3200 | 1.2187 | 0.79 | 0.7658 | 0.1973 | 0.0777 | | No log | 16.0 | 112 | 3.6019 | 0.83 | 0.2920 | 1.0547 | 0.83 | 0.8050 | 0.1655 | 0.0632 | | No log | 17.0 | 119 | 3.5622 | 0.84 | 0.2741 | 1.0769 | 0.8400 | 0.8228 | 0.1641 | 0.0636 | | No log | 18.0 | 126 | 3.5921 | 0.835 | 0.2706 | 1.0996 | 0.835 | 0.8205 | 0.1810 | 0.0581 | | No log | 19.0 | 133 | 3.5886 | 0.815 | 0.2773 | 1.2384 | 0.815 | 0.7975 | 0.1772 | 0.0643 | | No log | 20.0 | 140 | 3.5798 | 0.84 | 0.2732 | 1.4226 | 0.8400 | 0.8236 | 0.1624 | 0.0672 | | No log | 21.0 | 147 | 3.5683 | 0.82 | 0.2868 | 1.3978 | 0.82 | 0.7990 | 0.1629 | 0.0681 | | No log | 22.0 | 154 | 3.5891 | 0.825 | 0.2893 | 1.4084 | 0.825 | 0.8024 | 0.1856 | 0.0666 | | No log | 23.0 | 161 | 3.5484 | 0.82 | 0.2595 | 1.0782 | 0.82 | 0.7990 | 0.1477 | 0.0595 | | No log | 24.0 | 168 | 3.5882 | 0.8 | 0.2686 | 1.0513 | 0.8000 | 0.7745 | 0.1334 | 0.0596 | | No log | 25.0 | 175 | 3.5636 | 0.81 | 0.2774 | 1.1250 | 0.81 | 0.7838 | 0.1563 | 0.0662 | | No log | 26.0 | 182 | 3.5478 | 0.82 | 0.2669 | 1.0724 | 0.82 | 0.8021 | 0.1318 | 0.0576 | | No log | 27.0 | 189 | 3.5092 | 0.84 | 0.2487 | 1.0652 | 0.8400 | 0.8144 | 0.1500 | 0.0567 | | No log | 28.0 | 196 | 3.5746 | 0.815 | 0.2810 | 1.3942 | 0.815 | 0.7997 | 0.1547 | 0.0682 | | No log | 29.0 | 203 | 3.5633 | 0.835 | 0.2587 | 1.1580 | 0.835 | 0.8154 | 0.1476 | 0.0604 | | No log | 30.0 | 210 | 3.5221 | 0.835 | 0.2591 | 0.9385 | 0.835 | 0.8180 | 0.1496 | 0.0584 | | No log | 31.0 | 217 | 3.6263 | 0.83 | 0.2676 | 1.3260 | 0.83 | 0.8213 | 0.1670 | 0.0550 | | No log | 32.0 | 224 | 3.5758 | 0.825 | 0.2855 | 1.3100 | 0.825 | 0.8082 | 0.1453 | 0.0637 | | No log | 33.0 | 231 | 3.5836 | 0.84 | 0.2550 | 0.9703 | 0.8400 | 0.8268 | 0.1344 | 0.0521 | | No log | 34.0 | 238 | 3.5466 | 0.825 | 0.2580 | 1.3118 | 0.825 | 0.8071 | 0.1553 | 0.0604 | | No log | 35.0 | 245 | 3.5566 | 0.835 | 0.2574 | 1.1729 | 0.835 | 0.8134 | 0.1357 | 0.0592 | | No log | 36.0 | 252 | 3.6022 | 0.83 | 0.2848 | 1.3337 | 0.83 | 0.8158 | 0.1528 | 0.0631 | | No log | 37.0 | 259 | 3.5422 | 0.845 | 0.2598 | 1.2362 | 0.845 | 0.8311 | 0.1488 | 0.0588 | | No log | 38.0 | 266 | 3.5677 | 0.825 | 0.2662 | 1.2509 | 0.825 | 0.8026 | 0.1417 | 0.0566 | | No log | 39.0 | 273 | 3.5600 | 0.83 | 0.2673 | 1.2032 | 0.83 | 0.8128 | 0.1344 | 0.0561 | | No log | 40.0 | 280 | 3.5818 | 0.82 | 0.2634 | 1.1062 | 0.82 | 0.8070 | 0.1389 | 0.0558 | | No log | 41.0 | 287 | 3.5326 | 0.85 | 0.2520 | 1.3207 | 0.85 | 0.8404 | 0.1485 | 0.0529 | | No log | 42.0 | 294 | 3.5954 | 0.83 | 0.2708 | 1.1103 | 0.83 | 0.8092 | 0.1401 | 0.0595 | | No log | 43.0 | 301 | 3.5330 | 0.84 | 0.2528 | 1.2339 | 0.8400 | 0.8241 | 0.1405 | 0.0540 | | No log | 44.0 | 308 | 3.5696 | 0.825 | 0.2654 | 1.1943 | 0.825 | 0.8079 | 0.1358 | 0.0544 | | No log | 45.0 | 315 | 3.5438 | 0.83 | 0.2558 | 1.1267 | 0.83 | 0.8138 | 0.1314 | 0.0530 | | No log | 46.0 | 322 | 3.5537 | 0.845 | 0.2497 | 1.2612 | 0.845 | 0.8298 | 0.1338 | 0.0525 | | No log | 47.0 | 329 | 3.5609 | 0.85 | 0.2467 | 1.4284 | 0.85 | 0.8315 | 0.1333 | 0.0563 | | No log | 48.0 | 336 | 3.5723 | 0.835 | 0.2595 | 1.1814 | 0.835 | 0.8187 | 0.1402 | 0.0545 | | No log | 49.0 | 343 | 3.5591 | 0.825 | 0.2485 | 1.1736 | 0.825 | 0.8072 | 0.1429 | 0.0536 | | No log | 50.0 | 350 | 3.5715 | 0.825 | 0.2585 | 1.3645 | 0.825 | 0.8098 | 0.1445 | 0.0564 | | No log | 51.0 | 357 | 3.5813 | 0.83 | 0.2617 | 1.2375 | 0.83 | 0.8210 | 0.1371 | 0.0551 | | No log | 52.0 | 364 | 3.6084 | 0.835 | 0.2592 | 1.2465 | 0.835 | 0.8168 | 0.1557 | 0.0550 | | No log | 53.0 | 371 | 3.5574 | 0.84 | 0.2474 | 1.1932 | 0.8400 | 0.8255 | 0.1351 | 0.0543 | | No log | 54.0 | 378 | 3.5863 | 0.85 | 0.2428 | 1.2885 | 0.85 | 0.8346 | 0.1347 | 0.0536 | | No log | 55.0 | 385 | 3.5510 | 0.83 | 0.2520 | 1.2654 | 0.83 | 0.8163 | 0.1342 | 0.0543 | | No log | 56.0 | 392 | 3.5516 | 0.835 | 0.2476 | 1.0430 | 0.835 | 0.8210 | 0.1336 | 0.0549 | | No log | 57.0 | 399 | 3.5754 | 0.835 | 0.2475 | 1.3656 | 0.835 | 0.8245 | 0.1165 | 0.0528 | | No log | 58.0 | 406 | 3.6017 | 0.83 | 0.2584 | 1.3561 | 0.83 | 0.8198 | 0.1490 | 0.0542 | | No log | 59.0 | 413 | 3.5767 | 0.845 | 0.2488 | 1.2699 | 0.845 | 0.8357 | 0.1291 | 0.0521 | | No log | 60.0 | 420 | 3.5844 | 0.835 | 0.2513 | 1.2919 | 0.835 | 0.8218 | 0.1286 | 0.0541 | | No log | 61.0 | 427 | 3.5744 | 0.84 | 0.2443 | 1.2315 | 0.8400 | 0.8334 | 0.1441 | 0.0515 | | No log | 62.0 | 434 | 3.5948 | 0.825 | 0.2505 | 1.2265 | 0.825 | 0.8052 | 0.1266 | 0.0539 | | No log | 63.0 | 441 | 3.5833 | 0.845 | 0.2403 | 1.2410 | 0.845 | 0.8382 | 0.1268 | 0.0506 | | No log | 64.0 | 448 | 3.6000 | 0.845 | 0.2451 | 1.2889 | 0.845 | 0.8282 | 0.1408 | 0.0526 | | No log | 65.0 | 455 | 3.6050 | 0.84 | 0.2497 | 1.2870 | 0.8400 | 0.8298 | 0.1401 | 0.0534 | | No log | 66.0 | 462 | 3.5950 | 0.86 | 0.2381 | 1.3004 | 0.8600 | 0.8491 | 0.1231 | 0.0511 | | No log | 67.0 | 469 | 3.6030 | 0.85 | 0.2435 | 1.2246 | 0.85 | 0.8374 | 0.1252 | 0.0517 | | No log | 68.0 | 476 | 3.6028 | 0.85 | 0.2433 | 1.2260 | 0.85 | 0.8404 | 0.1370 | 0.0513 | | No log | 69.0 | 483 | 3.6112 | 0.86 | 0.2438 | 1.2148 | 0.8600 | 0.8487 | 0.1362 | 0.0522 | | No log | 70.0 | 490 | 3.6147 | 0.85 | 0.2409 | 1.2230 | 0.85 | 0.8401 | 0.1258 | 0.0511 | | No log | 71.0 | 497 | 3.6231 | 0.845 | 0.2391 | 1.2277 | 0.845 | 0.8341 | 0.1332 | 0.0506 | | 3.5909 | 72.0 | 504 | 3.6298 | 0.845 | 0.2415 | 1.2897 | 0.845 | 0.8346 | 0.1288 | 0.0508 | | 3.5909 | 73.0 | 511 | 3.6384 | 0.85 | 0.2427 | 1.2893 | 0.85 | 0.8401 | 0.1366 | 0.0515 | | 3.5909 | 74.0 | 518 | 3.6364 | 0.845 | 0.2420 | 1.2224 | 0.845 | 0.8346 | 0.1219 | 0.0511 | | 3.5909 | 75.0 | 525 | 3.6471 | 0.845 | 0.2441 | 1.2252 | 0.845 | 0.8346 | 0.1322 | 0.0517 | | 3.5909 | 76.0 | 532 | 3.6469 | 0.85 | 0.2423 | 1.2259 | 0.85 | 0.8404 | 0.1300 | 0.0513 | | 3.5909 | 77.0 | 539 | 3.6493 | 0.85 | 0.2423 | 1.2253 | 0.85 | 0.8401 | 0.1248 | 0.0514 | | 3.5909 | 78.0 | 546 | 3.6534 | 0.85 | 0.2434 | 1.2273 | 0.85 | 0.8404 | 0.1271 | 0.0512 | | 3.5909 | 79.0 | 553 | 3.6588 | 0.845 | 0.2430 | 1.2254 | 0.845 | 0.8346 | 0.1307 | 0.0513 | | 3.5909 | 80.0 | 560 | 3.6636 | 0.845 | 0.2434 | 1.2249 | 0.845 | 0.8346 | 0.1259 | 0.0513 | | 3.5909 | 81.0 | 567 | 3.6670 | 0.845 | 0.2433 | 1.2253 | 0.845 | 0.8346 | 0.1356 | 0.0513 | | 3.5909 | 82.0 | 574 | 3.6689 | 0.845 | 0.2427 | 1.2256 | 0.845 | 0.8346 | 0.1365 | 0.0511 | | 3.5909 | 83.0 | 581 | 3.6724 | 0.845 | 0.2433 | 1.2278 | 0.845 | 0.8346 | 0.1315 | 0.0510 | | 3.5909 | 84.0 | 588 | 3.6768 | 0.85 | 0.2431 | 1.2260 | 0.85 | 0.8401 | 0.1374 | 0.0510 | | 3.5909 | 85.0 | 595 | 3.6782 | 0.85 | 0.2424 | 1.2265 | 0.85 | 0.8401 | 0.1340 | 0.0509 | | 3.5909 | 86.0 | 602 | 3.6817 | 0.85 | 0.2428 | 1.2261 | 0.85 | 0.8401 | 0.1332 | 0.0510 | | 3.5909 | 87.0 | 609 | 3.6822 | 0.85 | 0.2427 | 1.2266 | 0.85 | 0.8401 | 0.1330 | 0.0508 | | 3.5909 | 88.0 | 616 | 3.6835 | 0.85 | 0.2425 | 1.2259 | 0.85 | 0.8401 | 0.1328 | 0.0510 | | 3.5909 | 89.0 | 623 | 3.6854 | 0.85 | 0.2425 | 1.2260 | 0.85 | 0.8401 | 0.1328 | 0.0509 | | 3.5909 | 90.0 | 630 | 3.6874 | 0.85 | 0.2426 | 1.2259 | 0.85 | 0.8401 | 0.1327 | 0.0510 | | 3.5909 | 91.0 | 637 | 3.6891 | 0.85 | 0.2428 | 1.2264 | 0.85 | 0.8401 | 0.1327 | 0.0510 | | 3.5909 | 92.0 | 644 | 3.6903 | 0.85 | 0.2426 | 1.2265 | 0.85 | 0.8401 | 0.1328 | 0.0509 | | 3.5909 | 93.0 | 651 | 3.6913 | 0.85 | 0.2427 | 1.2264 | 0.85 | 0.8401 | 0.1327 | 0.0509 | | 3.5909 | 94.0 | 658 | 3.6922 | 0.85 | 0.2427 | 1.2265 | 0.85 | 0.8401 | 0.1326 | 0.0509 | | 3.5909 | 95.0 | 665 | 3.6930 | 0.85 | 0.2426 | 1.2262 | 0.85 | 0.8401 | 0.1326 | 0.0510 | | 3.5909 | 96.0 | 672 | 3.6936 | 0.85 | 0.2427 | 1.2266 | 0.85 | 0.8401 | 0.1327 | 0.0509 | | 3.5909 | 97.0 | 679 | 3.6940 | 0.85 | 0.2426 | 1.2264 | 0.85 | 0.8401 | 0.1325 | 0.0510 | | 3.5909 | 98.0 | 686 | 3.6946 | 0.85 | 0.2427 | 1.2265 | 0.85 | 0.8401 | 0.1326 | 0.0510 | | 3.5909 | 99.0 | 693 | 3.6948 | 0.85 | 0.2427 | 1.2266 | 0.85 | 0.8401 | 0.1325 | 0.0510 | | 3.5909 | 100.0 | 700 | 3.6948 | 0.85 | 0.2427 | 1.2265 | 0.85 | 0.8401 | 0.1325 | 0.0510 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH-40-30-30

# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH-40-30-30 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0418 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3299 | 1.0 | 187 | 0.2118 | 0.9218 | | 0.5922 | 2.0 | 374 | 0.3206 | 0.8629 | | 0.1763 | 3.0 | 561 | 0.2447 | 0.9127 | | 0.1351 | 4.0 | 749 | 0.1028 | 0.9564 | | 0.142 | 4.99 | 935 | 0.0418 | 0.9869 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "lung-benign_tissue", "lung_adenocarcinoma", "lung_squamous_cell_carcinoma" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone

# vit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_tNone_aNone_tNone_gNone This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0733 - Accuracy: 0.4825 - Brier Loss: 0.7791 - Nll: 2.6387 - F1 Micro: 0.4825 - F1 Macro: 0.4847 - Ece: 0.3427 - Aurc: 0.2765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.0917 | 0.0675 | 0.9375 | 7.4169 | 0.0675 | 0.0426 | 0.1087 | 0.9278 | | No log | 2.0 | 50 | 0.0830 | 0.07 | 0.9373 | 7.3255 | 0.07 | 0.0326 | 0.1058 | 0.9149 | | No log | 3.0 | 75 | 0.0823 | 0.08 | 0.9370 | 7.0476 | 0.08 | 0.0333 | 0.1129 | 0.9007 | | No log | 4.0 | 100 | 0.0820 | 0.0825 | 0.9368 | 6.9259 | 0.0825 | 0.0333 | 0.1113 | 0.8914 | | No log | 5.0 | 125 | 0.0817 | 0.095 | 0.9366 | 7.1920 | 0.095 | 0.0593 | 0.1189 | 0.8845 | | No log | 6.0 | 150 | 0.0814 | 0.105 | 0.9363 | 7.6541 | 0.1050 | 0.0654 | 0.1364 | 0.8354 | | No log | 7.0 | 175 | 0.0810 | 0.1075 | 0.9361 | 7.5199 | 0.1075 | 0.0628 | 0.1235 | 0.8559 | | No log | 8.0 | 200 | 0.0806 | 0.1025 | 0.9357 | 7.3552 | 0.1025 | 0.0532 | 0.1230 | 0.8697 | | No log | 9.0 | 225 | 0.0801 | 0.1125 | 0.9353 | 6.2436 | 0.1125 | 0.0580 | 0.1291 | 0.8258 | | No log | 10.0 | 250 | 0.0797 | 0.0975 | 0.9342 | 6.1811 | 0.0975 | 0.0486 | 0.1217 | 0.8531 | | No log | 11.0 | 275 | 0.0792 | 0.11 | 0.9331 | 5.1954 | 0.11 | 0.0558 | 0.1330 | 0.8172 | | No log | 12.0 | 300 | 0.0789 | 0.1225 | 0.9310 | 5.0567 | 0.1225 | 0.0536 | 0.1428 | 0.7847 | | No log | 13.0 | 325 | 0.0785 | 0.14 | 0.9283 | 4.2411 | 0.14 | 0.1085 | 0.1561 | 0.7098 | | No log | 14.0 | 350 | 0.0780 | 0.1925 | 0.9234 | 3.9402 | 0.1925 | 0.1627 | 0.1956 | 0.6553 | | No log | 15.0 | 375 | 0.0780 | 0.2275 | 0.9186 | 4.2282 | 0.2275 | 0.1806 | 0.2151 | 0.5919 | | No log | 16.0 | 400 | 0.0770 | 0.2925 | 0.9082 | 3.5789 | 0.2925 | 0.2357 | 0.2602 | 0.5043 | | No log | 17.0 | 425 | 0.0766 | 0.305 | 0.8993 | 3.6388 | 0.305 | 0.2465 | 0.2603 | 0.4771 | | No log | 18.0 | 450 | 0.0762 | 0.31 | 0.8916 | 3.2067 | 0.31 | 0.2602 | 0.2755 | 0.4341 | | No log | 19.0 | 475 | 0.0758 | 0.315 | 0.8861 | 3.1537 | 0.315 | 0.2659 | 0.2820 | 0.4282 | | 0.0818 | 20.0 | 500 | 0.0755 | 0.3475 | 0.8713 | 3.3614 | 0.3475 | 0.2869 | 0.2830 | 0.3966 | | 0.0818 | 21.0 | 525 | 0.0755 | 0.34 | 0.8627 | 3.3538 | 0.34 | 0.2728 | 0.2781 | 0.3934 | | 0.0818 | 22.0 | 550 | 0.0752 | 0.3575 | 0.8578 | 3.4181 | 0.3575 | 0.3052 | 0.2867 | 0.4037 | | 0.0818 | 23.0 | 575 | 0.0745 | 0.365 | 0.8486 | 2.7931 | 0.3650 | 0.3297 | 0.2908 | 0.3669 | | 0.0818 | 24.0 | 600 | 0.0743 | 0.395 | 0.8392 | 2.8800 | 0.395 | 0.3419 | 0.3054 | 0.3602 | | 0.0818 | 25.0 | 625 | 0.0741 | 0.3975 | 0.8382 | 2.8294 | 0.3975 | 0.3584 | 0.3049 | 0.3469 | | 0.0818 | 26.0 | 650 | 0.0739 | 0.4125 | 0.8308 | 2.9306 | 0.4125 | 0.3650 | 0.3179 | 0.3342 | | 0.0818 | 27.0 | 675 | 0.0740 | 0.425 | 0.8237 | 3.0954 | 0.425 | 0.3831 | 0.3069 | 0.3356 | | 0.0818 | 28.0 | 700 | 0.0739 | 0.425 | 0.8325 | 3.0230 | 0.425 | 0.3933 | 0.3154 | 0.3316 | | 0.0818 | 29.0 | 725 | 0.0735 | 0.445 | 0.8150 | 2.9001 | 0.445 | 0.4078 | 0.3320 | 0.3125 | | 0.0818 | 30.0 | 750 | 0.0734 | 0.44 | 0.8127 | 2.8272 | 0.44 | 0.4048 | 0.3196 | 0.3145 | | 0.0818 | 31.0 | 775 | 0.0733 | 0.45 | 0.8105 | 2.9716 | 0.45 | 0.4224 | 0.3214 | 0.3126 | | 0.0818 | 32.0 | 800 | 0.0732 | 0.4475 | 0.8059 | 2.7234 | 0.4475 | 0.4211 | 0.3166 | 0.3098 | | 0.0818 | 33.0 | 825 | 0.0734 | 0.45 | 0.8091 | 2.8963 | 0.45 | 0.4298 | 0.3174 | 0.3144 | | 0.0818 | 34.0 | 850 | 0.0732 | 0.45 | 0.8021 | 2.7268 | 0.45 | 0.4216 | 0.3203 | 0.3024 | | 0.0818 | 35.0 | 875 | 0.0732 | 0.465 | 0.8013 | 2.9374 | 0.465 | 0.4379 | 0.3417 | 0.2959 | | 0.0818 | 36.0 | 900 | 0.0732 | 0.4575 | 0.8039 | 2.9305 | 0.4575 | 0.4360 | 0.3166 | 0.3029 | | 0.0818 | 37.0 | 925 | 0.0733 | 0.4725 | 0.8017 | 2.7705 | 0.4725 | 0.4542 | 0.3348 | 0.2859 | | 0.0818 | 38.0 | 950 | 0.0732 | 0.4725 | 0.7963 | 2.8600 | 0.4725 | 0.4559 | 0.3432 | 0.2826 | | 0.0818 | 39.0 | 975 | 0.0731 | 0.4825 | 0.7979 | 2.7795 | 0.4825 | 0.4675 | 0.3361 | 0.2930 | | 0.0698 | 40.0 | 1000 | 0.0732 | 0.445 | 0.7962 | 2.8308 | 0.445 | 0.4366 | 0.3056 | 0.3058 | | 0.0698 | 41.0 | 1025 | 0.0732 | 0.4675 | 0.7914 | 2.7809 | 0.4675 | 0.4582 | 0.3173 | 0.2904 | | 0.0698 | 42.0 | 1050 | 0.0731 | 0.4625 | 0.7952 | 2.8907 | 0.4625 | 0.4644 | 0.3175 | 0.2910 | | 0.0698 | 43.0 | 1075 | 0.0733 | 0.4625 | 0.7955 | 2.7470 | 0.4625 | 0.4545 | 0.3107 | 0.2930 | | 0.0698 | 44.0 | 1100 | 0.0731 | 0.4725 | 0.7894 | 2.8684 | 0.4725 | 0.4640 | 0.3281 | 0.2883 | | 0.0698 | 45.0 | 1125 | 0.0731 | 0.475 | 0.7912 | 2.9091 | 0.4750 | 0.4594 | 0.3302 | 0.2830 | | 0.0698 | 46.0 | 1150 | 0.0731 | 0.47 | 0.7911 | 2.7282 | 0.47 | 0.4705 | 0.3344 | 0.2865 | | 0.0698 | 47.0 | 1175 | 0.0732 | 0.4775 | 0.7886 | 2.8402 | 0.4775 | 0.4737 | 0.3151 | 0.2846 | | 0.0698 | 48.0 | 1200 | 0.0731 | 0.4825 | 0.7850 | 2.7818 | 0.4825 | 0.4833 | 0.3422 | 0.2807 | | 0.0698 | 49.0 | 1225 | 0.0731 | 0.4625 | 0.7863 | 2.7929 | 0.4625 | 0.4621 | 0.3205 | 0.2828 | | 0.0698 | 50.0 | 1250 | 0.0732 | 0.4725 | 0.7875 | 2.8382 | 0.4725 | 0.4686 | 0.3364 | 0.2831 | | 0.0698 | 51.0 | 1275 | 0.0731 | 0.4725 | 0.7861 | 2.7543 | 0.4725 | 0.4661 | 0.3229 | 0.2838 | | 0.0698 | 52.0 | 1300 | 0.0731 | 0.475 | 0.7863 | 2.7936 | 0.4750 | 0.4771 | 0.3285 | 0.2801 | | 0.0698 | 53.0 | 1325 | 0.0731 | 0.4825 | 0.7846 | 2.8369 | 0.4825 | 0.4843 | 0.3369 | 0.2747 | | 0.0698 | 54.0 | 1350 | 0.0731 | 0.4725 | 0.7852 | 2.8102 | 0.4725 | 0.4747 | 0.3175 | 0.2869 | | 0.0698 | 55.0 | 1375 | 0.0731 | 0.475 | 0.7855 | 2.8205 | 0.4750 | 0.4801 | 0.3409 | 0.2802 | | 0.0698 | 56.0 | 1400 | 0.0731 | 0.48 | 0.7855 | 2.7926 | 0.48 | 0.4815 | 0.3403 | 0.2827 | | 0.0698 | 57.0 | 1425 | 0.0731 | 0.4825 | 0.7826 | 2.7536 | 0.4825 | 0.4815 | 0.3381 | 0.2788 | | 0.0698 | 58.0 | 1450 | 0.0731 | 0.4875 | 0.7851 | 2.8313 | 0.4875 | 0.4901 | 0.3395 | 0.2719 | | 0.0698 | 59.0 | 1475 | 0.0731 | 0.4875 | 0.7838 | 2.7423 | 0.4875 | 0.4905 | 0.3410 | 0.2735 | | 0.0654 | 60.0 | 1500 | 0.0731 | 0.48 | 0.7849 | 2.7730 | 0.48 | 0.4818 | 0.3344 | 0.2807 | | 0.0654 | 61.0 | 1525 | 0.0732 | 0.48 | 0.7816 | 2.7517 | 0.48 | 0.4813 | 0.3370 | 0.2762 | | 0.0654 | 62.0 | 1550 | 0.0731 | 0.4775 | 0.7833 | 2.8441 | 0.4775 | 0.4804 | 0.3314 | 0.2767 | | 0.0654 | 63.0 | 1575 | 0.0731 | 0.4775 | 0.7835 | 2.7252 | 0.4775 | 0.4811 | 0.3354 | 0.2769 | | 0.0654 | 64.0 | 1600 | 0.0732 | 0.4925 | 0.7819 | 2.7991 | 0.4925 | 0.4958 | 0.3371 | 0.2726 | | 0.0654 | 65.0 | 1625 | 0.0731 | 0.4825 | 0.7806 | 2.6719 | 0.4825 | 0.4850 | 0.3190 | 0.2752 | | 0.0654 | 66.0 | 1650 | 0.0732 | 0.48 | 0.7817 | 2.7669 | 0.48 | 0.4828 | 0.3336 | 0.2791 | | 0.0654 | 67.0 | 1675 | 0.0731 | 0.4775 | 0.7813 | 2.6678 | 0.4775 | 0.4822 | 0.3304 | 0.2750 | | 0.0654 | 68.0 | 1700 | 0.0732 | 0.4875 | 0.7829 | 2.7529 | 0.4875 | 0.4919 | 0.3381 | 0.2756 | | 0.0654 | 69.0 | 1725 | 0.0731 | 0.4825 | 0.7795 | 2.7291 | 0.4825 | 0.4839 | 0.3418 | 0.2737 | | 0.0654 | 70.0 | 1750 | 0.0732 | 0.4875 | 0.7827 | 2.7613 | 0.4875 | 0.4909 | 0.3308 | 0.2747 | | 0.0654 | 71.0 | 1775 | 0.0732 | 0.4825 | 0.7816 | 2.7348 | 0.4825 | 0.4863 | 0.3306 | 0.2733 | | 0.0654 | 72.0 | 1800 | 0.0732 | 0.4825 | 0.7813 | 2.6920 | 0.4825 | 0.4863 | 0.3268 | 0.2724 | | 0.0654 | 73.0 | 1825 | 0.0731 | 0.485 | 0.7809 | 2.6890 | 0.485 | 0.4872 | 0.3307 | 0.2741 | | 0.0654 | 74.0 | 1850 | 0.0732 | 0.4825 | 0.7810 | 2.6668 | 0.4825 | 0.4854 | 0.3245 | 0.2758 | | 0.0654 | 75.0 | 1875 | 0.0732 | 0.48 | 0.7814 | 2.7337 | 0.48 | 0.4836 | 0.3232 | 0.2767 | | 0.0654 | 76.0 | 1900 | 0.0731 | 0.49 | 0.7802 | 2.7219 | 0.49 | 0.4900 | 0.3290 | 0.2727 | | 0.0654 | 77.0 | 1925 | 0.0732 | 0.48 | 0.7804 | 2.7187 | 0.48 | 0.4821 | 0.3223 | 0.2759 | | 0.0654 | 78.0 | 1950 | 0.0732 | 0.485 | 0.7811 | 2.6797 | 0.485 | 0.4884 | 0.3343 | 0.2754 | | 0.0654 | 79.0 | 1975 | 0.0731 | 0.48 | 0.7784 | 2.6604 | 0.48 | 0.4816 | 0.3345 | 0.2751 | | 0.0641 | 80.0 | 2000 | 0.0732 | 0.485 | 0.7797 | 2.6380 | 0.485 | 0.4876 | 0.3317 | 0.2755 | | 0.0641 | 81.0 | 2025 | 0.0732 | 0.4775 | 0.7805 | 2.6934 | 0.4775 | 0.4808 | 0.3225 | 0.2758 | | 0.0641 | 82.0 | 2050 | 0.0732 | 0.4825 | 0.7802 | 2.7315 | 0.4825 | 0.4851 | 0.3364 | 0.2781 | | 0.0641 | 83.0 | 2075 | 0.0732 | 0.4875 | 0.7800 | 2.7011 | 0.4875 | 0.4899 | 0.3222 | 0.2736 | | 0.0641 | 84.0 | 2100 | 0.0732 | 0.4825 | 0.7796 | 2.6672 | 0.4825 | 0.4845 | 0.3203 | 0.2772 | | 0.0641 | 85.0 | 2125 | 0.0732 | 0.4825 | 0.7798 | 2.6956 | 0.4825 | 0.4833 | 0.3373 | 0.2757 | | 0.0641 | 86.0 | 2150 | 0.0732 | 0.48 | 0.7797 | 2.6349 | 0.48 | 0.4823 | 0.3265 | 0.2774 | | 0.0641 | 87.0 | 2175 | 0.0732 | 0.49 | 0.7800 | 2.7238 | 0.49 | 0.4921 | 0.3407 | 0.2755 | | 0.0641 | 88.0 | 2200 | 0.0732 | 0.4775 | 0.7800 | 2.6423 | 0.4775 | 0.4804 | 0.3163 | 0.2785 | | 0.0641 | 89.0 | 2225 | 0.0732 | 0.485 | 0.7793 | 2.6734 | 0.485 | 0.4881 | 0.3310 | 0.2760 | | 0.0641 | 90.0 | 2250 | 0.0732 | 0.4825 | 0.7796 | 2.6582 | 0.4825 | 0.4858 | 0.3232 | 0.2774 | | 0.0641 | 91.0 | 2275 | 0.0732 | 0.485 | 0.7790 | 2.6705 | 0.485 | 0.4882 | 0.3277 | 0.2760 | | 0.0641 | 92.0 | 2300 | 0.0732 | 0.49 | 0.7795 | 2.6465 | 0.49 | 0.4943 | 0.3513 | 0.2767 | | 0.0641 | 93.0 | 2325 | 0.0732 | 0.4825 | 0.7791 | 2.6495 | 0.4825 | 0.4852 | 0.3414 | 0.2763 | | 0.0641 | 94.0 | 2350 | 0.0732 | 0.49 | 0.7793 | 2.6402 | 0.49 | 0.4933 | 0.3458 | 0.2760 | | 0.0641 | 95.0 | 2375 | 0.0732 | 0.4875 | 0.7792 | 2.6448 | 0.4875 | 0.4898 | 0.3420 | 0.2763 | | 0.0641 | 96.0 | 2400 | 0.0732 | 0.4825 | 0.7792 | 2.6402 | 0.4825 | 0.4847 | 0.3346 | 0.2766 | | 0.0641 | 97.0 | 2425 | 0.0733 | 0.485 | 0.7793 | 2.6397 | 0.485 | 0.4873 | 0.3407 | 0.2768 | | 0.0641 | 98.0 | 2450 | 0.0732 | 0.4825 | 0.7790 | 2.6388 | 0.4825 | 0.4847 | 0.3374 | 0.2763 | | 0.0641 | 99.0 | 2475 | 0.0733 | 0.4825 | 0.7792 | 2.6390 | 0.4825 | 0.4847 | 0.3393 | 0.2767 | | 0.0637 | 100.0 | 2500 | 0.0733 | 0.4825 | 0.7791 | 2.6387 | 0.4825 | 0.4847 | 0.3427 | 0.2765 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5

# vit-small_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2014 - Accuracy: 0.635 - Brier Loss: 0.5252 - Nll: 2.1069 - F1 Micro: 0.635 - F1 Macro: 0.6363 - Ece: 0.1836 - Aurc: 0.1520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 7.0792 | 0.0825 | 0.9657 | 10.6330 | 0.0825 | 0.0736 | 0.1618 | 0.9054 | | No log | 2.0 | 14 | 6.4691 | 0.07 | 0.9514 | 8.1662 | 0.07 | 0.0643 | 0.1779 | 0.9461 | | No log | 3.0 | 21 | 5.8986 | 0.2975 | 0.8596 | 5.3260 | 0.2975 | 0.2944 | 0.2211 | 0.5304 | | No log | 4.0 | 28 | 5.5468 | 0.3925 | 0.7531 | 3.5791 | 0.3925 | 0.3860 | 0.2145 | 0.3645 | | No log | 5.0 | 35 | 5.2678 | 0.46 | 0.6755 | 3.2144 | 0.46 | 0.4517 | 0.1901 | 0.2901 | | No log | 6.0 | 42 | 5.1237 | 0.5075 | 0.6334 | 2.9369 | 0.5075 | 0.4888 | 0.1985 | 0.2550 | | No log | 7.0 | 49 | 5.1530 | 0.5125 | 0.6131 | 3.1196 | 0.5125 | 0.4843 | 0.1634 | 0.2417 | | No log | 8.0 | 56 | 5.0462 | 0.545 | 0.5898 | 2.7596 | 0.545 | 0.5376 | 0.1792 | 0.2232 | | No log | 9.0 | 63 | 5.1437 | 0.565 | 0.5759 | 2.9426 | 0.565 | 0.5660 | 0.1715 | 0.2208 | | No log | 10.0 | 70 | 4.9658 | 0.605 | 0.5382 | 2.4096 | 0.605 | 0.5945 | 0.1828 | 0.1734 | | No log | 11.0 | 77 | 5.1189 | 0.57 | 0.5592 | 2.5892 | 0.57 | 0.5677 | 0.1381 | 0.1952 | | No log | 12.0 | 84 | 5.2082 | 0.54 | 0.5774 | 2.7250 | 0.54 | 0.5323 | 0.1578 | 0.2144 | | No log | 13.0 | 91 | 4.9674 | 0.5775 | 0.5365 | 2.5469 | 0.5775 | 0.5654 | 0.1603 | 0.1824 | | No log | 14.0 | 98 | 5.0007 | 0.5875 | 0.5299 | 2.6635 | 0.5875 | 0.5778 | 0.1567 | 0.1701 | | No log | 15.0 | 105 | 4.9925 | 0.585 | 0.5417 | 2.6416 | 0.585 | 0.5760 | 0.1731 | 0.1896 | | No log | 16.0 | 112 | 4.8314 | 0.6425 | 0.4939 | 2.4601 | 0.6425 | 0.6444 | 0.1492 | 0.1506 | | No log | 17.0 | 119 | 4.8729 | 0.6075 | 0.5197 | 2.4297 | 0.6075 | 0.6054 | 0.1511 | 0.1702 | | No log | 18.0 | 126 | 4.8960 | 0.61 | 0.5085 | 2.2405 | 0.61 | 0.6197 | 0.1664 | 0.1657 | | No log | 19.0 | 133 | 4.8227 | 0.62 | 0.5032 | 2.4320 | 0.62 | 0.6177 | 0.1399 | 0.1615 | | No log | 20.0 | 140 | 4.9420 | 0.61 | 0.5160 | 2.3051 | 0.61 | 0.6119 | 0.1460 | 0.1722 | | No log | 21.0 | 147 | 4.8779 | 0.6125 | 0.5132 | 2.3564 | 0.6125 | 0.6080 | 0.1549 | 0.1639 | | No log | 22.0 | 154 | 4.9454 | 0.6125 | 0.5261 | 2.4064 | 0.6125 | 0.6155 | 0.1792 | 0.1733 | | No log | 23.0 | 161 | 4.8659 | 0.5925 | 0.5018 | 2.5961 | 0.5925 | 0.5897 | 0.1537 | 0.1607 | | No log | 24.0 | 168 | 4.8150 | 0.605 | 0.4996 | 2.2624 | 0.605 | 0.6050 | 0.1525 | 0.1588 | | No log | 25.0 | 175 | 4.8303 | 0.6175 | 0.4970 | 2.1999 | 0.6175 | 0.6204 | 0.1284 | 0.1515 | | No log | 26.0 | 182 | 4.8442 | 0.6225 | 0.5060 | 2.2842 | 0.6225 | 0.6251 | 0.1639 | 0.1614 | | No log | 27.0 | 189 | 4.8260 | 0.63 | 0.4953 | 2.2666 | 0.63 | 0.6345 | 0.1638 | 0.1531 | | No log | 28.0 | 196 | 4.8421 | 0.6375 | 0.4979 | 2.3173 | 0.6375 | 0.6344 | 0.1430 | 0.1525 | | No log | 29.0 | 203 | 4.9011 | 0.62 | 0.5066 | 2.2663 | 0.62 | 0.6221 | 0.1596 | 0.1602 | | No log | 30.0 | 210 | 4.8689 | 0.62 | 0.4994 | 2.1498 | 0.62 | 0.6260 | 0.1581 | 0.1567 | | No log | 31.0 | 217 | 4.8681 | 0.6075 | 0.5143 | 2.0979 | 0.6075 | 0.6080 | 0.1673 | 0.1641 | | No log | 32.0 | 224 | 4.8489 | 0.6 | 0.5074 | 2.1485 | 0.6 | 0.5913 | 0.1579 | 0.1613 | | No log | 33.0 | 231 | 4.8669 | 0.63 | 0.5037 | 2.3142 | 0.63 | 0.6272 | 0.1512 | 0.1519 | | No log | 34.0 | 238 | 4.8382 | 0.6075 | 0.5005 | 2.1817 | 0.6075 | 0.6038 | 0.1683 | 0.1552 | | No log | 35.0 | 245 | 4.8406 | 0.61 | 0.5012 | 2.2132 | 0.61 | 0.6019 | 0.1443 | 0.1518 | | No log | 36.0 | 252 | 4.8241 | 0.6275 | 0.5040 | 2.2466 | 0.6275 | 0.6182 | 0.1511 | 0.1563 | | No log | 37.0 | 259 | 4.8359 | 0.6225 | 0.4993 | 2.1727 | 0.6225 | 0.6201 | 0.1665 | 0.1570 | | No log | 38.0 | 266 | 4.8812 | 0.6025 | 0.5155 | 2.2712 | 0.6025 | 0.5990 | 0.1634 | 0.1649 | | No log | 39.0 | 273 | 4.8672 | 0.61 | 0.5075 | 2.1626 | 0.61 | 0.6073 | 0.1603 | 0.1592 | | No log | 40.0 | 280 | 4.9083 | 0.6175 | 0.5098 | 2.1507 | 0.6175 | 0.6204 | 0.1524 | 0.1594 | | No log | 41.0 | 287 | 4.8942 | 0.61 | 0.5132 | 2.2443 | 0.61 | 0.6070 | 0.1574 | 0.1618 | | No log | 42.0 | 294 | 4.9435 | 0.62 | 0.5177 | 2.1770 | 0.62 | 0.6186 | 0.1567 | 0.1664 | | No log | 43.0 | 301 | 4.8836 | 0.63 | 0.5089 | 2.1922 | 0.63 | 0.6300 | 0.1612 | 0.1553 | | No log | 44.0 | 308 | 4.9806 | 0.6225 | 0.5205 | 2.1855 | 0.6225 | 0.6213 | 0.1715 | 0.1631 | | No log | 45.0 | 315 | 4.9314 | 0.6225 | 0.5185 | 2.1783 | 0.6225 | 0.6182 | 0.1743 | 0.1631 | | No log | 46.0 | 322 | 4.8615 | 0.6275 | 0.4984 | 2.2407 | 0.6275 | 0.6259 | 0.1529 | 0.1497 | | No log | 47.0 | 329 | 4.8550 | 0.625 | 0.4985 | 2.1229 | 0.625 | 0.6261 | 0.1517 | 0.1531 | | No log | 48.0 | 336 | 4.9218 | 0.6125 | 0.5113 | 2.2200 | 0.6125 | 0.6114 | 0.1627 | 0.1588 | | No log | 49.0 | 343 | 4.9067 | 0.63 | 0.5102 | 2.2177 | 0.63 | 0.6299 | 0.1534 | 0.1567 | | No log | 50.0 | 350 | 4.9040 | 0.6125 | 0.5110 | 2.1105 | 0.6125 | 0.6136 | 0.1731 | 0.1559 | | No log | 51.0 | 357 | 4.9557 | 0.615 | 0.5180 | 2.2031 | 0.615 | 0.6157 | 0.1726 | 0.1602 | | No log | 52.0 | 364 | 4.9409 | 0.61 | 0.5195 | 2.2616 | 0.61 | 0.6079 | 0.1627 | 0.1618 | | No log | 53.0 | 371 | 4.9290 | 0.6225 | 0.5125 | 2.1352 | 0.6225 | 0.6227 | 0.1873 | 0.1549 | | No log | 54.0 | 378 | 4.9297 | 0.6225 | 0.5075 | 2.1558 | 0.6225 | 0.6216 | 0.1724 | 0.1530 | | No log | 55.0 | 385 | 4.9192 | 0.6225 | 0.5131 | 2.1572 | 0.6225 | 0.6220 | 0.1655 | 0.1578 | | No log | 56.0 | 392 | 4.9760 | 0.61 | 0.5203 | 2.1227 | 0.61 | 0.6092 | 0.1852 | 0.1594 | | No log | 57.0 | 399 | 4.9860 | 0.6125 | 0.5208 | 2.1996 | 0.6125 | 0.6154 | 0.1812 | 0.1608 | | No log | 58.0 | 406 | 4.9418 | 0.62 | 0.5176 | 2.1034 | 0.62 | 0.6220 | 0.1635 | 0.1549 | | No log | 59.0 | 413 | 4.9462 | 0.62 | 0.5143 | 2.1095 | 0.62 | 0.6221 | 0.1855 | 0.1553 | | No log | 60.0 | 420 | 4.9447 | 0.6175 | 0.5142 | 2.0731 | 0.6175 | 0.6180 | 0.1571 | 0.1533 | | No log | 61.0 | 427 | 4.9677 | 0.63 | 0.5091 | 2.1491 | 0.63 | 0.6346 | 0.1693 | 0.1498 | | No log | 62.0 | 434 | 4.9567 | 0.62 | 0.5089 | 2.1222 | 0.62 | 0.6242 | 0.1609 | 0.1546 | | No log | 63.0 | 441 | 4.9378 | 0.6325 | 0.5030 | 2.1787 | 0.6325 | 0.6310 | 0.1558 | 0.1471 | | No log | 64.0 | 448 | 4.9764 | 0.6175 | 0.5154 | 2.0751 | 0.6175 | 0.6192 | 0.1835 | 0.1549 | | No log | 65.0 | 455 | 4.9520 | 0.6325 | 0.5069 | 2.1067 | 0.6325 | 0.6352 | 0.1670 | 0.1499 | | No log | 66.0 | 462 | 4.9649 | 0.6375 | 0.5109 | 2.1016 | 0.6375 | 0.6361 | 0.1665 | 0.1506 | | No log | 67.0 | 469 | 5.0023 | 0.635 | 0.5174 | 2.2166 | 0.635 | 0.6350 | 0.1653 | 0.1543 | | No log | 68.0 | 476 | 5.0084 | 0.63 | 0.5187 | 2.1238 | 0.63 | 0.6302 | 0.1674 | 0.1535 | | No log | 69.0 | 483 | 4.9875 | 0.6325 | 0.5096 | 2.1744 | 0.6325 | 0.6345 | 0.1822 | 0.1510 | | No log | 70.0 | 490 | 5.0129 | 0.6325 | 0.5151 | 2.1042 | 0.6325 | 0.6335 | 0.1691 | 0.1535 | | No log | 71.0 | 497 | 5.0389 | 0.6275 | 0.5201 | 2.0941 | 0.6275 | 0.6283 | 0.1765 | 0.1550 | | 3.4121 | 72.0 | 504 | 5.0288 | 0.6325 | 0.5168 | 2.1299 | 0.6325 | 0.6314 | 0.1802 | 0.1529 | | 3.4121 | 73.0 | 511 | 5.0181 | 0.625 | 0.5121 | 2.1690 | 0.625 | 0.6236 | 0.1683 | 0.1511 | | 3.4121 | 74.0 | 518 | 5.0422 | 0.625 | 0.5139 | 2.1323 | 0.625 | 0.6264 | 0.1873 | 0.1517 | | 3.4121 | 75.0 | 525 | 5.0557 | 0.6325 | 0.5177 | 2.1695 | 0.6325 | 0.6342 | 0.1677 | 0.1503 | | 3.4121 | 76.0 | 532 | 5.0440 | 0.6375 | 0.5113 | 2.1384 | 0.6375 | 0.6384 | 0.1714 | 0.1489 | | 3.4121 | 77.0 | 539 | 5.0710 | 0.6375 | 0.5163 | 2.1017 | 0.6375 | 0.6397 | 0.1785 | 0.1508 | | 3.4121 | 78.0 | 546 | 5.1024 | 0.63 | 0.5218 | 2.0905 | 0.63 | 0.6280 | 0.1724 | 0.1538 | | 3.4121 | 79.0 | 553 | 5.0906 | 0.635 | 0.5186 | 2.1293 | 0.635 | 0.6358 | 0.1908 | 0.1509 | | 3.4121 | 80.0 | 560 | 5.1027 | 0.63 | 0.5206 | 2.1292 | 0.63 | 0.6299 | 0.1850 | 0.1525 | | 3.4121 | 81.0 | 567 | 5.1063 | 0.64 | 0.5161 | 2.1620 | 0.64 | 0.6404 | 0.1754 | 0.1489 | | 3.4121 | 82.0 | 574 | 5.1267 | 0.64 | 0.5207 | 2.1291 | 0.64 | 0.6400 | 0.1849 | 0.1516 | | 3.4121 | 83.0 | 581 | 5.1332 | 0.63 | 0.5224 | 2.1338 | 0.63 | 0.6322 | 0.1750 | 0.1522 | | 3.4121 | 84.0 | 588 | 5.1408 | 0.6325 | 0.5233 | 2.1333 | 0.6325 | 0.6334 | 0.1797 | 0.1522 | | 3.4121 | 85.0 | 595 | 5.1510 | 0.63 | 0.5224 | 2.1635 | 0.63 | 0.6301 | 0.1755 | 0.1522 | | 3.4121 | 86.0 | 602 | 5.1536 | 0.6375 | 0.5215 | 2.1628 | 0.6375 | 0.6382 | 0.1683 | 0.1511 | | 3.4121 | 87.0 | 609 | 5.1580 | 0.6325 | 0.5228 | 2.1348 | 0.6325 | 0.6328 | 0.1779 | 0.1523 | | 3.4121 | 88.0 | 616 | 5.1701 | 0.64 | 0.5235 | 2.1352 | 0.64 | 0.6417 | 0.1818 | 0.1515 | | 3.4121 | 89.0 | 623 | 5.1734 | 0.6375 | 0.5235 | 2.1354 | 0.6375 | 0.6385 | 0.1775 | 0.1515 | | 3.4121 | 90.0 | 630 | 5.1779 | 0.635 | 0.5243 | 2.1334 | 0.635 | 0.6360 | 0.1842 | 0.1519 | | 3.4121 | 91.0 | 637 | 5.1834 | 0.635 | 0.5241 | 2.1344 | 0.635 | 0.6363 | 0.1813 | 0.1521 | | 3.4121 | 92.0 | 644 | 5.1877 | 0.6375 | 0.5247 | 2.1356 | 0.6375 | 0.6385 | 0.1871 | 0.1517 | | 3.4121 | 93.0 | 651 | 5.1906 | 0.635 | 0.5245 | 2.1389 | 0.635 | 0.6360 | 0.1888 | 0.1520 | | 3.4121 | 94.0 | 658 | 5.1935 | 0.635 | 0.5248 | 2.1083 | 0.635 | 0.6363 | 0.1831 | 0.1521 | | 3.4121 | 95.0 | 665 | 5.1955 | 0.635 | 0.5249 | 2.1098 | 0.635 | 0.6363 | 0.1795 | 0.1521 | | 3.4121 | 96.0 | 672 | 5.1978 | 0.635 | 0.5250 | 2.1079 | 0.635 | 0.6363 | 0.1820 | 0.1521 | | 3.4121 | 97.0 | 679 | 5.1995 | 0.635 | 0.5251 | 2.1073 | 0.635 | 0.6363 | 0.1834 | 0.1521 | | 3.4121 | 98.0 | 686 | 5.2004 | 0.635 | 0.5251 | 2.1072 | 0.635 | 0.6360 | 0.1834 | 0.1520 | | 3.4121 | 99.0 | 693 | 5.2012 | 0.635 | 0.5252 | 2.1071 | 0.635 | 0.6360 | 0.1836 | 0.1520 | | 3.4121 | 100.0 | 700 | 5.2014 | 0.635 | 0.5252 | 2.1069 | 0.635 | 0.6363 | 0.1836 | 0.1520 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM1

# vit-base-patch16-224-Trial006-007-008-YEL_STEM1 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0457 - Accuracy: 0.9912 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7163 | 0.89 | 4 | 0.7036 | 0.5175 | | 0.7183 | 2.0 | 9 | 0.6982 | 0.5439 | | 0.6561 | 2.89 | 13 | 0.5914 | 0.6667 | | 0.5799 | 4.0 | 18 | 0.6411 | 0.5526 | | 0.5731 | 4.89 | 22 | 0.4833 | 0.8158 | | 0.5162 | 6.0 | 27 | 0.4483 | 0.7807 | | 0.4738 | 6.89 | 31 | 0.3961 | 0.8596 | | 0.3283 | 8.0 | 36 | 0.2897 | 0.8772 | | 0.3365 | 8.89 | 40 | 0.2774 | 0.8860 | | 0.5987 | 10.0 | 45 | 0.2617 | 0.8947 | | 0.3633 | 10.89 | 49 | 0.2661 | 0.8772 | | 0.3342 | 12.0 | 54 | 0.2269 | 0.9211 | | 0.3406 | 12.89 | 58 | 0.1903 | 0.9211 | | 0.2331 | 14.0 | 63 | 0.2075 | 0.9123 | | 0.2494 | 14.89 | 67 | 0.1345 | 0.9474 | | 0.3143 | 16.0 | 72 | 0.1508 | 0.9298 | | 0.2622 | 16.89 | 76 | 0.1267 | 0.9298 | | 0.2894 | 18.0 | 81 | 0.0921 | 0.9737 | | 0.2105 | 18.89 | 85 | 0.1058 | 0.9298 | | 0.2011 | 20.0 | 90 | 0.1021 | 0.9561 | | 0.1977 | 20.89 | 94 | 0.1122 | 0.9561 | | 0.2685 | 22.0 | 99 | 0.1656 | 0.9298 | | 0.2394 | 22.89 | 103 | 0.1197 | 0.9561 | | 0.2394 | 24.0 | 108 | 0.1461 | 0.9211 | | 0.1523 | 24.89 | 112 | 0.0969 | 0.9649 | | 0.2231 | 26.0 | 117 | 0.0827 | 0.9649 | | 0.1887 | 26.89 | 121 | 0.0979 | 0.9561 | | 0.1656 | 28.0 | 126 | 0.0907 | 0.9386 | | 0.1824 | 28.89 | 130 | 0.1009 | 0.9561 | | 0.2443 | 30.0 | 135 | 0.1249 | 0.9474 | | 0.2082 | 30.89 | 139 | 0.0755 | 0.9649 | | 0.1276 | 32.0 | 144 | 0.0531 | 0.9825 | | 0.1857 | 32.89 | 148 | 0.0707 | 0.9561 | | 0.1676 | 34.0 | 153 | 0.0570 | 0.9825 | | 0.1992 | 34.89 | 157 | 0.0591 | 0.9737 | | 0.2283 | 36.0 | 162 | 0.0575 | 0.9737 | | 0.2125 | 36.89 | 166 | 0.0519 | 0.9825 | | 0.1915 | 38.0 | 171 | 0.0457 | 0.9912 | | 0.1969 | 38.89 | 175 | 0.0644 | 0.9561 | | 0.1522 | 40.0 | 180 | 0.0771 | 0.9561 | | 0.1037 | 40.89 | 184 | 0.0557 | 0.9561 | | 0.2348 | 42.0 | 189 | 0.0470 | 0.9737 | | 0.1674 | 42.89 | 193 | 0.0469 | 0.9737 | | 0.0874 | 44.0 | 198 | 0.0483 | 0.9737 | | 0.1605 | 44.44 | 200 | 0.0485 | 0.9649 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

[ "plant2", "plant3" ]

sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM2

# vit-base-patch16-224-Trial006-007-008-YEL_STEM2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0188 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7014 | 0.94 | 4 | 0.6789 | 0.5872 | | 0.6242 | 1.88 | 8 | 0.6182 | 0.6147 | | 0.5903 | 2.82 | 12 | 0.5147 | 0.8165 | | 0.516 | 4.0 | 17 | 0.4137 | 0.8440 | | 0.4612 | 4.94 | 21 | 0.3127 | 0.8899 | | 0.3615 | 5.88 | 25 | 0.2448 | 0.8899 | | 0.2899 | 6.82 | 29 | 0.2502 | 0.9083 | | 0.3271 | 8.0 | 34 | 0.2268 | 0.8899 | | 0.3765 | 8.94 | 38 | 0.1419 | 0.9266 | | 0.2954 | 9.88 | 42 | 0.1233 | 0.9450 | | 0.2187 | 10.82 | 46 | 0.1541 | 0.9358 | | 0.3391 | 12.0 | 51 | 0.1575 | 0.9266 | | 0.2027 | 12.94 | 55 | 0.0838 | 0.9908 | | 0.2894 | 13.88 | 59 | 0.0852 | 0.9633 | | 0.1972 | 14.82 | 63 | 0.0648 | 0.9908 | | 0.1533 | 16.0 | 68 | 0.0884 | 0.9908 | | 0.1588 | 16.94 | 72 | 0.0951 | 0.9633 | | 0.1746 | 17.88 | 76 | 0.0793 | 0.9725 | | 0.2386 | 18.82 | 80 | 0.1204 | 0.9633 | | 0.1226 | 20.0 | 85 | 0.0502 | 0.9633 | | 0.1901 | 20.94 | 89 | 0.0188 | 1.0 | | 0.1305 | 21.88 | 93 | 0.0244 | 0.9908 | | 0.1695 | 22.82 | 97 | 0.0186 | 1.0 | | 0.2682 | 24.0 | 102 | 0.0460 | 0.9817 | | 0.1956 | 24.94 | 106 | 0.0328 | 0.9817 | | 0.1651 | 25.88 | 110 | 0.0493 | 0.9817 | | 0.1841 | 26.82 | 114 | 0.0168 | 1.0 | | 0.1897 | 28.0 | 119 | 0.0139 | 1.0 | | 0.2088 | 28.94 | 123 | 0.0195 | 1.0 | | 0.1386 | 29.88 | 127 | 0.0483 | 0.9817 | | 0.1544 | 30.82 | 131 | 0.0258 | 0.9817 | | 0.1293 | 32.0 | 136 | 0.0461 | 0.9725 | | 0.1696 | 32.94 | 140 | 0.0207 | 0.9908 | | 0.1793 | 33.88 | 144 | 0.0182 | 1.0 | | 0.1545 | 34.82 | 148 | 0.0361 | 0.9817 | | 0.141 | 36.0 | 153 | 0.0455 | 0.9817 | | 0.138 | 36.94 | 157 | 0.0175 | 0.9908 | | 0.1382 | 37.88 | 161 | 0.0359 | 0.9817 | | 0.163 | 38.82 | 165 | 0.0463 | 0.9817 | | 0.1374 | 40.0 | 170 | 0.0153 | 0.9908 | | 0.1993 | 40.94 | 174 | 0.0116 | 1.0 | | 0.1471 | 41.88 | 178 | 0.0199 | 0.9817 | | 0.1414 | 42.82 | 182 | 0.0330 | 0.9817 | | 0.1494 | 44.0 | 187 | 0.0228 | 0.9817 | | 0.1367 | 44.94 | 191 | 0.0174 | 0.9817 | | 0.1529 | 45.88 | 195 | 0.0162 | 0.9817 | | 0.1365 | 46.82 | 199 | 0.0153 | 0.9908 | | 0.1301 | 47.06 | 200 | 0.0152 | 0.9908 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

[ "plant2", "plant3" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5

# vit-tiny_rvl_cdip_100_examples_per_class_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4262 - Accuracy: 0.535 - Brier Loss: 0.6080 - Nll: 2.4569 - F1 Micro: 0.535 - F1 Macro: 0.5345 - Ece: 0.2120 - Aurc: 0.2105 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 8.7328 | 0.0375 | 1.0712 | 7.1959 | 0.0375 | 0.0284 | 0.2770 | 0.9564 | | No log | 2.0 | 14 | 6.6754 | 0.0725 | 0.9650 | 6.2033 | 0.0725 | 0.0442 | 0.1890 | 0.9239 | | No log | 3.0 | 21 | 6.2399 | 0.1525 | 0.9213 | 5.6927 | 0.1525 | 0.1339 | 0.1647 | 0.8105 | | No log | 4.0 | 28 | 5.9557 | 0.2125 | 0.8771 | 4.2773 | 0.2125 | 0.1952 | 0.1637 | 0.6347 | | No log | 5.0 | 35 | 5.5957 | 0.3275 | 0.7962 | 3.1599 | 0.3275 | 0.3087 | 0.1928 | 0.4595 | | No log | 6.0 | 42 | 5.4573 | 0.4075 | 0.7283 | 3.0860 | 0.4075 | 0.3732 | 0.1803 | 0.3610 | | No log | 7.0 | 49 | 5.3445 | 0.43 | 0.7150 | 2.9840 | 0.4300 | 0.4112 | 0.2071 | 0.3498 | | No log | 8.0 | 56 | 5.2986 | 0.445 | 0.6860 | 2.9281 | 0.445 | 0.4327 | 0.1891 | 0.3159 | | No log | 9.0 | 63 | 5.2567 | 0.465 | 0.6608 | 3.0783 | 0.465 | 0.4494 | 0.1835 | 0.2885 | | No log | 10.0 | 70 | 5.4317 | 0.4425 | 0.6752 | 2.9067 | 0.4425 | 0.4477 | 0.1836 | 0.3209 | | No log | 11.0 | 77 | 5.2073 | 0.4825 | 0.6342 | 2.7960 | 0.4825 | 0.4664 | 0.1731 | 0.2708 | | No log | 12.0 | 84 | 5.2094 | 0.51 | 0.6150 | 2.8886 | 0.51 | 0.4767 | 0.1463 | 0.2459 | | No log | 13.0 | 91 | 5.2379 | 0.4975 | 0.6135 | 2.7446 | 0.4975 | 0.4819 | 0.1702 | 0.2436 | | No log | 14.0 | 98 | 5.2716 | 0.505 | 0.6123 | 2.7888 | 0.505 | 0.4962 | 0.1595 | 0.2496 | | No log | 15.0 | 105 | 5.2410 | 0.5125 | 0.6090 | 2.6474 | 0.5125 | 0.5144 | 0.1659 | 0.2451 | | No log | 16.0 | 112 | 5.2764 | 0.54 | 0.6063 | 2.7700 | 0.54 | 0.5316 | 0.1634 | 0.2383 | | No log | 17.0 | 119 | 5.2652 | 0.5275 | 0.6027 | 2.7934 | 0.5275 | 0.5171 | 0.1800 | 0.2326 | | No log | 18.0 | 126 | 5.2145 | 0.54 | 0.5944 | 2.6456 | 0.54 | 0.5350 | 0.1548 | 0.2323 | | No log | 19.0 | 133 | 5.2611 | 0.5175 | 0.6096 | 2.5302 | 0.5175 | 0.5273 | 0.1709 | 0.2435 | | No log | 20.0 | 140 | 5.3536 | 0.52 | 0.6110 | 2.6530 | 0.52 | 0.5229 | 0.1619 | 0.2359 | | No log | 21.0 | 147 | 5.3020 | 0.5125 | 0.6060 | 2.7184 | 0.5125 | 0.5070 | 0.1724 | 0.2398 | | No log | 22.0 | 154 | 5.2107 | 0.5275 | 0.5926 | 2.4436 | 0.5275 | 0.5242 | 0.1618 | 0.2255 | | No log | 23.0 | 161 | 5.2723 | 0.53 | 0.5953 | 2.7008 | 0.53 | 0.5209 | 0.1698 | 0.2253 | | No log | 24.0 | 168 | 5.1615 | 0.5325 | 0.5875 | 2.4753 | 0.5325 | 0.5254 | 0.1699 | 0.2247 | | No log | 25.0 | 175 | 5.1795 | 0.5375 | 0.5825 | 2.6856 | 0.5375 | 0.5316 | 0.1781 | 0.2144 | | No log | 26.0 | 182 | 5.2340 | 0.54 | 0.5937 | 2.6542 | 0.54 | 0.5271 | 0.1778 | 0.2215 | | No log | 27.0 | 189 | 5.2197 | 0.5375 | 0.5831 | 2.4800 | 0.5375 | 0.5366 | 0.1666 | 0.2119 | | No log | 28.0 | 196 | 5.2345 | 0.5275 | 0.6105 | 2.6475 | 0.5275 | 0.5247 | 0.1919 | 0.2338 | | No log | 29.0 | 203 | 5.2050 | 0.5475 | 0.5917 | 2.6350 | 0.5475 | 0.5531 | 0.1753 | 0.2251 | | No log | 30.0 | 210 | 5.1753 | 0.5425 | 0.5891 | 2.6472 | 0.5425 | 0.5282 | 0.1831 | 0.2215 | | No log | 31.0 | 217 | 5.2349 | 0.535 | 0.5946 | 2.5653 | 0.535 | 0.5257 | 0.1617 | 0.2186 | | No log | 32.0 | 224 | 5.1497 | 0.545 | 0.5778 | 2.6174 | 0.545 | 0.5425 | 0.1716 | 0.2138 | | No log | 33.0 | 231 | 5.1688 | 0.5175 | 0.5899 | 2.5079 | 0.5175 | 0.5149 | 0.1624 | 0.2159 | | No log | 34.0 | 238 | 5.2269 | 0.53 | 0.5961 | 2.5188 | 0.53 | 0.5326 | 0.1746 | 0.2206 | | No log | 35.0 | 245 | 5.1477 | 0.5325 | 0.5867 | 2.4762 | 0.5325 | 0.5369 | 0.1728 | 0.2176 | | No log | 36.0 | 252 | 5.2229 | 0.5375 | 0.5838 | 2.4397 | 0.5375 | 0.5386 | 0.1693 | 0.2167 | | No log | 37.0 | 259 | 5.1578 | 0.535 | 0.5802 | 2.5103 | 0.535 | 0.5286 | 0.1755 | 0.2124 | | No log | 38.0 | 266 | 5.1405 | 0.535 | 0.5979 | 2.5852 | 0.535 | 0.5346 | 0.1913 | 0.2268 | | No log | 39.0 | 273 | 5.1236 | 0.535 | 0.5844 | 2.4851 | 0.535 | 0.5378 | 0.1729 | 0.2168 | | No log | 40.0 | 280 | 5.0813 | 0.5475 | 0.5757 | 2.4305 | 0.5475 | 0.5434 | 0.1781 | 0.2091 | | No log | 41.0 | 287 | 5.1844 | 0.535 | 0.5888 | 2.4730 | 0.535 | 0.5306 | 0.1707 | 0.2159 | | No log | 42.0 | 294 | 5.1468 | 0.53 | 0.5926 | 2.4866 | 0.53 | 0.5316 | 0.1776 | 0.2200 | | No log | 43.0 | 301 | 5.1469 | 0.53 | 0.5837 | 2.5769 | 0.53 | 0.5252 | 0.1805 | 0.2168 | | No log | 44.0 | 308 | 5.2168 | 0.54 | 0.5955 | 2.5216 | 0.54 | 0.5419 | 0.1689 | 0.2226 | | No log | 45.0 | 315 | 5.1395 | 0.525 | 0.5861 | 2.4328 | 0.525 | 0.5293 | 0.2006 | 0.2180 | | No log | 46.0 | 322 | 5.1163 | 0.5425 | 0.5822 | 2.4635 | 0.5425 | 0.5416 | 0.1937 | 0.2106 | | No log | 47.0 | 329 | 5.1227 | 0.5475 | 0.5786 | 2.5198 | 0.5475 | 0.5489 | 0.1580 | 0.2111 | | No log | 48.0 | 336 | 5.1134 | 0.5375 | 0.5839 | 2.5239 | 0.5375 | 0.5318 | 0.1832 | 0.2071 | | No log | 49.0 | 343 | 5.1907 | 0.5375 | 0.5913 | 2.5012 | 0.5375 | 0.5334 | 0.1853 | 0.2145 | | No log | 50.0 | 350 | 5.1364 | 0.5375 | 0.5875 | 2.4105 | 0.5375 | 0.5415 | 0.1857 | 0.2121 | | No log | 51.0 | 357 | 5.1739 | 0.5425 | 0.5905 | 2.5208 | 0.5425 | 0.5399 | 0.1894 | 0.2112 | | No log | 52.0 | 364 | 5.1635 | 0.5325 | 0.5841 | 2.4658 | 0.5325 | 0.5300 | 0.1924 | 0.2124 | | No log | 53.0 | 371 | 5.2055 | 0.5425 | 0.5866 | 2.4800 | 0.5425 | 0.5390 | 0.1983 | 0.2135 | | No log | 54.0 | 378 | 5.1547 | 0.5375 | 0.5869 | 2.4575 | 0.5375 | 0.5340 | 0.1839 | 0.2117 | | No log | 55.0 | 385 | 5.1437 | 0.535 | 0.5838 | 2.4117 | 0.535 | 0.5366 | 0.1914 | 0.2110 | | No log | 56.0 | 392 | 5.2042 | 0.5425 | 0.5915 | 2.4286 | 0.5425 | 0.5445 | 0.1905 | 0.2124 | | No log | 57.0 | 399 | 5.2084 | 0.5625 | 0.5909 | 2.4774 | 0.5625 | 0.5646 | 0.2006 | 0.2116 | | No log | 58.0 | 406 | 5.1844 | 0.545 | 0.5895 | 2.3826 | 0.545 | 0.5466 | 0.1948 | 0.2102 | | No log | 59.0 | 413 | 5.1759 | 0.545 | 0.5892 | 2.4790 | 0.545 | 0.5498 | 0.1730 | 0.2143 | | No log | 60.0 | 420 | 5.1783 | 0.5475 | 0.5894 | 2.4294 | 0.5475 | 0.5452 | 0.2043 | 0.2087 | | No log | 61.0 | 427 | 5.1874 | 0.545 | 0.5879 | 2.4295 | 0.545 | 0.5412 | 0.1959 | 0.2080 | | No log | 62.0 | 434 | 5.1861 | 0.5475 | 0.5840 | 2.4513 | 0.5475 | 0.5430 | 0.2097 | 0.2107 | | No log | 63.0 | 441 | 5.1608 | 0.545 | 0.5818 | 2.4581 | 0.545 | 0.5450 | 0.1666 | 0.2055 | | No log | 64.0 | 448 | 5.2018 | 0.5475 | 0.5911 | 2.4537 | 0.5475 | 0.5448 | 0.1938 | 0.2113 | | No log | 65.0 | 455 | 5.2113 | 0.5375 | 0.5953 | 2.4444 | 0.5375 | 0.5360 | 0.1757 | 0.2106 | | No log | 66.0 | 462 | 5.1985 | 0.5425 | 0.5897 | 2.4287 | 0.5425 | 0.5377 | 0.1870 | 0.2095 | | No log | 67.0 | 469 | 5.2218 | 0.5325 | 0.5856 | 2.4340 | 0.5325 | 0.5320 | 0.1882 | 0.2059 | | No log | 68.0 | 476 | 5.2243 | 0.545 | 0.5931 | 2.3923 | 0.545 | 0.5447 | 0.1799 | 0.2120 | | No log | 69.0 | 483 | 5.2103 | 0.55 | 0.5881 | 2.4619 | 0.55 | 0.5486 | 0.2084 | 0.2060 | | No log | 70.0 | 490 | 5.2370 | 0.55 | 0.5933 | 2.4236 | 0.55 | 0.5521 | 0.1920 | 0.2108 | | No log | 71.0 | 497 | 5.2185 | 0.5475 | 0.5890 | 2.4137 | 0.5475 | 0.5435 | 0.2121 | 0.2076 | | 3.6002 | 72.0 | 504 | 5.2460 | 0.545 | 0.5944 | 2.4704 | 0.545 | 0.5430 | 0.1922 | 0.2117 | | 3.6002 | 73.0 | 511 | 5.2454 | 0.5425 | 0.5928 | 2.4750 | 0.5425 | 0.5406 | 0.1940 | 0.2080 | | 3.6002 | 74.0 | 518 | 5.2307 | 0.5575 | 0.5935 | 2.4623 | 0.5575 | 0.5599 | 0.1959 | 0.2071 | | 3.6002 | 75.0 | 525 | 5.2674 | 0.56 | 0.5877 | 2.4453 | 0.56 | 0.5587 | 0.1956 | 0.2033 | | 3.6002 | 76.0 | 532 | 5.2263 | 0.5525 | 0.5907 | 2.5044 | 0.5525 | 0.5526 | 0.1862 | 0.2067 | | 3.6002 | 77.0 | 539 | 5.2498 | 0.55 | 0.5938 | 2.4668 | 0.55 | 0.5467 | 0.2072 | 0.2059 | | 3.6002 | 78.0 | 546 | 5.2671 | 0.545 | 0.5961 | 2.4394 | 0.545 | 0.5421 | 0.2056 | 0.2093 | | 3.6002 | 79.0 | 553 | 5.2923 | 0.545 | 0.5950 | 2.4662 | 0.545 | 0.5455 | 0.1833 | 0.2058 | | 3.6002 | 80.0 | 560 | 5.2854 | 0.555 | 0.5918 | 2.5010 | 0.555 | 0.5526 | 0.2040 | 0.2059 | | 3.6002 | 81.0 | 567 | 5.3009 | 0.535 | 0.5955 | 2.4253 | 0.535 | 0.5319 | 0.1939 | 0.2101 | | 3.6002 | 82.0 | 574 | 5.3016 | 0.535 | 0.5979 | 2.4528 | 0.535 | 0.5315 | 0.2020 | 0.2101 | | 3.6002 | 83.0 | 581 | 5.3262 | 0.545 | 0.5990 | 2.4245 | 0.545 | 0.5422 | 0.1816 | 0.2081 | | 3.6002 | 84.0 | 588 | 5.3206 | 0.535 | 0.5990 | 2.4519 | 0.535 | 0.5350 | 0.1959 | 0.2121 | | 3.6002 | 85.0 | 595 | 5.3333 | 0.5375 | 0.5999 | 2.4909 | 0.5375 | 0.5352 | 0.1881 | 0.2109 | | 3.6002 | 86.0 | 602 | 5.3407 | 0.535 | 0.6008 | 2.5019 | 0.535 | 0.5331 | 0.2087 | 0.2096 | | 3.6002 | 87.0 | 609 | 5.3413 | 0.5425 | 0.6015 | 2.4753 | 0.5425 | 0.5402 | 0.2147 | 0.2101 | | 3.6002 | 88.0 | 616 | 5.3716 | 0.5375 | 0.6041 | 2.4290 | 0.5375 | 0.5373 | 0.2234 | 0.2094 | | 3.6002 | 89.0 | 623 | 5.3639 | 0.535 | 0.6010 | 2.4159 | 0.535 | 0.5319 | 0.2068 | 0.2108 | | 3.6002 | 90.0 | 630 | 5.3742 | 0.5425 | 0.6030 | 2.4588 | 0.5425 | 0.5420 | 0.2021 | 0.2099 | | 3.6002 | 91.0 | 637 | 5.3731 | 0.53 | 0.6046 | 2.4580 | 0.53 | 0.5284 | 0.2193 | 0.2122 | | 3.6002 | 92.0 | 644 | 5.3919 | 0.54 | 0.6051 | 2.4317 | 0.54 | 0.5395 | 0.2057 | 0.2090 | | 3.6002 | 93.0 | 651 | 5.3947 | 0.54 | 0.6049 | 2.4372 | 0.54 | 0.5385 | 0.2053 | 0.2092 | | 3.6002 | 94.0 | 658 | 5.4070 | 0.535 | 0.6067 | 2.4600 | 0.535 | 0.5328 | 0.2297 | 0.2108 | | 3.6002 | 95.0 | 665 | 5.4129 | 0.535 | 0.6071 | 2.4249 | 0.535 | 0.5345 | 0.2186 | 0.2104 | | 3.6002 | 96.0 | 672 | 5.4137 | 0.535 | 0.6071 | 2.4580 | 0.535 | 0.5339 | 0.2192 | 0.2102 | | 3.6002 | 97.0 | 679 | 5.4218 | 0.5325 | 0.6079 | 2.4584 | 0.5325 | 0.5316 | 0.2197 | 0.2112 | | 3.6002 | 98.0 | 686 | 5.4236 | 0.5325 | 0.6083 | 2.4585 | 0.5325 | 0.5311 | 0.2154 | 0.2110 | | 3.6002 | 99.0 | 693 | 5.4261 | 0.5325 | 0.6081 | 2.4569 | 0.5325 | 0.5316 | 0.2294 | 0.2114 | | 3.6002 | 100.0 | 700 | 5.4262 | 0.535 | 0.6080 | 2.4569 | 0.535 | 0.5345 | 0.2120 | 0.2105 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM3

# vit-base-patch16-224-Trial006-007-008-YEL_STEM3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1618 - Accuracy: 0.9241 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7035 | 1.0 | 6 | 0.6557 | 0.6203 | | 0.6168 | 2.0 | 12 | 0.5788 | 0.7215 | | 0.5412 | 3.0 | 18 | 0.5005 | 0.7785 | | 0.496 | 4.0 | 24 | 0.4946 | 0.7722 | | 0.4024 | 5.0 | 30 | 0.4057 | 0.8165 | | 0.4098 | 6.0 | 36 | 0.3076 | 0.8544 | | 0.3645 | 7.0 | 42 | 0.3250 | 0.8418 | | 0.276 | 8.0 | 48 | 0.2206 | 0.8924 | | 0.3358 | 9.0 | 54 | 0.2100 | 0.8987 | | 0.3386 | 10.0 | 60 | 0.1618 | 0.9241 | | 0.2778 | 11.0 | 66 | 0.1609 | 0.9177 | | 0.25 | 12.0 | 72 | 0.1581 | 0.9114 | | 0.2914 | 13.0 | 78 | 0.1663 | 0.9114 | | 0.2273 | 14.0 | 84 | 0.1525 | 0.9177 | | 0.2694 | 15.0 | 90 | 0.1708 | 0.9051 | | 0.2745 | 16.0 | 96 | 0.2364 | 0.8734 | | 0.2809 | 17.0 | 102 | 0.1976 | 0.8608 | | 0.2368 | 18.0 | 108 | 0.1517 | 0.9114 | | 0.328 | 19.0 | 114 | 0.2454 | 0.8671 | | 0.2571 | 20.0 | 120 | 0.1482 | 0.9114 | | 0.2996 | 21.0 | 126 | 0.1629 | 0.8987 | | 0.266 | 22.0 | 132 | 0.1360 | 0.9114 | | 0.2323 | 23.0 | 138 | 0.1427 | 0.9114 | | 0.2285 | 24.0 | 144 | 0.1683 | 0.9051 | | 0.2566 | 25.0 | 150 | 0.1442 | 0.9114 | | 0.2509 | 26.0 | 156 | 0.1595 | 0.9114 | | 0.2337 | 27.0 | 162 | 0.1291 | 0.9177 | | 0.2203 | 28.0 | 168 | 0.1302 | 0.8987 | | 0.2409 | 29.0 | 174 | 0.1274 | 0.9114 | | 0.2256 | 30.0 | 180 | 0.1272 | 0.8987 | | 0.2157 | 31.0 | 186 | 0.1289 | 0.9177 | | 0.2168 | 32.0 | 192 | 0.1267 | 0.9114 | | 0.2426 | 33.0 | 198 | 0.1438 | 0.8987 | | 0.2404 | 34.0 | 204 | 0.1388 | 0.8987 | | 0.2218 | 35.0 | 210 | 0.1243 | 0.9241 | | 0.3068 | 36.0 | 216 | 0.1268 | 0.9241 | | 0.1721 | 37.0 | 222 | 0.1477 | 0.8987 | | 0.2201 | 38.0 | 228 | 0.1545 | 0.8987 | | 0.2581 | 39.0 | 234 | 0.1700 | 0.8987 | | 0.213 | 40.0 | 240 | 0.1254 | 0.9114 | | 0.2953 | 41.0 | 246 | 0.1237 | 0.9114 | | 0.2564 | 42.0 | 252 | 0.1472 | 0.9051 | | 0.249 | 43.0 | 258 | 0.1409 | 0.9051 | | 0.2372 | 44.0 | 264 | 0.1495 | 0.9114 | | 0.2541 | 45.0 | 270 | 0.1412 | 0.9051 | | 0.1997 | 46.0 | 276 | 0.1308 | 0.9114 | | 0.2381 | 47.0 | 282 | 0.1253 | 0.9177 | | 0.2623 | 48.0 | 288 | 0.1267 | 0.9051 | | 0.1855 | 49.0 | 294 | 0.1285 | 0.9051 | | 0.1877 | 50.0 | 300 | 0.1289 | 0.9051 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

[ "plant2", "plant3" ]

sbaner24/vit-base-patch16-224-Trial006-007-008-YEL_STEM4

# vit-base-patch16-224-Trial006-007-008-YEL_STEM4 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1729 - Accuracy: 0.9355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6572 | 1.0 | 6 | 0.6719 | 0.5677 | | 0.6279 | 2.0 | 12 | 0.6436 | 0.6516 | | 0.5282 | 3.0 | 18 | 0.5677 | 0.7871 | | 0.5405 | 4.0 | 24 | 0.4718 | 0.8258 | | 0.5276 | 5.0 | 30 | 0.4959 | 0.7419 | | 0.412 | 6.0 | 36 | 0.3081 | 0.8581 | | 0.3011 | 7.0 | 42 | 0.2542 | 0.8839 | | 0.3329 | 8.0 | 48 | 0.2350 | 0.8774 | | 0.4478 | 9.0 | 54 | 0.1743 | 0.9290 | | 0.268 | 10.0 | 60 | 0.1708 | 0.9161 | | 0.2177 | 11.0 | 66 | 0.1729 | 0.9355 | | 0.2675 | 12.0 | 72 | 0.1913 | 0.8968 | | 0.4784 | 13.0 | 78 | 0.1826 | 0.9032 | | 0.2456 | 14.0 | 84 | 0.1774 | 0.9032 | | 0.6229 | 15.0 | 90 | 0.2196 | 0.8968 | | 0.2561 | 16.0 | 96 | 0.1823 | 0.9226 | | 0.3785 | 17.0 | 102 | 0.1770 | 0.9032 | | 0.2334 | 18.0 | 108 | 0.2056 | 0.8903 | | 0.1904 | 19.0 | 114 | 0.1564 | 0.9097 | | 0.2256 | 20.0 | 120 | 0.1407 | 0.9226 | | 0.2547 | 21.0 | 126 | 0.1552 | 0.9032 | | 0.3468 | 22.0 | 132 | 0.1819 | 0.8968 | | 0.4116 | 23.0 | 138 | 0.1537 | 0.9290 | | 0.3689 | 24.0 | 144 | 0.1645 | 0.9097 | | 0.3541 | 25.0 | 150 | 0.1527 | 0.9290 | | 0.2498 | 26.0 | 156 | 0.1670 | 0.9161 | | 0.3625 | 27.0 | 162 | 0.1522 | 0.9161 | | 0.2463 | 28.0 | 168 | 0.1552 | 0.9226 | | 0.3447 | 29.0 | 174 | 0.1510 | 0.9097 | | 0.205 | 30.0 | 180 | 0.1924 | 0.9032 | | 0.2023 | 31.0 | 186 | 0.1376 | 0.9355 | | 0.3617 | 32.0 | 192 | 0.1518 | 0.9097 | | 0.3515 | 33.0 | 198 | 0.1473 | 0.9097 | | 0.1927 | 34.0 | 204 | 0.1544 | 0.9097 | | 0.4567 | 35.0 | 210 | 0.1528 | 0.9097 | | 0.3113 | 36.0 | 216 | 0.1510 | 0.9226 | | 0.3475 | 37.0 | 222 | 0.1594 | 0.9161 | | 0.1889 | 38.0 | 228 | 0.1448 | 0.9290 | | 0.1979 | 39.0 | 234 | 0.1533 | 0.9226 | | 0.3578 | 40.0 | 240 | 0.1627 | 0.9097 | | 0.2004 | 41.0 | 246 | 0.1620 | 0.9161 | | 0.3567 | 42.0 | 252 | 0.1475 | 0.9226 | | 0.192 | 43.0 | 258 | 0.1504 | 0.9032 | | 0.1872 | 44.0 | 264 | 0.1535 | 0.9097 | | 0.2079 | 45.0 | 270 | 0.1490 | 0.9161 | | 0.1503 | 46.0 | 276 | 0.1459 | 0.9161 | | 0.169 | 47.0 | 282 | 0.1506 | 0.8968 | | 0.1884 | 48.0 | 288 | 0.1556 | 0.8968 | | 0.1638 | 49.0 | 294 | 0.1573 | 0.8968 | | 0.1921 | 50.0 | 300 | 0.1570 | 0.8968 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

[ "plant2", "plant3" ]

02shanky/test_model_graphics_classification_LION

# test_model_graphics_classification_LION This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1569 - Accuracy: 0.9558 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1765 | 0.99 | 42 | 0.1569 | 0.9558 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "cartoon", "icon", "images", "nongraphics" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.5

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6786 - Accuracy: 0.5475 - Brier Loss: 0.5719 - Nll: 2.4381 - F1 Micro: 0.5475 - F1 Macro: 0.5432 - Ece: 0.1563 - Aurc: 0.2080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.6969 | 0.0425 | 1.0710 | 7.3408 | 0.0425 | 0.0336 | 0.2818 | 0.9543 | | No log | 2.0 | 14 | 3.6834 | 0.1 | 0.9492 | 5.8001 | 0.1000 | 0.0878 | 0.1662 | 0.8811 | | No log | 3.0 | 21 | 3.2796 | 0.2325 | 0.8815 | 5.3289 | 0.2325 | 0.1817 | 0.1601 | 0.6217 | | No log | 4.0 | 28 | 2.7674 | 0.2975 | 0.7907 | 3.6563 | 0.2975 | 0.2855 | 0.1624 | 0.4730 | | No log | 5.0 | 35 | 2.4072 | 0.3925 | 0.7175 | 3.0843 | 0.3925 | 0.3726 | 0.1811 | 0.3501 | | No log | 6.0 | 42 | 2.2499 | 0.44 | 0.6923 | 3.0183 | 0.44 | 0.4252 | 0.2052 | 0.3017 | | No log | 7.0 | 49 | 2.2199 | 0.455 | 0.6973 | 3.0254 | 0.455 | 0.4233 | 0.2117 | 0.2958 | | No log | 8.0 | 56 | 2.1564 | 0.4725 | 0.6958 | 2.8105 | 0.4725 | 0.4637 | 0.2159 | 0.2954 | | No log | 9.0 | 63 | 2.2963 | 0.4575 | 0.7105 | 2.7884 | 0.4575 | 0.4386 | 0.2617 | 0.2866 | | No log | 10.0 | 70 | 2.3511 | 0.4475 | 0.7553 | 2.8174 | 0.4475 | 0.4278 | 0.2698 | 0.3171 | | No log | 11.0 | 77 | 2.4111 | 0.4425 | 0.7483 | 2.9313 | 0.4425 | 0.4290 | 0.2569 | 0.3056 | | No log | 12.0 | 84 | 2.2927 | 0.4825 | 0.7158 | 2.7197 | 0.4825 | 0.4799 | 0.2453 | 0.2920 | | No log | 13.0 | 91 | 2.2809 | 0.4825 | 0.7098 | 2.6808 | 0.4825 | 0.4758 | 0.2454 | 0.2811 | | No log | 14.0 | 98 | 2.1787 | 0.4825 | 0.7036 | 2.5537 | 0.4825 | 0.4801 | 0.2417 | 0.2877 | | No log | 15.0 | 105 | 2.1643 | 0.495 | 0.6934 | 2.7769 | 0.495 | 0.4892 | 0.2375 | 0.2707 | | No log | 16.0 | 112 | 2.1309 | 0.48 | 0.6866 | 2.5936 | 0.48 | 0.4647 | 0.2130 | 0.2663 | | No log | 17.0 | 119 | 2.1389 | 0.5125 | 0.6580 | 2.6313 | 0.5125 | 0.4962 | 0.1976 | 0.2424 | | No log | 18.0 | 126 | 2.0813 | 0.5025 | 0.6649 | 2.6517 | 0.5025 | 0.5019 | 0.2126 | 0.2602 | | No log | 19.0 | 133 | 2.0742 | 0.4975 | 0.6763 | 2.6690 | 0.4975 | 0.4739 | 0.2473 | 0.2575 | | No log | 20.0 | 140 | 2.0550 | 0.48 | 0.6702 | 2.5468 | 0.48 | 0.4838 | 0.2206 | 0.2745 | | No log | 21.0 | 147 | 1.9216 | 0.52 | 0.6332 | 2.6838 | 0.52 | 0.5237 | 0.1895 | 0.2410 | | No log | 22.0 | 154 | 1.9572 | 0.5025 | 0.6500 | 2.4719 | 0.5025 | 0.4877 | 0.2223 | 0.2470 | | No log | 23.0 | 161 | 1.8821 | 0.5175 | 0.6294 | 2.5337 | 0.5175 | 0.5067 | 0.2081 | 0.2332 | | No log | 24.0 | 168 | 1.9198 | 0.5125 | 0.6332 | 2.5310 | 0.5125 | 0.5028 | 0.2039 | 0.2434 | | No log | 25.0 | 175 | 1.9419 | 0.515 | 0.6358 | 2.6324 | 0.515 | 0.5005 | 0.2203 | 0.2383 | | No log | 26.0 | 182 | 1.8714 | 0.525 | 0.6145 | 2.5820 | 0.525 | 0.5005 | 0.1843 | 0.2232 | | No log | 27.0 | 189 | 1.9460 | 0.4975 | 0.6365 | 2.6119 | 0.4975 | 0.4907 | 0.2169 | 0.2395 | | No log | 28.0 | 196 | 1.9364 | 0.525 | 0.6333 | 2.5789 | 0.525 | 0.5013 | 0.1808 | 0.2437 | | No log | 29.0 | 203 | 1.9143 | 0.5425 | 0.6144 | 2.7809 | 0.5425 | 0.5181 | 0.1700 | 0.2224 | | No log | 30.0 | 210 | 1.8565 | 0.5275 | 0.6171 | 2.5610 | 0.5275 | 0.5142 | 0.1823 | 0.2304 | | No log | 31.0 | 217 | 1.8281 | 0.5325 | 0.6113 | 2.6216 | 0.5325 | 0.5191 | 0.1802 | 0.2167 | | No log | 32.0 | 224 | 1.8213 | 0.525 | 0.6192 | 2.5521 | 0.525 | 0.5278 | 0.1844 | 0.2368 | | No log | 33.0 | 231 | 1.7724 | 0.545 | 0.6030 | 2.4900 | 0.545 | 0.5460 | 0.1810 | 0.2211 | | No log | 34.0 | 238 | 1.7617 | 0.5475 | 0.5985 | 2.5994 | 0.5475 | 0.5345 | 0.1907 | 0.2161 | | No log | 35.0 | 245 | 1.8276 | 0.54 | 0.6090 | 2.5254 | 0.54 | 0.5172 | 0.1839 | 0.2249 | | No log | 36.0 | 252 | 1.7646 | 0.5475 | 0.5924 | 2.5729 | 0.5475 | 0.5458 | 0.1930 | 0.2190 | | No log | 37.0 | 259 | 1.7851 | 0.5675 | 0.5872 | 2.5036 | 0.5675 | 0.5600 | 0.1670 | 0.2056 | | No log | 38.0 | 266 | 1.7743 | 0.5225 | 0.6136 | 2.4765 | 0.5225 | 0.5163 | 0.1831 | 0.2276 | | No log | 39.0 | 273 | 1.7760 | 0.5325 | 0.5912 | 2.5692 | 0.5325 | 0.5228 | 0.1589 | 0.2108 | | No log | 40.0 | 280 | 1.7664 | 0.53 | 0.5917 | 2.5363 | 0.53 | 0.5089 | 0.1800 | 0.2095 | | No log | 41.0 | 287 | 1.8177 | 0.535 | 0.6070 | 2.5195 | 0.535 | 0.5272 | 0.1883 | 0.2268 | | No log | 42.0 | 294 | 1.7575 | 0.56 | 0.5868 | 2.5956 | 0.56 | 0.5504 | 0.1740 | 0.2091 | | No log | 43.0 | 301 | 1.7616 | 0.54 | 0.6001 | 2.3469 | 0.54 | 0.5401 | 0.1861 | 0.2272 | | No log | 44.0 | 308 | 1.7105 | 0.5425 | 0.5831 | 2.4709 | 0.5425 | 0.5380 | 0.1884 | 0.2086 | | No log | 45.0 | 315 | 1.7502 | 0.565 | 0.5880 | 2.4600 | 0.565 | 0.5546 | 0.1527 | 0.2084 | | No log | 46.0 | 322 | 1.7135 | 0.565 | 0.5834 | 2.4571 | 0.565 | 0.5631 | 0.1703 | 0.2103 | | No log | 47.0 | 329 | 1.7327 | 0.5525 | 0.5892 | 2.4573 | 0.5525 | 0.5491 | 0.1820 | 0.2178 | | No log | 48.0 | 336 | 1.7405 | 0.535 | 0.5947 | 2.5414 | 0.535 | 0.5338 | 0.1840 | 0.2180 | | No log | 49.0 | 343 | 1.7265 | 0.555 | 0.5758 | 2.4824 | 0.555 | 0.5423 | 0.1603 | 0.2025 | | No log | 50.0 | 350 | 1.7065 | 0.5525 | 0.5904 | 2.3807 | 0.5525 | 0.5576 | 0.1769 | 0.2212 | | No log | 51.0 | 357 | 1.7265 | 0.545 | 0.5833 | 2.4535 | 0.545 | 0.5413 | 0.1704 | 0.2108 | | No log | 52.0 | 364 | 1.7197 | 0.55 | 0.5740 | 2.5386 | 0.55 | 0.5347 | 0.1467 | 0.2032 | | No log | 53.0 | 371 | 1.7015 | 0.5575 | 0.5826 | 2.4159 | 0.5575 | 0.5578 | 0.1751 | 0.2138 | | No log | 54.0 | 378 | 1.7263 | 0.55 | 0.5873 | 2.4471 | 0.55 | 0.5456 | 0.1629 | 0.2144 | | No log | 55.0 | 385 | 1.6786 | 0.555 | 0.5780 | 2.3908 | 0.555 | 0.5490 | 0.1627 | 0.2106 | | No log | 56.0 | 392 | 1.7147 | 0.55 | 0.5811 | 2.3876 | 0.55 | 0.5476 | 0.1724 | 0.2122 | | No log | 57.0 | 399 | 1.6983 | 0.5525 | 0.5769 | 2.5028 | 0.5525 | 0.5415 | 0.1716 | 0.2054 | | No log | 58.0 | 406 | 1.7350 | 0.5425 | 0.5984 | 2.3835 | 0.5425 | 0.5406 | 0.1806 | 0.2282 | | No log | 59.0 | 413 | 1.7015 | 0.54 | 0.5779 | 2.4865 | 0.54 | 0.5288 | 0.1848 | 0.2079 | | No log | 60.0 | 420 | 1.6783 | 0.5525 | 0.5712 | 2.4231 | 0.5525 | 0.5497 | 0.1777 | 0.2046 | | No log | 61.0 | 427 | 1.7236 | 0.545 | 0.5856 | 2.5104 | 0.545 | 0.5379 | 0.1850 | 0.2142 | | No log | 62.0 | 434 | 1.6904 | 0.5425 | 0.5752 | 2.4626 | 0.5425 | 0.5400 | 0.1787 | 0.2084 | | No log | 63.0 | 441 | 1.7136 | 0.5375 | 0.5837 | 2.4091 | 0.5375 | 0.5304 | 0.1681 | 0.2150 | | No log | 64.0 | 448 | 1.6783 | 0.56 | 0.5762 | 2.4017 | 0.56 | 0.5594 | 0.1607 | 0.2093 | | No log | 65.0 | 455 | 1.7098 | 0.5425 | 0.5797 | 2.5062 | 0.5425 | 0.5406 | 0.1707 | 0.2120 | | No log | 66.0 | 462 | 1.6752 | 0.5525 | 0.5712 | 2.4684 | 0.5525 | 0.5478 | 0.1711 | 0.2063 | | No log | 67.0 | 469 | 1.7068 | 0.545 | 0.5761 | 2.5107 | 0.545 | 0.5415 | 0.1750 | 0.2090 | | No log | 68.0 | 476 | 1.6706 | 0.5525 | 0.5700 | 2.4322 | 0.5525 | 0.5466 | 0.1798 | 0.2068 | | No log | 69.0 | 483 | 1.6941 | 0.5525 | 0.5749 | 2.4689 | 0.5525 | 0.5501 | 0.1654 | 0.2072 | | No log | 70.0 | 490 | 1.6726 | 0.545 | 0.5711 | 2.4348 | 0.545 | 0.5416 | 0.1811 | 0.2083 | | No log | 71.0 | 497 | 1.6831 | 0.5525 | 0.5740 | 2.4659 | 0.5525 | 0.5498 | 0.1687 | 0.2061 | | 0.4086 | 72.0 | 504 | 1.6749 | 0.5575 | 0.5699 | 2.4122 | 0.5575 | 0.5535 | 0.1706 | 0.2051 | | 0.4086 | 73.0 | 511 | 1.6867 | 0.555 | 0.5740 | 2.4402 | 0.555 | 0.5495 | 0.1725 | 0.2069 | | 0.4086 | 74.0 | 518 | 1.6768 | 0.5475 | 0.5716 | 2.4369 | 0.5475 | 0.5442 | 0.1621 | 0.2080 | | 0.4086 | 75.0 | 525 | 1.6835 | 0.55 | 0.5736 | 2.4404 | 0.55 | 0.5448 | 0.1824 | 0.2075 | | 0.4086 | 76.0 | 532 | 1.6737 | 0.5525 | 0.5701 | 2.4361 | 0.5525 | 0.5497 | 0.1731 | 0.2063 | | 0.4086 | 77.0 | 539 | 1.6796 | 0.55 | 0.5721 | 2.4399 | 0.55 | 0.5450 | 0.1723 | 0.2066 | | 0.4086 | 78.0 | 546 | 1.6774 | 0.55 | 0.5718 | 2.4362 | 0.55 | 0.5472 | 0.1732 | 0.2071 | | 0.4086 | 79.0 | 553 | 1.6781 | 0.555 | 0.5711 | 2.4390 | 0.555 | 0.5506 | 0.1588 | 0.2059 | | 0.4086 | 80.0 | 560 | 1.6787 | 0.555 | 0.5714 | 2.4380 | 0.555 | 0.5508 | 0.1832 | 0.2070 | | 0.4086 | 81.0 | 567 | 1.6778 | 0.555 | 0.5717 | 2.4366 | 0.555 | 0.5516 | 0.1615 | 0.2060 | | 0.4086 | 82.0 | 574 | 1.6788 | 0.55 | 0.5717 | 2.4380 | 0.55 | 0.5462 | 0.1680 | 0.2070 | | 0.4086 | 83.0 | 581 | 1.6761 | 0.5525 | 0.5712 | 2.4366 | 0.5525 | 0.5505 | 0.1809 | 0.2064 | | 0.4086 | 84.0 | 588 | 1.6778 | 0.55 | 0.5714 | 2.4388 | 0.55 | 0.5443 | 0.1571 | 0.2073 | | 0.4086 | 85.0 | 595 | 1.6772 | 0.5525 | 0.5716 | 2.4367 | 0.5525 | 0.5479 | 0.1768 | 0.2073 | | 0.4086 | 86.0 | 602 | 1.6789 | 0.5525 | 0.5722 | 2.4376 | 0.5525 | 0.5470 | 0.1646 | 0.2066 | | 0.4086 | 87.0 | 609 | 1.6784 | 0.5525 | 0.5717 | 2.4384 | 0.5525 | 0.5486 | 0.1743 | 0.2073 | | 0.4086 | 88.0 | 616 | 1.6786 | 0.55 | 0.5720 | 2.4382 | 0.55 | 0.5443 | 0.1559 | 0.2077 | | 0.4086 | 89.0 | 623 | 1.6784 | 0.5525 | 0.5718 | 2.4379 | 0.5525 | 0.5479 | 0.1561 | 0.2073 | | 0.4086 | 90.0 | 630 | 1.6782 | 0.5525 | 0.5718 | 2.4381 | 0.5525 | 0.5482 | 0.1561 | 0.2066 | | 0.4086 | 91.0 | 637 | 1.6784 | 0.5525 | 0.5719 | 2.4375 | 0.5525 | 0.5482 | 0.1612 | 0.2065 | | 0.4086 | 92.0 | 644 | 1.6783 | 0.55 | 0.5718 | 2.4377 | 0.55 | 0.5456 | 0.1702 | 0.2073 | | 0.4086 | 93.0 | 651 | 1.6781 | 0.55 | 0.5718 | 2.4378 | 0.55 | 0.5456 | 0.1581 | 0.2075 | | 0.4086 | 94.0 | 658 | 1.6785 | 0.55 | 0.5719 | 2.4379 | 0.55 | 0.5455 | 0.1602 | 0.2074 | | 0.4086 | 95.0 | 665 | 1.6785 | 0.55 | 0.5719 | 2.4379 | 0.55 | 0.5456 | 0.1635 | 0.2075 | | 0.4086 | 96.0 | 672 | 1.6785 | 0.5475 | 0.5719 | 2.4381 | 0.5475 | 0.5432 | 0.1659 | 0.2080 | | 0.4086 | 97.0 | 679 | 1.6786 | 0.55 | 0.5719 | 2.4381 | 0.55 | 0.5458 | 0.1545 | 0.2071 | | 0.4086 | 98.0 | 686 | 1.6786 | 0.5475 | 0.5719 | 2.4381 | 0.5475 | 0.5431 | 0.1613 | 0.2081 | | 0.4086 | 99.0 | 693 | 1.6786 | 0.5475 | 0.5719 | 2.4381 | 0.5475 | 0.5431 | 0.1613 | 0.2081 | | 0.4086 | 100.0 | 700 | 1.6786 | 0.5475 | 0.5719 | 2.4381 | 0.5475 | 0.5432 | 0.1563 | 0.2080 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-small_rvl_cdip

# vit-small_rvl_cdip This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0854 - Accuracy: 0.9110 - Brier Loss: 0.1305 - Nll: 1.2339 - F1 Micro: 0.9110 - F1 Macro: 0.9112 - Ece: 0.0097 - Aurc: 0.0122 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 0.2638 | 1.0 | 2500 | 0.2093 | 0.8574 | 0.2035 | 1.6580 | 0.8574 | 0.8564 | 0.0105 | 0.0272 | | 0.1559 | 2.0 | 5000 | 0.1413 | 0.8854 | 0.1645 | 1.5062 | 0.8854 | 0.8863 | 0.0101 | 0.0185 | | 0.1036 | 3.0 | 7500 | 0.1212 | 0.8994 | 0.1461 | 1.4353 | 0.8994 | 0.8999 | 0.0096 | 0.0150 | | 0.0693 | 4.0 | 10000 | 0.1196 | 0.9013 | 0.1440 | 1.3827 | 0.9013 | 0.9017 | 0.0152 | 0.0148 | | 0.0451 | 5.0 | 12500 | 0.1094 | 0.9062 | 0.1391 | 1.3099 | 0.9062 | 0.9064 | 0.0124 | 0.0139 | | 0.0317 | 6.0 | 15000 | 0.0997 | 0.9073 | 0.1357 | 1.2889 | 0.9073 | 0.9078 | 0.0091 | 0.0132 | | 0.0224 | 7.0 | 17500 | 0.0961 | 0.9081 | 0.1348 | 1.2705 | 0.9081 | 0.9082 | 0.0100 | 0.0129 | | 0.0166 | 8.0 | 20000 | 0.0890 | 0.9099 | 0.1328 | 1.2484 | 0.9099 | 0.9102 | 0.0085 | 0.0126 | | 0.0117 | 9.0 | 22500 | 0.0862 | 0.9096 | 0.1316 | 1.2428 | 0.9096 | 0.9100 | 0.0100 | 0.0123 | | 0.0085 | 10.0 | 25000 | 0.0854 | 0.9110 | 0.1305 | 1.2339 | 0.9110 | 0.9112 | 0.0097 | 0.0122 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5923 - Accuracy: 0.57 - Brier Loss: 0.5750 - Nll: 2.3088 - F1 Micro: 0.57 - F1 Macro: 0.5661 - Ece: 0.1722 - Aurc: 0.2058 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.3150 | 0.0425 | 1.0714 | 7.3695 | 0.0425 | 0.0339 | 0.2866 | 0.9549 | | No log | 2.0 | 14 | 3.3516 | 0.1 | 0.9507 | 5.8558 | 0.1000 | 0.0866 | 0.1647 | 0.8831 | | No log | 3.0 | 21 | 2.9890 | 0.225 | 0.8838 | 5.3580 | 0.225 | 0.1805 | 0.1576 | 0.6316 | | No log | 4.0 | 28 | 2.5376 | 0.29 | 0.7946 | 3.5543 | 0.29 | 0.2749 | 0.1832 | 0.4807 | | No log | 5.0 | 35 | 2.2193 | 0.3875 | 0.7186 | 3.0794 | 0.3875 | 0.3677 | 0.1577 | 0.3531 | | No log | 6.0 | 42 | 2.0818 | 0.43 | 0.6905 | 2.9853 | 0.4300 | 0.4165 | 0.1668 | 0.3056 | | No log | 7.0 | 49 | 2.1032 | 0.45 | 0.7019 | 3.1044 | 0.45 | 0.4208 | 0.2121 | 0.2997 | | No log | 8.0 | 56 | 2.0360 | 0.455 | 0.6994 | 2.8491 | 0.455 | 0.4492 | 0.2131 | 0.3026 | | No log | 9.0 | 63 | 2.1719 | 0.475 | 0.7092 | 2.7831 | 0.4750 | 0.4549 | 0.2332 | 0.2870 | | No log | 10.0 | 70 | 2.1820 | 0.4525 | 0.7393 | 2.8185 | 0.4525 | 0.4318 | 0.2813 | 0.2994 | | No log | 11.0 | 77 | 2.2659 | 0.4475 | 0.7485 | 2.8020 | 0.4475 | 0.4227 | 0.2677 | 0.3046 | | No log | 12.0 | 84 | 2.1798 | 0.4575 | 0.7325 | 2.6772 | 0.4575 | 0.4555 | 0.2738 | 0.3081 | | No log | 13.0 | 91 | 2.3031 | 0.465 | 0.7431 | 2.8956 | 0.465 | 0.4390 | 0.2771 | 0.2945 | | No log | 14.0 | 98 | 2.0867 | 0.49 | 0.7048 | 2.5312 | 0.49 | 0.4823 | 0.2528 | 0.2921 | | No log | 15.0 | 105 | 2.1671 | 0.49 | 0.7218 | 2.7779 | 0.49 | 0.4749 | 0.2396 | 0.2877 | | No log | 16.0 | 112 | 2.0091 | 0.485 | 0.6857 | 2.7234 | 0.485 | 0.4608 | 0.2493 | 0.2577 | | No log | 17.0 | 119 | 1.9494 | 0.515 | 0.6714 | 2.4841 | 0.515 | 0.5072 | 0.2380 | 0.2614 | | No log | 18.0 | 126 | 1.9132 | 0.505 | 0.6665 | 2.4777 | 0.505 | 0.4945 | 0.2206 | 0.2622 | | No log | 19.0 | 133 | 2.0539 | 0.505 | 0.6776 | 2.7885 | 0.505 | 0.4986 | 0.2209 | 0.2724 | | No log | 20.0 | 140 | 1.9533 | 0.5125 | 0.6666 | 2.7287 | 0.5125 | 0.5044 | 0.2385 | 0.2645 | | No log | 21.0 | 147 | 1.9038 | 0.525 | 0.6365 | 2.8345 | 0.525 | 0.5099 | 0.2021 | 0.2290 | | No log | 22.0 | 154 | 1.8525 | 0.5075 | 0.6448 | 2.6337 | 0.5075 | 0.4958 | 0.2083 | 0.2494 | | No log | 23.0 | 161 | 1.7880 | 0.51 | 0.6386 | 2.4856 | 0.51 | 0.5078 | 0.2186 | 0.2478 | | No log | 24.0 | 168 | 1.8363 | 0.505 | 0.6456 | 2.5075 | 0.505 | 0.4966 | 0.1962 | 0.2399 | | No log | 25.0 | 175 | 1.9655 | 0.4725 | 0.6864 | 2.6331 | 0.4725 | 0.4608 | 0.2291 | 0.2669 | | No log | 26.0 | 182 | 1.8660 | 0.5175 | 0.6547 | 2.5404 | 0.5175 | 0.5076 | 0.2252 | 0.2489 | | No log | 27.0 | 189 | 1.8693 | 0.525 | 0.6446 | 2.6230 | 0.525 | 0.5145 | 0.2047 | 0.2540 | | No log | 28.0 | 196 | 1.8113 | 0.51 | 0.6407 | 2.4380 | 0.51 | 0.4978 | 0.2030 | 0.2454 | | No log | 29.0 | 203 | 1.8313 | 0.53 | 0.6445 | 2.4777 | 0.53 | 0.5284 | 0.2071 | 0.2575 | | No log | 30.0 | 210 | 1.7310 | 0.5425 | 0.6197 | 2.4559 | 0.5425 | 0.5384 | 0.1869 | 0.2367 | | No log | 31.0 | 217 | 1.8023 | 0.5325 | 0.6351 | 2.5026 | 0.5325 | 0.5216 | 0.2081 | 0.2496 | | No log | 32.0 | 224 | 1.7652 | 0.5325 | 0.6186 | 2.4794 | 0.5325 | 0.5145 | 0.1715 | 0.2338 | | No log | 33.0 | 231 | 1.7583 | 0.525 | 0.6363 | 2.4889 | 0.525 | 0.5275 | 0.1984 | 0.2463 | | No log | 34.0 | 238 | 1.7552 | 0.5475 | 0.6164 | 2.4291 | 0.5475 | 0.5305 | 0.2026 | 0.2377 | | No log | 35.0 | 245 | 1.6839 | 0.5375 | 0.6085 | 2.5915 | 0.5375 | 0.5253 | 0.1828 | 0.2214 | | No log | 36.0 | 252 | 1.7480 | 0.5425 | 0.6104 | 2.5809 | 0.5425 | 0.5366 | 0.1716 | 0.2232 | | No log | 37.0 | 259 | 1.7534 | 0.525 | 0.6225 | 2.3614 | 0.525 | 0.5183 | 0.1930 | 0.2249 | | No log | 38.0 | 266 | 1.7484 | 0.5425 | 0.6125 | 2.5714 | 0.5425 | 0.5282 | 0.1792 | 0.2272 | | No log | 39.0 | 273 | 1.7073 | 0.55 | 0.6172 | 2.4200 | 0.55 | 0.5370 | 0.1902 | 0.2314 | | No log | 40.0 | 280 | 1.7303 | 0.55 | 0.6134 | 2.4829 | 0.55 | 0.5394 | 0.1916 | 0.2324 | | No log | 41.0 | 287 | 1.6684 | 0.54 | 0.6060 | 2.4632 | 0.54 | 0.5350 | 0.2028 | 0.2251 | | No log | 42.0 | 294 | 1.7171 | 0.5375 | 0.6055 | 2.4705 | 0.5375 | 0.5213 | 0.1776 | 0.2262 | | No log | 43.0 | 301 | 1.6493 | 0.545 | 0.5991 | 2.5207 | 0.545 | 0.5412 | 0.1779 | 0.2214 | | No log | 44.0 | 308 | 1.6548 | 0.5625 | 0.5920 | 2.4810 | 0.5625 | 0.5568 | 0.1892 | 0.2182 | | No log | 45.0 | 315 | 1.6392 | 0.565 | 0.5943 | 2.3771 | 0.565 | 0.5586 | 0.2165 | 0.2162 | | No log | 46.0 | 322 | 1.6923 | 0.5225 | 0.6159 | 2.3661 | 0.5225 | 0.5158 | 0.1775 | 0.2400 | | No log | 47.0 | 329 | 1.6266 | 0.5525 | 0.5827 | 2.4385 | 0.5525 | 0.5468 | 0.1845 | 0.2100 | | No log | 48.0 | 336 | 1.6804 | 0.55 | 0.6019 | 2.3884 | 0.55 | 0.5481 | 0.1895 | 0.2291 | | No log | 49.0 | 343 | 1.6202 | 0.5725 | 0.5847 | 2.4882 | 0.5725 | 0.5596 | 0.1642 | 0.2125 | | No log | 50.0 | 350 | 1.6222 | 0.54 | 0.5882 | 2.4144 | 0.54 | 0.5311 | 0.1830 | 0.2226 | | No log | 51.0 | 357 | 1.6119 | 0.5775 | 0.5794 | 2.4063 | 0.5775 | 0.5731 | 0.1647 | 0.2019 | | No log | 52.0 | 364 | 1.5958 | 0.57 | 0.5757 | 2.3342 | 0.57 | 0.5642 | 0.1778 | 0.2094 | | No log | 53.0 | 371 | 1.6206 | 0.545 | 0.5913 | 2.3884 | 0.545 | 0.5365 | 0.1799 | 0.2187 | | No log | 54.0 | 378 | 1.5982 | 0.5675 | 0.5745 | 2.4276 | 0.5675 | 0.5640 | 0.1746 | 0.2050 | | No log | 55.0 | 385 | 1.6258 | 0.5525 | 0.5856 | 2.4005 | 0.5525 | 0.5373 | 0.1890 | 0.2124 | | No log | 56.0 | 392 | 1.5763 | 0.57 | 0.5744 | 2.4477 | 0.57 | 0.5729 | 0.1651 | 0.2081 | | No log | 57.0 | 399 | 1.6249 | 0.5525 | 0.5861 | 2.3791 | 0.5525 | 0.5432 | 0.1531 | 0.2114 | | No log | 58.0 | 406 | 1.6240 | 0.5775 | 0.5791 | 2.4540 | 0.5775 | 0.5730 | 0.1582 | 0.2054 | | No log | 59.0 | 413 | 1.6149 | 0.545 | 0.5851 | 2.3134 | 0.545 | 0.5395 | 0.1870 | 0.2137 | | No log | 60.0 | 420 | 1.6163 | 0.5775 | 0.5792 | 2.3778 | 0.5775 | 0.5708 | 0.1762 | 0.2076 | | No log | 61.0 | 427 | 1.6132 | 0.5575 | 0.5868 | 2.3759 | 0.5575 | 0.5530 | 0.1842 | 0.2159 | | No log | 62.0 | 434 | 1.5940 | 0.5725 | 0.5756 | 2.3394 | 0.5725 | 0.5731 | 0.2102 | 0.2054 | | No log | 63.0 | 441 | 1.6167 | 0.56 | 0.5841 | 2.4117 | 0.56 | 0.5541 | 0.1806 | 0.2160 | | No log | 64.0 | 448 | 1.5988 | 0.57 | 0.5775 | 2.3388 | 0.57 | 0.5667 | 0.1680 | 0.2064 | | No log | 65.0 | 455 | 1.5893 | 0.5725 | 0.5752 | 2.4281 | 0.5725 | 0.5695 | 0.1624 | 0.2050 | | No log | 66.0 | 462 | 1.5975 | 0.5725 | 0.5737 | 2.3760 | 0.5725 | 0.5662 | 0.1733 | 0.2026 | | No log | 67.0 | 469 | 1.5903 | 0.57 | 0.5772 | 2.2921 | 0.57 | 0.5675 | 0.1888 | 0.2112 | | No log | 68.0 | 476 | 1.5878 | 0.575 | 0.5730 | 2.3676 | 0.575 | 0.5706 | 0.1683 | 0.2039 | | No log | 69.0 | 483 | 1.5950 | 0.57 | 0.5775 | 2.3006 | 0.57 | 0.5641 | 0.1639 | 0.2076 | | No log | 70.0 | 490 | 1.5916 | 0.58 | 0.5728 | 2.3424 | 0.58 | 0.5769 | 0.1714 | 0.2026 | | No log | 71.0 | 497 | 1.5960 | 0.5675 | 0.5784 | 2.3057 | 0.5675 | 0.5624 | 0.1600 | 0.2073 | | 0.3705 | 72.0 | 504 | 1.5907 | 0.575 | 0.5755 | 2.3322 | 0.575 | 0.5723 | 0.1578 | 0.2066 | | 0.3705 | 73.0 | 511 | 1.5918 | 0.5675 | 0.5762 | 2.3182 | 0.5675 | 0.5605 | 0.1942 | 0.2071 | | 0.3705 | 74.0 | 518 | 1.5894 | 0.585 | 0.5747 | 2.3335 | 0.585 | 0.5818 | 0.1739 | 0.2035 | | 0.3705 | 75.0 | 525 | 1.5878 | 0.565 | 0.5750 | 2.3019 | 0.565 | 0.5607 | 0.1649 | 0.2060 | | 0.3705 | 76.0 | 532 | 1.5923 | 0.575 | 0.5742 | 2.3376 | 0.575 | 0.5699 | 0.1779 | 0.2048 | | 0.3705 | 77.0 | 539 | 1.5891 | 0.565 | 0.5760 | 2.2978 | 0.565 | 0.5616 | 0.1691 | 0.2066 | | 0.3705 | 78.0 | 546 | 1.5896 | 0.575 | 0.5738 | 2.3748 | 0.575 | 0.5703 | 0.1733 | 0.2048 | | 0.3705 | 79.0 | 553 | 1.5901 | 0.5675 | 0.5757 | 2.3039 | 0.5675 | 0.5634 | 0.1710 | 0.2064 | | 0.3705 | 80.0 | 560 | 1.5906 | 0.57 | 0.5746 | 2.3125 | 0.57 | 0.5657 | 0.1692 | 0.2054 | | 0.3705 | 81.0 | 567 | 1.5907 | 0.57 | 0.5751 | 2.3097 | 0.57 | 0.5659 | 0.1600 | 0.2047 | | 0.3705 | 82.0 | 574 | 1.5902 | 0.57 | 0.5746 | 2.3072 | 0.57 | 0.5657 | 0.1797 | 0.2055 | | 0.3705 | 83.0 | 581 | 1.5906 | 0.5725 | 0.5746 | 2.3145 | 0.5725 | 0.5681 | 0.1547 | 0.2050 | | 0.3705 | 84.0 | 588 | 1.5909 | 0.5725 | 0.5750 | 2.3057 | 0.5725 | 0.5684 | 0.1746 | 0.2055 | | 0.3705 | 85.0 | 595 | 1.5906 | 0.57 | 0.5746 | 2.3098 | 0.57 | 0.5661 | 0.1721 | 0.2054 | | 0.3705 | 86.0 | 602 | 1.5916 | 0.57 | 0.5749 | 2.3093 | 0.57 | 0.5661 | 0.1659 | 0.2058 | | 0.3705 | 87.0 | 609 | 1.5913 | 0.57 | 0.5748 | 2.3084 | 0.57 | 0.5661 | 0.1631 | 0.2058 | | 0.3705 | 88.0 | 616 | 1.5918 | 0.57 | 0.5749 | 2.3082 | 0.57 | 0.5661 | 0.1652 | 0.2058 | | 0.3705 | 89.0 | 623 | 1.5919 | 0.57 | 0.5750 | 2.3084 | 0.57 | 0.5661 | 0.1658 | 0.2059 | | 0.3705 | 90.0 | 630 | 1.5918 | 0.5725 | 0.5749 | 2.3087 | 0.5725 | 0.5685 | 0.1650 | 0.2056 | | 0.3705 | 91.0 | 637 | 1.5921 | 0.57 | 0.5750 | 2.3076 | 0.57 | 0.5661 | 0.1549 | 0.2059 | | 0.3705 | 92.0 | 644 | 1.5920 | 0.57 | 0.5750 | 2.3079 | 0.57 | 0.5661 | 0.1581 | 0.2058 | | 0.3705 | 93.0 | 651 | 1.5917 | 0.57 | 0.5749 | 2.3080 | 0.57 | 0.5661 | 0.1680 | 0.2057 | | 0.3705 | 94.0 | 658 | 1.5923 | 0.57 | 0.5750 | 2.3083 | 0.57 | 0.5661 | 0.1643 | 0.2058 | | 0.3705 | 95.0 | 665 | 1.5924 | 0.57 | 0.5751 | 2.3085 | 0.57 | 0.5661 | 0.1543 | 0.2059 | | 0.3705 | 96.0 | 672 | 1.5922 | 0.57 | 0.5750 | 2.3085 | 0.57 | 0.5661 | 0.1530 | 0.2058 | | 0.3705 | 97.0 | 679 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1688 | 0.2058 | | 0.3705 | 98.0 | 686 | 1.5923 | 0.57 | 0.5749 | 2.3089 | 0.57 | 0.5661 | 0.1733 | 0.2058 | | 0.3705 | 99.0 | 693 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1735 | 0.2058 | | 0.3705 | 100.0 | 700 | 1.5923 | 0.57 | 0.5750 | 2.3088 | 0.57 | 0.5661 | 0.1722 | 0.2058 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5908 - Accuracy: 0.54 - Brier Loss: 0.6121 - Nll: 2.4999 - F1 Micro: 0.54 - F1 Macro: 0.5334 - Ece: 0.1895 - Aurc: 0.2228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 3.9334 | 0.0425 | 1.0719 | 7.3494 | 0.0425 | 0.0341 | 0.2781 | 0.9557 | | No log | 2.0 | 14 | 3.0206 | 0.09 | 0.9526 | 5.9271 | 0.09 | 0.0682 | 0.1659 | 0.8896 | | No log | 3.0 | 21 | 2.6955 | 0.2275 | 0.8867 | 5.4041 | 0.2275 | 0.1804 | 0.1637 | 0.6351 | | No log | 4.0 | 28 | 2.3029 | 0.29 | 0.8005 | 3.5699 | 0.29 | 0.2727 | 0.1512 | 0.4877 | | No log | 5.0 | 35 | 2.0293 | 0.37 | 0.7219 | 2.9856 | 0.37 | 0.3519 | 0.1495 | 0.3632 | | No log | 6.0 | 42 | 1.9194 | 0.44 | 0.6926 | 2.9746 | 0.44 | 0.4240 | 0.1778 | 0.3091 | | No log | 7.0 | 49 | 1.9640 | 0.4675 | 0.7000 | 3.0857 | 0.4675 | 0.4390 | 0.2009 | 0.3004 | | No log | 8.0 | 56 | 1.9049 | 0.4625 | 0.7003 | 2.8472 | 0.4625 | 0.4602 | 0.2053 | 0.3051 | | No log | 9.0 | 63 | 2.0561 | 0.4675 | 0.7168 | 2.8229 | 0.4675 | 0.4501 | 0.2288 | 0.2994 | | No log | 10.0 | 70 | 2.1002 | 0.45 | 0.7433 | 2.7915 | 0.45 | 0.4234 | 0.2691 | 0.3022 | | No log | 11.0 | 77 | 2.2528 | 0.4525 | 0.7686 | 3.0103 | 0.4525 | 0.4320 | 0.2921 | 0.3183 | | No log | 12.0 | 84 | 2.1190 | 0.475 | 0.7427 | 2.6715 | 0.4750 | 0.4660 | 0.2832 | 0.3077 | | No log | 13.0 | 91 | 2.3102 | 0.445 | 0.7825 | 2.9698 | 0.445 | 0.4252 | 0.3093 | 0.3100 | | No log | 14.0 | 98 | 2.3501 | 0.42 | 0.8145 | 2.7585 | 0.4200 | 0.4248 | 0.3206 | 0.3662 | | No log | 15.0 | 105 | 2.2402 | 0.495 | 0.7423 | 3.0313 | 0.495 | 0.4702 | 0.2692 | 0.2818 | | No log | 16.0 | 112 | 2.2266 | 0.49 | 0.7349 | 2.8824 | 0.49 | 0.4714 | 0.2763 | 0.2895 | | No log | 17.0 | 119 | 2.2989 | 0.4725 | 0.7509 | 3.0951 | 0.4725 | 0.4499 | 0.2863 | 0.2855 | | No log | 18.0 | 126 | 2.1355 | 0.47 | 0.7322 | 2.9349 | 0.47 | 0.4616 | 0.2725 | 0.2845 | | No log | 19.0 | 133 | 2.0965 | 0.505 | 0.7067 | 2.8254 | 0.505 | 0.4956 | 0.2523 | 0.2757 | | No log | 20.0 | 140 | 2.1961 | 0.485 | 0.7358 | 3.1604 | 0.485 | 0.4567 | 0.2825 | 0.2841 | | No log | 21.0 | 147 | 2.1287 | 0.5025 | 0.7247 | 2.5998 | 0.5025 | 0.5074 | 0.2703 | 0.3064 | | No log | 22.0 | 154 | 2.2280 | 0.4675 | 0.7760 | 2.8571 | 0.4675 | 0.4636 | 0.2911 | 0.3232 | | No log | 23.0 | 161 | 1.9649 | 0.5025 | 0.6828 | 2.8224 | 0.5025 | 0.4970 | 0.2410 | 0.2633 | | No log | 24.0 | 168 | 1.9361 | 0.5125 | 0.6780 | 2.7309 | 0.5125 | 0.5035 | 0.2326 | 0.2553 | | No log | 25.0 | 175 | 2.0161 | 0.5 | 0.6980 | 2.9958 | 0.5 | 0.4912 | 0.2580 | 0.2556 | | No log | 26.0 | 182 | 1.8763 | 0.5025 | 0.6624 | 2.8291 | 0.5025 | 0.4952 | 0.2305 | 0.2431 | | No log | 27.0 | 189 | 1.9057 | 0.525 | 0.6793 | 2.5627 | 0.525 | 0.5174 | 0.2161 | 0.2634 | | No log | 28.0 | 196 | 1.8529 | 0.52 | 0.6683 | 2.7191 | 0.52 | 0.5132 | 0.2375 | 0.2535 | | No log | 29.0 | 203 | 1.9603 | 0.5125 | 0.6831 | 2.7822 | 0.5125 | 0.5076 | 0.2395 | 0.2657 | | No log | 30.0 | 210 | 1.8247 | 0.52 | 0.6533 | 2.8547 | 0.52 | 0.5058 | 0.2080 | 0.2426 | | No log | 31.0 | 217 | 1.8275 | 0.5125 | 0.6547 | 2.6194 | 0.5125 | 0.5032 | 0.2208 | 0.2488 | | No log | 32.0 | 224 | 1.8003 | 0.52 | 0.6455 | 2.6138 | 0.52 | 0.5124 | 0.2302 | 0.2370 | | No log | 33.0 | 231 | 1.8714 | 0.505 | 0.6694 | 2.6643 | 0.505 | 0.4970 | 0.2195 | 0.2553 | | No log | 34.0 | 238 | 1.8018 | 0.5075 | 0.6659 | 2.5423 | 0.5075 | 0.4978 | 0.2241 | 0.2515 | | No log | 35.0 | 245 | 1.7844 | 0.5225 | 0.6503 | 2.6100 | 0.5225 | 0.5181 | 0.2181 | 0.2435 | | No log | 36.0 | 252 | 1.8321 | 0.5225 | 0.6674 | 2.7821 | 0.5225 | 0.5020 | 0.2285 | 0.2462 | | No log | 37.0 | 259 | 1.7859 | 0.4975 | 0.6725 | 2.6066 | 0.4975 | 0.4974 | 0.2351 | 0.2627 | | No log | 38.0 | 266 | 1.7790 | 0.5125 | 0.6595 | 2.6983 | 0.5125 | 0.5023 | 0.2172 | 0.2497 | | No log | 39.0 | 273 | 1.6989 | 0.5225 | 0.6401 | 2.6743 | 0.5225 | 0.5151 | 0.2100 | 0.2407 | | No log | 40.0 | 280 | 1.7568 | 0.52 | 0.6488 | 2.5294 | 0.52 | 0.5132 | 0.2208 | 0.2442 | | No log | 41.0 | 287 | 1.6896 | 0.5275 | 0.6362 | 2.5489 | 0.5275 | 0.5141 | 0.2045 | 0.2323 | | No log | 42.0 | 294 | 1.7193 | 0.5275 | 0.6517 | 2.5525 | 0.5275 | 0.5232 | 0.1986 | 0.2467 | | No log | 43.0 | 301 | 1.7199 | 0.535 | 0.6403 | 2.5974 | 0.535 | 0.5279 | 0.2104 | 0.2432 | | No log | 44.0 | 308 | 1.6594 | 0.5375 | 0.6330 | 2.4854 | 0.5375 | 0.5316 | 0.2015 | 0.2321 | | No log | 45.0 | 315 | 1.6543 | 0.5275 | 0.6239 | 2.4955 | 0.5275 | 0.5223 | 0.2144 | 0.2308 | | No log | 46.0 | 322 | 1.6490 | 0.5425 | 0.6262 | 2.5215 | 0.5425 | 0.5358 | 0.2104 | 0.2273 | | No log | 47.0 | 329 | 1.6570 | 0.54 | 0.6233 | 2.5454 | 0.54 | 0.5380 | 0.2047 | 0.2301 | | No log | 48.0 | 336 | 1.6359 | 0.5375 | 0.6218 | 2.5546 | 0.5375 | 0.5320 | 0.2171 | 0.2257 | | No log | 49.0 | 343 | 1.6320 | 0.55 | 0.6214 | 2.4958 | 0.55 | 0.5452 | 0.2014 | 0.2267 | | No log | 50.0 | 350 | 1.6230 | 0.53 | 0.6208 | 2.4979 | 0.53 | 0.5243 | 0.2017 | 0.2315 | | No log | 51.0 | 357 | 1.6374 | 0.535 | 0.6257 | 2.4644 | 0.535 | 0.5293 | 0.2038 | 0.2286 | | No log | 52.0 | 364 | 1.6190 | 0.5375 | 0.6199 | 2.5279 | 0.5375 | 0.5310 | 0.1855 | 0.2290 | | No log | 53.0 | 371 | 1.6155 | 0.5475 | 0.6158 | 2.4738 | 0.5475 | 0.5435 | 0.1913 | 0.2239 | | No log | 54.0 | 378 | 1.6131 | 0.5425 | 0.6184 | 2.4982 | 0.5425 | 0.5377 | 0.1969 | 0.2248 | | No log | 55.0 | 385 | 1.6035 | 0.545 | 0.6138 | 2.4690 | 0.545 | 0.5406 | 0.2164 | 0.2223 | | No log | 56.0 | 392 | 1.5990 | 0.54 | 0.6153 | 2.4701 | 0.54 | 0.5356 | 0.2019 | 0.2249 | | No log | 57.0 | 399 | 1.6024 | 0.5425 | 0.6153 | 2.4626 | 0.5425 | 0.5375 | 0.1826 | 0.2237 | | No log | 58.0 | 406 | 1.5935 | 0.545 | 0.6141 | 2.4390 | 0.545 | 0.5415 | 0.1933 | 0.2238 | | No log | 59.0 | 413 | 1.6016 | 0.545 | 0.6137 | 2.4640 | 0.545 | 0.5401 | 0.2021 | 0.2230 | | No log | 60.0 | 420 | 1.5976 | 0.54 | 0.6146 | 2.4618 | 0.54 | 0.5355 | 0.1912 | 0.2245 | | No log | 61.0 | 427 | 1.5984 | 0.545 | 0.6133 | 2.4683 | 0.545 | 0.5408 | 0.1971 | 0.2228 | | No log | 62.0 | 434 | 1.5941 | 0.54 | 0.6131 | 2.4639 | 0.54 | 0.5358 | 0.1898 | 0.2236 | | No log | 63.0 | 441 | 1.5936 | 0.545 | 0.6123 | 2.4689 | 0.545 | 0.5404 | 0.1953 | 0.2222 | | No log | 64.0 | 448 | 1.5970 | 0.5425 | 0.6138 | 2.4647 | 0.5425 | 0.5384 | 0.2015 | 0.2238 | | No log | 65.0 | 455 | 1.5943 | 0.545 | 0.6130 | 2.4963 | 0.545 | 0.5400 | 0.1979 | 0.2229 | | No log | 66.0 | 462 | 1.5936 | 0.545 | 0.6127 | 2.4977 | 0.545 | 0.5400 | 0.1933 | 0.2229 | | No log | 67.0 | 469 | 1.5928 | 0.5425 | 0.6127 | 2.4965 | 0.5425 | 0.5381 | 0.1976 | 0.2233 | | No log | 68.0 | 476 | 1.5946 | 0.5425 | 0.6128 | 2.4768 | 0.5425 | 0.5383 | 0.2149 | 0.2233 | | No log | 69.0 | 483 | 1.5924 | 0.54 | 0.6126 | 2.4946 | 0.54 | 0.5356 | 0.2094 | 0.2233 | | No log | 70.0 | 490 | 1.5921 | 0.54 | 0.6120 | 2.4964 | 0.54 | 0.5356 | 0.1801 | 0.2230 | | No log | 71.0 | 497 | 1.5926 | 0.54 | 0.6126 | 2.4955 | 0.54 | 0.5356 | 0.2039 | 0.2235 | | 0.3138 | 72.0 | 504 | 1.5916 | 0.5425 | 0.6121 | 2.4964 | 0.5425 | 0.5366 | 0.1898 | 0.2229 | | 0.3138 | 73.0 | 511 | 1.5917 | 0.54 | 0.6119 | 2.4966 | 0.54 | 0.5356 | 0.2039 | 0.2231 | | 0.3138 | 74.0 | 518 | 1.5918 | 0.54 | 0.6123 | 2.4964 | 0.54 | 0.5351 | 0.2035 | 0.2229 | | 0.3138 | 75.0 | 525 | 1.5912 | 0.54 | 0.6118 | 2.4975 | 0.54 | 0.5351 | 0.2059 | 0.2228 | | 0.3138 | 76.0 | 532 | 1.5918 | 0.54 | 0.6124 | 2.4965 | 0.54 | 0.5351 | 0.1971 | 0.2231 | | 0.3138 | 77.0 | 539 | 1.5919 | 0.5425 | 0.6120 | 2.4974 | 0.5425 | 0.5358 | 0.2087 | 0.2227 | | 0.3138 | 78.0 | 546 | 1.5903 | 0.54 | 0.6118 | 2.4978 | 0.54 | 0.5341 | 0.2169 | 0.2228 | | 0.3138 | 79.0 | 553 | 1.5922 | 0.54 | 0.6124 | 2.4976 | 0.54 | 0.5351 | 0.2109 | 0.2234 | | 0.3138 | 80.0 | 560 | 1.5914 | 0.54 | 0.6122 | 2.4983 | 0.54 | 0.5345 | 0.2041 | 0.2228 | | 0.3138 | 81.0 | 567 | 1.5907 | 0.54 | 0.6119 | 2.4981 | 0.54 | 0.5345 | 0.2128 | 0.2226 | | 0.3138 | 82.0 | 574 | 1.5921 | 0.5425 | 0.6124 | 2.4986 | 0.5425 | 0.5362 | 0.2084 | 0.2227 | | 0.3138 | 83.0 | 581 | 1.5918 | 0.5425 | 0.6125 | 2.4987 | 0.5425 | 0.5362 | 0.2038 | 0.2230 | | 0.3138 | 84.0 | 588 | 1.5902 | 0.54 | 0.6120 | 2.4989 | 0.54 | 0.5345 | 0.2043 | 0.2226 | | 0.3138 | 85.0 | 595 | 1.5919 | 0.5425 | 0.6124 | 2.4988 | 0.5425 | 0.5360 | 0.1998 | 0.2228 | | 0.3138 | 86.0 | 602 | 1.5916 | 0.5425 | 0.6124 | 2.4990 | 0.5425 | 0.5362 | 0.2079 | 0.2227 | | 0.3138 | 87.0 | 609 | 1.5906 | 0.54 | 0.6120 | 2.4990 | 0.54 | 0.5345 | 0.2037 | 0.2227 | | 0.3138 | 88.0 | 616 | 1.5908 | 0.54 | 0.6120 | 2.4989 | 0.54 | 0.5345 | 0.2091 | 0.2230 | | 0.3138 | 89.0 | 623 | 1.5909 | 0.54 | 0.6120 | 2.4995 | 0.54 | 0.5344 | 0.2113 | 0.2228 | | 0.3138 | 90.0 | 630 | 1.5906 | 0.54 | 0.6119 | 2.4996 | 0.54 | 0.5345 | 0.1969 | 0.2228 | | 0.3138 | 91.0 | 637 | 1.5911 | 0.5425 | 0.6121 | 2.4999 | 0.5425 | 0.5360 | 0.1954 | 0.2226 | | 0.3138 | 92.0 | 644 | 1.5909 | 0.54 | 0.6121 | 2.4994 | 0.54 | 0.5344 | 0.1928 | 0.2228 | | 0.3138 | 93.0 | 651 | 1.5907 | 0.5425 | 0.6121 | 2.4999 | 0.5425 | 0.5360 | 0.2034 | 0.2225 | | 0.3138 | 94.0 | 658 | 1.5910 | 0.5425 | 0.6122 | 2.4996 | 0.5425 | 0.5360 | 0.1974 | 0.2227 | | 0.3138 | 95.0 | 665 | 1.5909 | 0.5375 | 0.6121 | 2.4995 | 0.5375 | 0.5319 | 0.1990 | 0.2230 | | 0.3138 | 96.0 | 672 | 1.5907 | 0.5375 | 0.6120 | 2.4997 | 0.5375 | 0.5318 | 0.1980 | 0.2229 | | 0.3138 | 97.0 | 679 | 1.5907 | 0.54 | 0.6120 | 2.4998 | 0.54 | 0.5344 | 0.1900 | 0.2228 | | 0.3138 | 98.0 | 686 | 1.5907 | 0.5425 | 0.6120 | 2.4999 | 0.5425 | 0.5362 | 0.1899 | 0.2226 | | 0.3138 | 99.0 | 693 | 1.5908 | 0.54 | 0.6121 | 2.4999 | 0.54 | 0.5334 | 0.1936 | 0.2228 | | 0.3138 | 100.0 | 700 | 1.5908 | 0.54 | 0.6121 | 2.4999 | 0.54 | 0.5334 | 0.1895 | 0.2228 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

ayanban011/vit-base_tobacco

# vit-base_tobacco This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7324 - Accuracy: 0.8 - Brier Loss: 0.3049 - Nll: 1.3070 - F1 Micro: 0.8000 - F1 Macro: 0.7733 - Ece: 0.2124 - Aurc: 0.0840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 40 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 640 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.8 | 1 | 0.7434 | 0.815 | 0.3073 | 1.1863 | 0.815 | 0.7942 | 0.2217 | 0.0720 | | No log | 1.6 | 2 | 0.7569 | 0.81 | 0.3117 | 1.2131 | 0.81 | 0.7893 | 0.2153 | 0.0800 | | No log | 2.4 | 3 | 0.7491 | 0.82 | 0.3107 | 1.2631 | 0.82 | 0.8063 | 0.2311 | 0.0777 | | No log | 4.0 | 5 | 0.7489 | 0.795 | 0.3088 | 1.1544 | 0.795 | 0.7766 | 0.2427 | 0.0730 | | No log | 4.8 | 6 | 0.7658 | 0.81 | 0.3171 | 1.3766 | 0.81 | 0.7983 | 0.2434 | 0.0822 | | No log | 5.6 | 7 | 0.7496 | 0.815 | 0.3097 | 1.1920 | 0.815 | 0.8014 | 0.2434 | 0.0848 | | No log | 6.4 | 8 | 0.7468 | 0.8 | 0.3090 | 1.0732 | 0.8000 | 0.7750 | 0.2195 | 0.0774 | | No log | 8.0 | 10 | 0.7563 | 0.815 | 0.3131 | 1.3472 | 0.815 | 0.8082 | 0.2255 | 0.0741 | | No log | 8.8 | 11 | 0.7548 | 0.81 | 0.3116 | 1.2016 | 0.81 | 0.7930 | 0.2496 | 0.0868 | | No log | 9.6 | 12 | 0.7395 | 0.805 | 0.3071 | 1.1664 | 0.805 | 0.7841 | 0.2432 | 0.0772 | | No log | 10.4 | 13 | 0.7296 | 0.82 | 0.3018 | 1.1776 | 0.82 | 0.8078 | 0.2214 | 0.0676 | | No log | 12.0 | 15 | 0.7515 | 0.815 | 0.3104 | 1.2034 | 0.815 | 0.7987 | 0.2307 | 0.0835 | | No log | 12.8 | 16 | 0.7350 | 0.81 | 0.3053 | 1.1762 | 0.81 | 0.7978 | 0.2196 | 0.0747 | | No log | 13.6 | 17 | 0.7281 | 0.805 | 0.3023 | 1.1664 | 0.805 | 0.7841 | 0.2144 | 0.0718 | | No log | 14.4 | 18 | 0.7395 | 0.81 | 0.3064 | 1.1750 | 0.81 | 0.7871 | 0.2306 | 0.0778 | | No log | 16.0 | 20 | 0.7427 | 0.81 | 0.3076 | 1.2637 | 0.81 | 0.7986 | 0.2194 | 0.0808 | | No log | 16.8 | 21 | 0.7337 | 0.81 | 0.3044 | 1.2447 | 0.81 | 0.7948 | 0.2321 | 0.0743 | | No log | 17.6 | 22 | 0.7340 | 0.805 | 0.3050 | 1.1681 | 0.805 | 0.7841 | 0.2307 | 0.0743 | | No log | 18.4 | 23 | 0.7338 | 0.805 | 0.3047 | 1.1708 | 0.805 | 0.7841 | 0.2290 | 0.0759 | | No log | 20.0 | 25 | 0.7390 | 0.815 | 0.3058 | 1.2551 | 0.815 | 0.7984 | 0.2489 | 0.0818 | | No log | 20.8 | 26 | 0.7390 | 0.815 | 0.3063 | 1.1894 | 0.815 | 0.7984 | 0.2294 | 0.0818 | | No log | 21.6 | 27 | 0.7349 | 0.805 | 0.3054 | 1.1714 | 0.805 | 0.7847 | 0.2011 | 0.0791 | | No log | 22.4 | 28 | 0.7308 | 0.81 | 0.3037 | 1.1694 | 0.81 | 0.7948 | 0.2128 | 0.0766 | | No log | 24.0 | 30 | 0.7353 | 0.81 | 0.3051 | 1.1852 | 0.81 | 0.7956 | 0.2282 | 0.0794 | | No log | 24.8 | 31 | 0.7378 | 0.81 | 0.3062 | 1.1870 | 0.81 | 0.7956 | 0.2293 | 0.0819 | | No log | 25.6 | 32 | 0.7356 | 0.81 | 0.3054 | 1.1863 | 0.81 | 0.7956 | 0.2287 | 0.0817 | | No log | 26.4 | 33 | 0.7309 | 0.81 | 0.3037 | 1.1801 | 0.81 | 0.7954 | 0.2209 | 0.0795 | | No log | 28.0 | 35 | 0.7336 | 0.805 | 0.3050 | 1.1733 | 0.805 | 0.7850 | 0.2082 | 0.0789 | | No log | 28.8 | 36 | 0.7334 | 0.81 | 0.3045 | 1.1799 | 0.81 | 0.7956 | 0.2207 | 0.0797 | | No log | 29.6 | 37 | 0.7320 | 0.81 | 0.3040 | 1.2447 | 0.81 | 0.7956 | 0.2279 | 0.0804 | | No log | 30.4 | 38 | 0.7328 | 0.81 | 0.3045 | 1.2473 | 0.81 | 0.7956 | 0.2154 | 0.0812 | | No log | 32.0 | 40 | 0.7322 | 0.805 | 0.3044 | 1.1796 | 0.805 | 0.7850 | 0.2384 | 0.0804 | | No log | 32.8 | 41 | 0.7318 | 0.81 | 0.3045 | 1.1792 | 0.81 | 0.7954 | 0.2291 | 0.0794 | | No log | 33.6 | 42 | 0.7302 | 0.81 | 0.3034 | 1.2401 | 0.81 | 0.7954 | 0.2086 | 0.0794 | | No log | 34.4 | 43 | 0.7311 | 0.805 | 0.3036 | 1.2424 | 0.805 | 0.7850 | 0.2278 | 0.0804 | | No log | 36.0 | 45 | 0.7323 | 0.805 | 0.3043 | 1.1902 | 0.805 | 0.7850 | 0.2119 | 0.0816 | | No log | 36.8 | 46 | 0.7304 | 0.805 | 0.3034 | 1.2428 | 0.805 | 0.7850 | 0.2330 | 0.0807 | | No log | 37.6 | 47 | 0.7297 | 0.805 | 0.3032 | 1.2413 | 0.805 | 0.7850 | 0.2447 | 0.0801 | | No log | 38.4 | 48 | 0.7310 | 0.805 | 0.3039 | 1.2424 | 0.805 | 0.7850 | 0.2233 | 0.0802 | | No log | 40.0 | 50 | 0.7316 | 0.805 | 0.3040 | 1.2451 | 0.805 | 0.7850 | 0.2094 | 0.0809 | | No log | 40.8 | 51 | 0.7313 | 0.805 | 0.3041 | 1.2450 | 0.805 | 0.7850 | 0.2093 | 0.0810 | | No log | 41.6 | 52 | 0.7313 | 0.805 | 0.3041 | 1.2445 | 0.805 | 0.7850 | 0.2073 | 0.0814 | | No log | 42.4 | 53 | 0.7315 | 0.805 | 0.3040 | 1.2447 | 0.805 | 0.7850 | 0.2198 | 0.0821 | | No log | 44.0 | 55 | 0.7303 | 0.805 | 0.3034 | 1.2441 | 0.805 | 0.7850 | 0.2048 | 0.0813 | | No log | 44.8 | 56 | 0.7306 | 0.805 | 0.3038 | 1.2444 | 0.805 | 0.7850 | 0.1966 | 0.0809 | | No log | 45.6 | 57 | 0.7317 | 0.805 | 0.3043 | 1.2449 | 0.805 | 0.7850 | 0.1976 | 0.0821 | | No log | 46.4 | 58 | 0.7317 | 0.805 | 0.3041 | 1.2466 | 0.805 | 0.7850 | 0.2007 | 0.0822 | | No log | 48.0 | 60 | 0.7316 | 0.805 | 0.3041 | 1.2499 | 0.805 | 0.7850 | 0.2137 | 0.0820 | | No log | 48.8 | 61 | 0.7320 | 0.8 | 0.3043 | 1.2536 | 0.8000 | 0.7733 | 0.2081 | 0.0822 | | No log | 49.6 | 62 | 0.7319 | 0.805 | 0.3044 | 1.2494 | 0.805 | 0.7850 | 0.1998 | 0.0825 | | No log | 50.4 | 63 | 0.7326 | 0.805 | 0.3048 | 1.2476 | 0.805 | 0.7850 | 0.1936 | 0.0828 | | No log | 52.0 | 65 | 0.7313 | 0.8 | 0.3044 | 1.2495 | 0.8000 | 0.7733 | 0.2117 | 0.0822 | | No log | 52.8 | 66 | 0.7304 | 0.8 | 0.3039 | 1.2524 | 0.8000 | 0.7733 | 0.2009 | 0.0818 | | No log | 53.6 | 67 | 0.7306 | 0.8 | 0.3038 | 1.2505 | 0.8000 | 0.7733 | 0.2182 | 0.0818 | | No log | 54.4 | 68 | 0.7321 | 0.8 | 0.3044 | 1.2513 | 0.8000 | 0.7733 | 0.2185 | 0.0833 | | No log | 56.0 | 70 | 0.7326 | 0.8 | 0.3049 | 1.2519 | 0.8000 | 0.7733 | 0.2014 | 0.0833 | | No log | 56.8 | 71 | 0.7320 | 0.8 | 0.3047 | 1.2580 | 0.8000 | 0.7733 | 0.2175 | 0.0829 | | No log | 57.6 | 72 | 0.7313 | 0.8 | 0.3043 | 1.2571 | 0.8000 | 0.7733 | 0.2045 | 0.0828 | | No log | 58.4 | 73 | 0.7314 | 0.8 | 0.3043 | 1.3065 | 0.8000 | 0.7733 | 0.2038 | 0.0827 | | No log | 60.0 | 75 | 0.7322 | 0.8 | 0.3046 | 1.3081 | 0.8000 | 0.7733 | 0.2047 | 0.0840 | | No log | 60.8 | 76 | 0.7323 | 0.8 | 0.3047 | 1.3078 | 0.8000 | 0.7733 | 0.2053 | 0.0839 | | No log | 61.6 | 77 | 0.7322 | 0.8 | 0.3047 | 1.3070 | 0.8000 | 0.7733 | 0.2051 | 0.0837 | | No log | 62.4 | 78 | 0.7316 | 0.8 | 0.3045 | 1.3062 | 0.8000 | 0.7733 | 0.2145 | 0.0835 | | No log | 64.0 | 80 | 0.7315 | 0.8 | 0.3044 | 1.3063 | 0.8000 | 0.7733 | 0.2067 | 0.0836 | | No log | 64.8 | 81 | 0.7320 | 0.8 | 0.3047 | 1.3064 | 0.8000 | 0.7733 | 0.2041 | 0.0839 | | No log | 65.6 | 82 | 0.7323 | 0.8 | 0.3048 | 1.3070 | 0.8000 | 0.7733 | 0.2046 | 0.0839 | | No log | 66.4 | 83 | 0.7323 | 0.8 | 0.3048 | 1.3068 | 0.8000 | 0.7733 | 0.2045 | 0.0838 | | No log | 68.0 | 85 | 0.7320 | 0.8 | 0.3046 | 1.3068 | 0.8000 | 0.7733 | 0.2046 | 0.0840 | | No log | 68.8 | 86 | 0.7318 | 0.8 | 0.3045 | 1.3069 | 0.8000 | 0.7733 | 0.2114 | 0.0838 | | No log | 69.6 | 87 | 0.7316 | 0.8 | 0.3045 | 1.3066 | 0.8000 | 0.7733 | 0.2149 | 0.0836 | | No log | 70.4 | 88 | 0.7316 | 0.8 | 0.3045 | 1.3066 | 0.8000 | 0.7733 | 0.2244 | 0.0834 | | No log | 72.0 | 90 | 0.7321 | 0.8 | 0.3047 | 1.3069 | 0.8000 | 0.7733 | 0.2151 | 0.0837 | | No log | 72.8 | 91 | 0.7322 | 0.8 | 0.3048 | 1.3070 | 0.8000 | 0.7733 | 0.2151 | 0.0839 | | No log | 73.6 | 92 | 0.7322 | 0.8 | 0.3048 | 1.3070 | 0.8000 | 0.7733 | 0.2155 | 0.0840 | | No log | 74.4 | 93 | 0.7323 | 0.8 | 0.3048 | 1.3071 | 0.8000 | 0.7733 | 0.2129 | 0.0842 | | No log | 76.0 | 95 | 0.7324 | 0.8 | 0.3049 | 1.3071 | 0.8000 | 0.7733 | 0.2084 | 0.0841 | | No log | 76.8 | 96 | 0.7324 | 0.8 | 0.3049 | 1.3071 | 0.8000 | 0.7733 | 0.2141 | 0.0842 | | No log | 77.6 | 97 | 0.7324 | 0.8 | 0.3049 | 1.3070 | 0.8000 | 0.7733 | 0.2136 | 0.0841 | | No log | 78.4 | 98 | 0.7324 | 0.8 | 0.3049 | 1.3070 | 0.8000 | 0.7733 | 0.2136 | 0.0841 | | No log | 80.0 | 100 | 0.7324 | 0.8 | 0.3049 | 1.3070 | 0.8000 | 0.7733 | 0.2124 | 0.0840 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.5

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9682 - Accuracy: 0.5575 - Brier Loss: 0.5680 - Nll: 2.3526 - F1 Micro: 0.5575 - F1 Macro: 0.5516 - Ece: 0.1676 - Aurc: 0.1973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 5.4578 | 0.045 | 1.0710 | 7.3211 | 0.045 | 0.0342 | 0.2911 | 0.9529 | | No log | 2.0 | 14 | 4.3718 | 0.0975 | 0.9466 | 5.6031 | 0.0975 | 0.0893 | 0.1665 | 0.8820 | | No log | 3.0 | 21 | 3.9389 | 0.2325 | 0.8820 | 5.3266 | 0.2325 | 0.1837 | 0.1656 | 0.6302 | | No log | 4.0 | 28 | 3.3251 | 0.3075 | 0.7848 | 3.6819 | 0.3075 | 0.2963 | 0.1644 | 0.4646 | | No log | 5.0 | 35 | 2.8980 | 0.4025 | 0.7180 | 2.9699 | 0.4025 | 0.3740 | 0.1867 | 0.3388 | | No log | 6.0 | 42 | 2.7055 | 0.4375 | 0.7010 | 3.2343 | 0.4375 | 0.4029 | 0.2096 | 0.3042 | | No log | 7.0 | 49 | 2.6466 | 0.4575 | 0.7040 | 2.9977 | 0.4575 | 0.4272 | 0.2082 | 0.2999 | | No log | 8.0 | 56 | 2.5399 | 0.4775 | 0.6809 | 2.8098 | 0.4775 | 0.4693 | 0.2106 | 0.2837 | | No log | 9.0 | 63 | 2.5949 | 0.49 | 0.6824 | 2.6503 | 0.49 | 0.4827 | 0.2289 | 0.2718 | | No log | 10.0 | 70 | 2.6997 | 0.4725 | 0.7318 | 2.8289 | 0.4725 | 0.4523 | 0.2536 | 0.3056 | | No log | 11.0 | 77 | 2.5694 | 0.47 | 0.7040 | 2.7969 | 0.47 | 0.4534 | 0.2348 | 0.2766 | | No log | 12.0 | 84 | 2.4539 | 0.4975 | 0.6762 | 2.6929 | 0.4975 | 0.4831 | 0.2300 | 0.2664 | | No log | 13.0 | 91 | 2.4841 | 0.5025 | 0.6664 | 2.6140 | 0.5025 | 0.4944 | 0.2098 | 0.2584 | | No log | 14.0 | 98 | 2.2755 | 0.535 | 0.6410 | 2.4991 | 0.535 | 0.5249 | 0.1905 | 0.2405 | | No log | 15.0 | 105 | 2.2998 | 0.5125 | 0.6282 | 2.6561 | 0.5125 | 0.4982 | 0.1916 | 0.2371 | | No log | 16.0 | 112 | 2.2156 | 0.525 | 0.6195 | 2.4837 | 0.525 | 0.5191 | 0.1870 | 0.2232 | | No log | 17.0 | 119 | 2.1862 | 0.5225 | 0.6096 | 2.7252 | 0.5225 | 0.5202 | 0.1747 | 0.2292 | | No log | 18.0 | 126 | 2.2058 | 0.5375 | 0.6160 | 2.5446 | 0.5375 | 0.5154 | 0.2011 | 0.2231 | | No log | 19.0 | 133 | 2.2147 | 0.5375 | 0.6143 | 2.5720 | 0.5375 | 0.5232 | 0.2028 | 0.2221 | | No log | 20.0 | 140 | 2.1791 | 0.525 | 0.6191 | 2.4505 | 0.525 | 0.5107 | 0.1918 | 0.2233 | | No log | 21.0 | 147 | 2.1165 | 0.535 | 0.5960 | 2.5369 | 0.535 | 0.5280 | 0.1867 | 0.2133 | | No log | 22.0 | 154 | 2.1193 | 0.54 | 0.6009 | 2.5568 | 0.54 | 0.5313 | 0.2049 | 0.2184 | | No log | 23.0 | 161 | 2.1082 | 0.5425 | 0.5929 | 2.5238 | 0.5425 | 0.5360 | 0.1691 | 0.2099 | | No log | 24.0 | 168 | 2.1221 | 0.535 | 0.6115 | 2.4854 | 0.535 | 0.5252 | 0.1779 | 0.2234 | | No log | 25.0 | 175 | 2.1912 | 0.52 | 0.6295 | 2.4975 | 0.52 | 0.5109 | 0.1970 | 0.2339 | | No log | 26.0 | 182 | 2.1056 | 0.5225 | 0.6150 | 2.4697 | 0.5225 | 0.5250 | 0.2020 | 0.2346 | | No log | 27.0 | 189 | 2.1017 | 0.535 | 0.6027 | 2.4992 | 0.535 | 0.5399 | 0.2003 | 0.2173 | | No log | 28.0 | 196 | 2.0999 | 0.545 | 0.5929 | 2.6313 | 0.545 | 0.5306 | 0.1844 | 0.2126 | | No log | 29.0 | 203 | 2.1188 | 0.54 | 0.6044 | 2.5420 | 0.54 | 0.5211 | 0.1745 | 0.2159 | | No log | 30.0 | 210 | 2.0670 | 0.56 | 0.5938 | 2.4868 | 0.56 | 0.5500 | 0.1849 | 0.2132 | | No log | 31.0 | 217 | 2.0709 | 0.5525 | 0.5937 | 2.4206 | 0.5525 | 0.5489 | 0.1759 | 0.2140 | | No log | 32.0 | 224 | 2.0390 | 0.5675 | 0.5794 | 2.5007 | 0.5675 | 0.5547 | 0.1745 | 0.2032 | | No log | 33.0 | 231 | 2.0725 | 0.5375 | 0.5912 | 2.4328 | 0.5375 | 0.5237 | 0.1817 | 0.2157 | | No log | 34.0 | 238 | 2.0644 | 0.565 | 0.5950 | 2.5103 | 0.565 | 0.5560 | 0.1712 | 0.2085 | | No log | 35.0 | 245 | 2.0665 | 0.5575 | 0.5918 | 2.4401 | 0.5575 | 0.5519 | 0.1604 | 0.2133 | | No log | 36.0 | 252 | 2.0485 | 0.53 | 0.5977 | 2.4321 | 0.53 | 0.5259 | 0.1927 | 0.2163 | | No log | 37.0 | 259 | 2.0352 | 0.555 | 0.5780 | 2.4665 | 0.555 | 0.5450 | 0.1701 | 0.2000 | | No log | 38.0 | 266 | 2.0423 | 0.5425 | 0.5777 | 2.5061 | 0.5425 | 0.5327 | 0.1689 | 0.2021 | | No log | 39.0 | 273 | 2.0138 | 0.5625 | 0.5799 | 2.3714 | 0.5625 | 0.5581 | 0.1705 | 0.2060 | | No log | 40.0 | 280 | 2.0161 | 0.5525 | 0.5918 | 2.3704 | 0.5525 | 0.5439 | 0.1720 | 0.2111 | | No log | 41.0 | 287 | 2.0315 | 0.545 | 0.5843 | 2.4582 | 0.545 | 0.5355 | 0.1782 | 0.2083 | | No log | 42.0 | 294 | 2.0157 | 0.545 | 0.5861 | 2.5374 | 0.545 | 0.5386 | 0.1995 | 0.2129 | | No log | 43.0 | 301 | 2.0495 | 0.555 | 0.5922 | 2.4841 | 0.555 | 0.5393 | 0.1538 | 0.2138 | | No log | 44.0 | 308 | 2.0293 | 0.5525 | 0.5824 | 2.4853 | 0.5525 | 0.5352 | 0.1745 | 0.2042 | | No log | 45.0 | 315 | 2.0253 | 0.5575 | 0.5776 | 2.4516 | 0.5575 | 0.5421 | 0.1978 | 0.2045 | | No log | 46.0 | 322 | 2.0246 | 0.5525 | 0.5953 | 2.4196 | 0.5525 | 0.5362 | 0.1715 | 0.2122 | | No log | 47.0 | 329 | 2.0114 | 0.555 | 0.5730 | 2.4431 | 0.555 | 0.5462 | 0.1759 | 0.2008 | | No log | 48.0 | 336 | 2.0046 | 0.5575 | 0.5801 | 2.3784 | 0.5575 | 0.5445 | 0.1703 | 0.2039 | | No log | 49.0 | 343 | 1.9721 | 0.565 | 0.5672 | 2.5034 | 0.565 | 0.5594 | 0.1686 | 0.1963 | | No log | 50.0 | 350 | 1.9872 | 0.565 | 0.5704 | 2.4067 | 0.565 | 0.5620 | 0.1888 | 0.2000 | | No log | 51.0 | 357 | 1.9668 | 0.5725 | 0.5695 | 2.3935 | 0.5725 | 0.5711 | 0.1534 | 0.1981 | | No log | 52.0 | 364 | 1.9796 | 0.5525 | 0.5742 | 2.3977 | 0.5525 | 0.5504 | 0.1614 | 0.2023 | | No log | 53.0 | 371 | 2.0086 | 0.56 | 0.5835 | 2.4361 | 0.56 | 0.5510 | 0.1912 | 0.2098 | | No log | 54.0 | 378 | 1.9998 | 0.54 | 0.5776 | 2.4292 | 0.54 | 0.5270 | 0.1679 | 0.2042 | | No log | 55.0 | 385 | 1.9736 | 0.555 | 0.5732 | 2.3619 | 0.555 | 0.5427 | 0.1830 | 0.2024 | | No log | 56.0 | 392 | 1.9850 | 0.57 | 0.5691 | 2.4491 | 0.57 | 0.5670 | 0.1798 | 0.1967 | | No log | 57.0 | 399 | 1.9680 | 0.5675 | 0.5680 | 2.4863 | 0.5675 | 0.5624 | 0.1644 | 0.1967 | | No log | 58.0 | 406 | 1.9633 | 0.565 | 0.5682 | 2.4407 | 0.565 | 0.5573 | 0.1576 | 0.1960 | | No log | 59.0 | 413 | 1.9715 | 0.555 | 0.5750 | 2.4037 | 0.555 | 0.5459 | 0.1753 | 0.2027 | | No log | 60.0 | 420 | 1.9791 | 0.575 | 0.5684 | 2.3886 | 0.575 | 0.5732 | 0.1647 | 0.1969 | | No log | 61.0 | 427 | 1.9587 | 0.5675 | 0.5669 | 2.4318 | 0.5675 | 0.5616 | 0.1583 | 0.1948 | | No log | 62.0 | 434 | 1.9735 | 0.5575 | 0.5699 | 2.3758 | 0.5575 | 0.5550 | 0.1442 | 0.1978 | | No log | 63.0 | 441 | 1.9720 | 0.5475 | 0.5748 | 2.4373 | 0.5475 | 0.5405 | 0.1660 | 0.2004 | | No log | 64.0 | 448 | 1.9825 | 0.55 | 0.5710 | 2.3517 | 0.55 | 0.5490 | 0.1810 | 0.2030 | | No log | 65.0 | 455 | 1.9679 | 0.5725 | 0.5705 | 2.4311 | 0.5725 | 0.5718 | 0.1690 | 0.1980 | | No log | 66.0 | 462 | 1.9735 | 0.565 | 0.5706 | 2.4810 | 0.565 | 0.5624 | 0.1740 | 0.1976 | | No log | 67.0 | 469 | 1.9752 | 0.5675 | 0.5689 | 2.3718 | 0.5675 | 0.5615 | 0.1681 | 0.1980 | | No log | 68.0 | 476 | 1.9798 | 0.565 | 0.5690 | 2.4235 | 0.565 | 0.5602 | 0.1671 | 0.1980 | | No log | 69.0 | 483 | 1.9720 | 0.5625 | 0.5681 | 2.3483 | 0.5625 | 0.5576 | 0.1845 | 0.1970 | | No log | 70.0 | 490 | 1.9640 | 0.5675 | 0.5673 | 2.3772 | 0.5675 | 0.5639 | 0.1621 | 0.1962 | | No log | 71.0 | 497 | 1.9641 | 0.5625 | 0.5680 | 2.3925 | 0.5625 | 0.5567 | 0.1670 | 0.1963 | | 0.4915 | 72.0 | 504 | 1.9753 | 0.5625 | 0.5707 | 2.4507 | 0.5625 | 0.5566 | 0.1780 | 0.1986 | | 0.4915 | 73.0 | 511 | 1.9792 | 0.56 | 0.5700 | 2.3604 | 0.56 | 0.5545 | 0.1580 | 0.1990 | | 0.4915 | 74.0 | 518 | 1.9679 | 0.55 | 0.5700 | 2.3519 | 0.55 | 0.5412 | 0.1781 | 0.1981 | | 0.4915 | 75.0 | 525 | 1.9711 | 0.57 | 0.5685 | 2.4204 | 0.57 | 0.5676 | 0.1891 | 0.1962 | | 0.4915 | 76.0 | 532 | 1.9705 | 0.565 | 0.5686 | 2.3512 | 0.565 | 0.5600 | 0.1684 | 0.1967 | | 0.4915 | 77.0 | 539 | 1.9673 | 0.5625 | 0.5685 | 2.3481 | 0.5625 | 0.5577 | 0.1761 | 0.1968 | | 0.4915 | 78.0 | 546 | 1.9641 | 0.5625 | 0.5668 | 2.3519 | 0.5625 | 0.5577 | 0.1747 | 0.1964 | | 0.4915 | 79.0 | 553 | 1.9685 | 0.5675 | 0.5677 | 2.3833 | 0.5675 | 0.5630 | 0.1490 | 0.1967 | | 0.4915 | 80.0 | 560 | 1.9722 | 0.5625 | 0.5691 | 2.3864 | 0.5625 | 0.5565 | 0.1583 | 0.1979 | | 0.4915 | 81.0 | 567 | 1.9641 | 0.5625 | 0.5675 | 2.3528 | 0.5625 | 0.5566 | 0.1820 | 0.1965 | | 0.4915 | 82.0 | 574 | 1.9666 | 0.5625 | 0.5678 | 2.3516 | 0.5625 | 0.5564 | 0.1622 | 0.1968 | | 0.4915 | 83.0 | 581 | 1.9703 | 0.56 | 0.5679 | 2.3564 | 0.56 | 0.5550 | 0.1577 | 0.1967 | | 0.4915 | 84.0 | 588 | 1.9666 | 0.56 | 0.5673 | 2.3532 | 0.56 | 0.5546 | 0.1633 | 0.1967 | | 0.4915 | 85.0 | 595 | 1.9683 | 0.56 | 0.5682 | 2.3506 | 0.56 | 0.5539 | 0.1526 | 0.1971 | | 0.4915 | 86.0 | 602 | 1.9673 | 0.5575 | 0.5674 | 2.3523 | 0.5575 | 0.5516 | 0.1642 | 0.1972 | | 0.4915 | 87.0 | 609 | 1.9678 | 0.56 | 0.5678 | 2.3514 | 0.56 | 0.5539 | 0.1543 | 0.1970 | | 0.4915 | 88.0 | 616 | 1.9680 | 0.56 | 0.5678 | 2.3531 | 0.56 | 0.5539 | 0.1581 | 0.1970 | | 0.4915 | 89.0 | 623 | 1.9683 | 0.5575 | 0.5681 | 2.3532 | 0.5575 | 0.5516 | 0.1732 | 0.1976 | | 0.4915 | 90.0 | 630 | 1.9677 | 0.56 | 0.5677 | 2.3536 | 0.56 | 0.5539 | 0.1702 | 0.1970 | | 0.4915 | 91.0 | 637 | 1.9680 | 0.5575 | 0.5679 | 2.3528 | 0.5575 | 0.5515 | 0.1760 | 0.1974 | | 0.4915 | 92.0 | 644 | 1.9681 | 0.5575 | 0.5679 | 2.3522 | 0.5575 | 0.5515 | 0.1584 | 0.1975 | | 0.4915 | 93.0 | 651 | 1.9680 | 0.5575 | 0.5678 | 2.3524 | 0.5575 | 0.5515 | 0.1610 | 0.1973 | | 0.4915 | 94.0 | 658 | 1.9680 | 0.5575 | 0.5680 | 2.3521 | 0.5575 | 0.5515 | 0.1653 | 0.1973 | | 0.4915 | 95.0 | 665 | 1.9681 | 0.5575 | 0.5679 | 2.3524 | 0.5575 | 0.5515 | 0.1663 | 0.1973 | | 0.4915 | 96.0 | 672 | 1.9682 | 0.5575 | 0.5680 | 2.3528 | 0.5575 | 0.5516 | 0.1663 | 0.1973 | | 0.4915 | 97.0 | 679 | 1.9682 | 0.5575 | 0.5680 | 2.3526 | 0.5575 | 0.5515 | 0.1625 | 0.1973 | | 0.4915 | 98.0 | 686 | 1.9681 | 0.5575 | 0.5679 | 2.3526 | 0.5575 | 0.5516 | 0.1676 | 0.1973 | | 0.4915 | 99.0 | 693 | 1.9682 | 0.5575 | 0.5679 | 2.3526 | 0.5575 | 0.5516 | 0.1635 | 0.1973 | | 0.4915 | 100.0 | 700 | 1.9682 | 0.5575 | 0.5680 | 2.3526 | 0.5575 | 0.5516 | 0.1676 | 0.1973 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

sbaner24/vit-base-patch16-224-Soybean_11-46

# vit-base-patch16-224-Soybean_11-46 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2058 - Accuracy: 0.9306 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3661 | 1.0 | 11 | 1.3698 | 0.5069 | | 0.9979 | 2.0 | 22 | 0.9817 | 0.6632 | | 0.6746 | 3.0 | 33 | 0.7423 | 0.7396 | | 0.6364 | 4.0 | 44 | 0.6075 | 0.7569 | | 0.5425 | 5.0 | 55 | 0.5500 | 0.7951 | | 0.5001 | 6.0 | 66 | 0.4883 | 0.8160 | | 0.3519 | 7.0 | 77 | 0.4539 | 0.8264 | | 0.4421 | 8.0 | 88 | 0.4483 | 0.8194 | | 0.3207 | 9.0 | 99 | 0.3785 | 0.8438 | | 0.3682 | 10.0 | 110 | 0.3385 | 0.8646 | | 0.2642 | 11.0 | 121 | 0.3827 | 0.8403 | | 0.3444 | 12.0 | 132 | 0.3462 | 0.8507 | | 0.2423 | 13.0 | 143 | 0.3170 | 0.8681 | | 0.3168 | 14.0 | 154 | 0.3168 | 0.8715 | | 0.2781 | 15.0 | 165 | 0.3323 | 0.8333 | | 0.2411 | 16.0 | 176 | 0.3200 | 0.8715 | | 0.2276 | 17.0 | 187 | 0.3296 | 0.875 | | 0.192 | 18.0 | 198 | 0.3119 | 0.8854 | | 0.1612 | 19.0 | 209 | 0.3647 | 0.875 | | 0.1084 | 20.0 | 220 | 0.2641 | 0.8993 | | 0.2099 | 21.0 | 231 | 0.2807 | 0.8958 | | 0.1666 | 22.0 | 242 | 0.2595 | 0.9097 | | 0.1355 | 23.0 | 253 | 0.2735 | 0.8924 | | 0.1165 | 24.0 | 264 | 0.3238 | 0.8785 | | 0.112 | 25.0 | 275 | 0.3066 | 0.8889 | | 0.1191 | 26.0 | 286 | 0.2427 | 0.9062 | | 0.1293 | 27.0 | 297 | 0.2536 | 0.9201 | | 0.2932 | 28.0 | 308 | 0.2707 | 0.8924 | | 0.0918 | 29.0 | 319 | 0.2688 | 0.8924 | | 0.1529 | 30.0 | 330 | 0.2715 | 0.8889 | | 0.227 | 31.0 | 341 | 0.2664 | 0.9028 | | 0.1044 | 32.0 | 352 | 0.2809 | 0.8993 | | 0.0894 | 33.0 | 363 | 0.2863 | 0.8924 | | 0.0566 | 34.0 | 374 | 0.2474 | 0.9201 | | 0.0915 | 35.0 | 385 | 0.2428 | 0.9097 | | 0.1136 | 36.0 | 396 | 0.2545 | 0.9097 | | 0.0947 | 37.0 | 407 | 0.2599 | 0.9097 | | 0.1012 | 38.0 | 418 | 0.2454 | 0.9167 | | 0.0465 | 39.0 | 429 | 0.2435 | 0.9201 | | 0.0299 | 40.0 | 440 | 0.2532 | 0.9062 | | 0.0311 | 41.0 | 451 | 0.2298 | 0.9271 | | 0.0796 | 42.0 | 462 | 0.2422 | 0.9167 | | 0.058 | 43.0 | 473 | 0.2058 | 0.9306 | | 0.0853 | 44.0 | 484 | 0.2266 | 0.9306 | | 0.0868 | 45.0 | 495 | 0.2266 | 0.9236 | | 0.0554 | 46.0 | 506 | 0.2163 | 0.9271 | | 0.0508 | 47.0 | 517 | 0.2104 | 0.9306 | | 0.0589 | 48.0 | 528 | 0.2172 | 0.9271 | | 0.0369 | 49.0 | 539 | 0.2214 | 0.9271 | | 0.0852 | 50.0 | 550 | 0.2241 | 0.9271 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

[ "0", "1", "2", "3", "4" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7789 - Accuracy: 0.565 - Brier Loss: 0.5798 - Nll: 2.3548 - F1 Micro: 0.565 - F1 Macro: 0.5569 - Ece: 0.1677 - Aurc: 0.2032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.7719 | 0.04 | 1.0712 | 7.3456 | 0.04 | 0.0315 | 0.2816 | 0.9543 | | No log | 2.0 | 14 | 3.7636 | 0.1 | 0.9486 | 5.7989 | 0.1000 | 0.0863 | 0.1625 | 0.8854 | | No log | 3.0 | 21 | 3.3871 | 0.235 | 0.8838 | 5.3315 | 0.235 | 0.1857 | 0.1589 | 0.6274 | | No log | 4.0 | 28 | 2.8780 | 0.2975 | 0.7900 | 3.5467 | 0.2975 | 0.2884 | 0.1672 | 0.4712 | | No log | 5.0 | 35 | 2.5223 | 0.3875 | 0.7164 | 3.0188 | 0.3875 | 0.3630 | 0.1596 | 0.3495 | | No log | 6.0 | 42 | 2.3634 | 0.43 | 0.6927 | 3.1378 | 0.4300 | 0.4031 | 0.1877 | 0.3055 | | No log | 7.0 | 49 | 2.3487 | 0.445 | 0.7009 | 3.0084 | 0.445 | 0.4123 | 0.2026 | 0.3003 | | No log | 8.0 | 56 | 2.2521 | 0.47 | 0.6857 | 2.8634 | 0.47 | 0.4607 | 0.1977 | 0.2918 | | No log | 9.0 | 63 | 2.3597 | 0.4775 | 0.6955 | 2.6768 | 0.4775 | 0.4642 | 0.2215 | 0.2787 | | No log | 10.0 | 70 | 2.3524 | 0.46 | 0.7130 | 2.7830 | 0.46 | 0.4441 | 0.2429 | 0.2941 | | No log | 11.0 | 77 | 2.4233 | 0.46 | 0.7108 | 3.0867 | 0.46 | 0.4418 | 0.2247 | 0.2828 | | No log | 12.0 | 84 | 2.2723 | 0.485 | 0.6901 | 2.7313 | 0.485 | 0.4742 | 0.2293 | 0.2774 | | No log | 13.0 | 91 | 2.2818 | 0.49 | 0.7007 | 2.5829 | 0.49 | 0.4859 | 0.2473 | 0.2791 | | No log | 14.0 | 98 | 2.1682 | 0.4975 | 0.6695 | 2.8292 | 0.4975 | 0.4815 | 0.2170 | 0.2653 | | No log | 15.0 | 105 | 2.0652 | 0.52 | 0.6520 | 2.4319 | 0.52 | 0.5120 | 0.2079 | 0.2617 | | No log | 16.0 | 112 | 2.0524 | 0.5225 | 0.6384 | 2.5273 | 0.5225 | 0.5128 | 0.1980 | 0.2391 | | No log | 17.0 | 119 | 2.1736 | 0.48 | 0.6765 | 2.4887 | 0.48 | 0.4809 | 0.2112 | 0.2834 | | No log | 18.0 | 126 | 2.0096 | 0.515 | 0.6496 | 2.4230 | 0.515 | 0.5215 | 0.1957 | 0.2513 | | No log | 19.0 | 133 | 2.0207 | 0.5175 | 0.6417 | 2.4591 | 0.5175 | 0.5104 | 0.2011 | 0.2526 | | No log | 20.0 | 140 | 1.9152 | 0.5425 | 0.6044 | 2.5069 | 0.5425 | 0.5405 | 0.1826 | 0.2147 | | No log | 21.0 | 147 | 1.9600 | 0.52 | 0.6332 | 2.5016 | 0.52 | 0.5204 | 0.1871 | 0.2427 | | No log | 22.0 | 154 | 1.9325 | 0.515 | 0.6226 | 2.4981 | 0.515 | 0.5078 | 0.1860 | 0.2323 | | No log | 23.0 | 161 | 1.9172 | 0.53 | 0.6144 | 2.4601 | 0.53 | 0.5221 | 0.1941 | 0.2311 | | No log | 24.0 | 168 | 1.8891 | 0.5425 | 0.6091 | 2.4653 | 0.5425 | 0.5399 | 0.1933 | 0.2260 | | No log | 25.0 | 175 | 1.9460 | 0.5175 | 0.6214 | 2.4785 | 0.5175 | 0.5168 | 0.1694 | 0.2335 | | No log | 26.0 | 182 | 1.9060 | 0.5525 | 0.5970 | 2.4789 | 0.5525 | 0.5430 | 0.1934 | 0.2137 | | No log | 27.0 | 189 | 1.9421 | 0.5375 | 0.6205 | 2.4814 | 0.5375 | 0.5313 | 0.2135 | 0.2402 | | No log | 28.0 | 196 | 2.0195 | 0.545 | 0.6187 | 2.5330 | 0.545 | 0.5256 | 0.1800 | 0.2360 | | No log | 29.0 | 203 | 1.9428 | 0.535 | 0.6167 | 2.4894 | 0.535 | 0.5111 | 0.1862 | 0.2322 | | No log | 30.0 | 210 | 1.8996 | 0.5225 | 0.6207 | 2.4810 | 0.5225 | 0.5137 | 0.1994 | 0.2330 | | No log | 31.0 | 217 | 1.8462 | 0.54 | 0.6119 | 2.4201 | 0.54 | 0.5341 | 0.1817 | 0.2190 | | No log | 32.0 | 224 | 1.8324 | 0.55 | 0.5988 | 2.4230 | 0.55 | 0.5427 | 0.1888 | 0.2171 | | No log | 33.0 | 231 | 1.8393 | 0.545 | 0.5977 | 2.3943 | 0.545 | 0.5339 | 0.1838 | 0.2172 | | No log | 34.0 | 238 | 1.8704 | 0.5475 | 0.6081 | 2.4488 | 0.5475 | 0.5427 | 0.1768 | 0.2200 | | No log | 35.0 | 245 | 1.8546 | 0.54 | 0.6022 | 2.3273 | 0.54 | 0.5316 | 0.1847 | 0.2226 | | No log | 36.0 | 252 | 1.8608 | 0.53 | 0.5972 | 2.5153 | 0.53 | 0.5139 | 0.1810 | 0.2202 | | No log | 37.0 | 259 | 1.8663 | 0.5325 | 0.6057 | 2.4642 | 0.5325 | 0.5243 | 0.1836 | 0.2205 | | No log | 38.0 | 266 | 1.8300 | 0.545 | 0.5954 | 2.5101 | 0.545 | 0.5418 | 0.1890 | 0.2141 | | No log | 39.0 | 273 | 1.8121 | 0.5625 | 0.5853 | 2.4397 | 0.5625 | 0.5550 | 0.1704 | 0.2110 | | No log | 40.0 | 280 | 1.7916 | 0.54 | 0.5884 | 2.3565 | 0.54 | 0.5361 | 0.1685 | 0.2135 | | No log | 41.0 | 287 | 1.8353 | 0.5575 | 0.5929 | 2.4252 | 0.5575 | 0.5451 | 0.1823 | 0.2116 | | No log | 42.0 | 294 | 1.7999 | 0.5675 | 0.5839 | 2.4820 | 0.5675 | 0.5631 | 0.1729 | 0.2045 | | No log | 43.0 | 301 | 1.8622 | 0.52 | 0.6106 | 2.4823 | 0.52 | 0.5028 | 0.1948 | 0.2270 | | No log | 44.0 | 308 | 1.7892 | 0.55 | 0.5892 | 2.3342 | 0.55 | 0.5450 | 0.1798 | 0.2126 | | No log | 45.0 | 315 | 1.7978 | 0.545 | 0.5868 | 2.4345 | 0.545 | 0.5439 | 0.1894 | 0.2094 | | No log | 46.0 | 322 | 1.7697 | 0.56 | 0.5772 | 2.4272 | 0.56 | 0.5585 | 0.1601 | 0.1997 | | No log | 47.0 | 329 | 1.7754 | 0.5475 | 0.5835 | 2.3977 | 0.5475 | 0.5438 | 0.1759 | 0.2059 | | No log | 48.0 | 336 | 1.7922 | 0.545 | 0.5929 | 2.4119 | 0.545 | 0.5390 | 0.1891 | 0.2131 | | No log | 49.0 | 343 | 1.8055 | 0.5625 | 0.5872 | 2.3654 | 0.5625 | 0.5497 | 0.1759 | 0.2073 | | No log | 50.0 | 350 | 1.7972 | 0.56 | 0.5894 | 2.3366 | 0.56 | 0.5487 | 0.1803 | 0.2083 | | No log | 51.0 | 357 | 1.7890 | 0.555 | 0.5815 | 2.3858 | 0.555 | 0.5501 | 0.1693 | 0.2067 | | No log | 52.0 | 364 | 1.7958 | 0.5475 | 0.5883 | 2.4244 | 0.5475 | 0.5355 | 0.1910 | 0.2105 | | No log | 53.0 | 371 | 1.7881 | 0.5675 | 0.5834 | 2.4135 | 0.5675 | 0.5603 | 0.1836 | 0.2028 | | No log | 54.0 | 378 | 1.7675 | 0.555 | 0.5766 | 2.4043 | 0.555 | 0.5563 | 0.1653 | 0.2047 | | No log | 55.0 | 385 | 1.7688 | 0.55 | 0.5843 | 2.3641 | 0.55 | 0.5505 | 0.1729 | 0.2092 | | No log | 56.0 | 392 | 1.7796 | 0.55 | 0.5861 | 2.3404 | 0.55 | 0.5458 | 0.1808 | 0.2114 | | No log | 57.0 | 399 | 1.7861 | 0.54 | 0.5885 | 2.3460 | 0.54 | 0.5323 | 0.1902 | 0.2073 | | No log | 58.0 | 406 | 1.7746 | 0.56 | 0.5818 | 2.3715 | 0.56 | 0.5557 | 0.1643 | 0.2034 | | No log | 59.0 | 413 | 1.7828 | 0.5575 | 0.5868 | 2.3086 | 0.5575 | 0.5526 | 0.1956 | 0.2088 | | No log | 60.0 | 420 | 1.7735 | 0.565 | 0.5825 | 2.3405 | 0.565 | 0.5619 | 0.1696 | 0.2058 | | No log | 61.0 | 427 | 1.7651 | 0.5675 | 0.5760 | 2.4771 | 0.5675 | 0.5636 | 0.1847 | 0.2027 | | No log | 62.0 | 434 | 1.7751 | 0.5575 | 0.5834 | 2.3727 | 0.5575 | 0.5524 | 0.1638 | 0.2052 | | No log | 63.0 | 441 | 1.7900 | 0.56 | 0.5834 | 2.3635 | 0.56 | 0.5502 | 0.1789 | 0.2061 | | No log | 64.0 | 448 | 1.7729 | 0.56 | 0.5821 | 2.3797 | 0.56 | 0.5554 | 0.1676 | 0.2046 | | No log | 65.0 | 455 | 1.7743 | 0.5625 | 0.5826 | 2.4174 | 0.5625 | 0.5581 | 0.1538 | 0.2052 | | No log | 66.0 | 462 | 1.7749 | 0.5625 | 0.5801 | 2.3799 | 0.5625 | 0.5592 | 0.1709 | 0.2036 | | No log | 67.0 | 469 | 1.7795 | 0.5625 | 0.5814 | 2.3169 | 0.5625 | 0.5533 | 0.1883 | 0.2037 | | No log | 68.0 | 476 | 1.7773 | 0.5675 | 0.5794 | 2.3588 | 0.5675 | 0.5622 | 0.1779 | 0.2013 | | No log | 69.0 | 483 | 1.7762 | 0.56 | 0.5793 | 2.3514 | 0.56 | 0.5566 | 0.1707 | 0.2039 | | No log | 70.0 | 490 | 1.7762 | 0.5625 | 0.5787 | 2.3620 | 0.5625 | 0.5529 | 0.1607 | 0.2017 | | No log | 71.0 | 497 | 1.7740 | 0.5675 | 0.5798 | 2.3235 | 0.5675 | 0.5612 | 0.1637 | 0.2046 | | 0.4215 | 72.0 | 504 | 1.7739 | 0.56 | 0.5790 | 2.3235 | 0.56 | 0.5542 | 0.1583 | 0.2023 | | 0.4215 | 73.0 | 511 | 1.7783 | 0.56 | 0.5806 | 2.4187 | 0.56 | 0.5545 | 0.1674 | 0.2040 | | 0.4215 | 74.0 | 518 | 1.7785 | 0.56 | 0.5805 | 2.3302 | 0.56 | 0.5544 | 0.1748 | 0.2033 | | 0.4215 | 75.0 | 525 | 1.7777 | 0.5625 | 0.5795 | 2.3321 | 0.5625 | 0.5548 | 0.1754 | 0.2029 | | 0.4215 | 76.0 | 532 | 1.7785 | 0.565 | 0.5799 | 2.3249 | 0.565 | 0.5586 | 0.1696 | 0.2023 | | 0.4215 | 77.0 | 539 | 1.7763 | 0.565 | 0.5790 | 2.3561 | 0.565 | 0.5573 | 0.1574 | 0.2022 | | 0.4215 | 78.0 | 546 | 1.7767 | 0.565 | 0.5790 | 2.3296 | 0.565 | 0.5572 | 0.1633 | 0.2024 | | 0.4215 | 79.0 | 553 | 1.7763 | 0.565 | 0.5790 | 2.3555 | 0.565 | 0.5580 | 0.1687 | 0.2016 | | 0.4215 | 80.0 | 560 | 1.7783 | 0.565 | 0.5800 | 2.3254 | 0.565 | 0.5576 | 0.1752 | 0.2032 | | 0.4215 | 81.0 | 567 | 1.7773 | 0.5675 | 0.5796 | 2.3530 | 0.5675 | 0.5605 | 0.1519 | 0.2023 | | 0.4215 | 82.0 | 574 | 1.7774 | 0.5625 | 0.5797 | 2.3253 | 0.5625 | 0.5549 | 0.1911 | 0.2028 | | 0.4215 | 83.0 | 581 | 1.7784 | 0.5625 | 0.5794 | 2.3554 | 0.5625 | 0.5544 | 0.1659 | 0.2030 | | 0.4215 | 84.0 | 588 | 1.7769 | 0.565 | 0.5793 | 2.3527 | 0.565 | 0.5585 | 0.1588 | 0.2024 | | 0.4215 | 85.0 | 595 | 1.7787 | 0.565 | 0.5799 | 2.3549 | 0.565 | 0.5576 | 0.1687 | 0.2032 | | 0.4215 | 86.0 | 602 | 1.7778 | 0.565 | 0.5795 | 2.3548 | 0.565 | 0.5574 | 0.1577 | 0.2029 | | 0.4215 | 87.0 | 609 | 1.7787 | 0.5625 | 0.5798 | 2.3545 | 0.5625 | 0.5549 | 0.1643 | 0.2032 | | 0.4215 | 88.0 | 616 | 1.7786 | 0.565 | 0.5796 | 2.3554 | 0.565 | 0.5574 | 0.1667 | 0.2031 | | 0.4215 | 89.0 | 623 | 1.7785 | 0.565 | 0.5799 | 2.3546 | 0.565 | 0.5574 | 0.1691 | 0.2032 | | 0.4215 | 90.0 | 630 | 1.7784 | 0.565 | 0.5797 | 2.3548 | 0.565 | 0.5574 | 0.1656 | 0.2031 | | 0.4215 | 91.0 | 637 | 1.7784 | 0.565 | 0.5797 | 2.3550 | 0.565 | 0.5569 | 0.1753 | 0.2032 | | 0.4215 | 92.0 | 644 | 1.7786 | 0.565 | 0.5797 | 2.3545 | 0.565 | 0.5574 | 0.1744 | 0.2030 | | 0.4215 | 93.0 | 651 | 1.7785 | 0.565 | 0.5797 | 2.3545 | 0.565 | 0.5574 | 0.1709 | 0.2031 | | 0.4215 | 94.0 | 658 | 1.7787 | 0.565 | 0.5797 | 2.3543 | 0.565 | 0.5574 | 0.1704 | 0.2032 | | 0.4215 | 95.0 | 665 | 1.7787 | 0.565 | 0.5798 | 2.3545 | 0.565 | 0.5574 | 0.1713 | 0.2032 | | 0.4215 | 96.0 | 672 | 1.7788 | 0.565 | 0.5798 | 2.3549 | 0.565 | 0.5569 | 0.1777 | 0.2031 | | 0.4215 | 97.0 | 679 | 1.7789 | 0.565 | 0.5798 | 2.3550 | 0.565 | 0.5569 | 0.1677 | 0.2032 | | 0.4215 | 98.0 | 686 | 1.7789 | 0.565 | 0.5798 | 2.3549 | 0.565 | 0.5569 | 0.1648 | 0.2032 | | 0.4215 | 99.0 | 693 | 1.7789 | 0.565 | 0.5798 | 2.3548 | 0.565 | 0.5569 | 0.1728 | 0.2032 | | 0.4215 | 100.0 | 700 | 1.7789 | 0.565 | 0.5798 | 2.3548 | 0.565 | 0.5569 | 0.1677 | 0.2032 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

ayanban011/vit-base_tobacco_lr5e-6_e_200

# vit-base_tobacco_lr5e-6_e_200 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7893 - Accuracy: 0.78 - Brier Loss: 0.3364 - Nll: 1.4430 - F1 Micro: 0.78 - F1 Macro: 0.7488 - Ece: 0.1922 - Aurc: 0.1018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7450 | 0.815 | 0.3079 | 1.1882 | 0.815 | 0.7942 | 0.2383 | 0.0734 | | No log | 2.0 | 25 | 0.7440 | 0.815 | 0.3074 | 1.1877 | 0.815 | 0.7922 | 0.2303 | 0.0734 | | No log | 2.96 | 37 | 0.7429 | 0.81 | 0.3071 | 1.1883 | 0.81 | 0.7914 | 0.2367 | 0.0714 | | No log | 4.0 | 50 | 0.7463 | 0.815 | 0.3083 | 1.1912 | 0.815 | 0.7942 | 0.2334 | 0.0768 | | No log | 4.96 | 62 | 0.7453 | 0.815 | 0.3080 | 1.1927 | 0.815 | 0.7922 | 0.2224 | 0.0751 | | No log | 6.0 | 75 | 0.7416 | 0.81 | 0.3067 | 1.1870 | 0.81 | 0.7914 | 0.2232 | 0.0716 | | No log | 6.96 | 87 | 0.7420 | 0.81 | 0.3070 | 1.1858 | 0.81 | 0.7914 | 0.2309 | 0.0730 | | No log | 8.0 | 100 | 0.7409 | 0.81 | 0.3062 | 1.1879 | 0.81 | 0.7871 | 0.2186 | 0.0749 | | No log | 8.96 | 112 | 0.7444 | 0.815 | 0.3079 | 1.1984 | 0.815 | 0.8059 | 0.2342 | 0.0766 | | No log | 10.0 | 125 | 0.7445 | 0.81 | 0.3079 | 1.1918 | 0.81 | 0.7894 | 0.2353 | 0.0775 | | No log | 10.96 | 137 | 0.7451 | 0.81 | 0.3085 | 1.1888 | 0.81 | 0.7871 | 0.2319 | 0.0784 | | No log | 12.0 | 150 | 0.7411 | 0.805 | 0.3060 | 1.1914 | 0.805 | 0.7829 | 0.2186 | 0.0779 | | No log | 12.96 | 162 | 0.7406 | 0.815 | 0.3075 | 1.1967 | 0.815 | 0.8057 | 0.2472 | 0.0746 | | No log | 14.0 | 175 | 0.7360 | 0.805 | 0.3048 | 1.2494 | 0.805 | 0.7841 | 0.2383 | 0.0770 | | No log | 14.96 | 187 | 0.7331 | 0.81 | 0.3037 | 1.1896 | 0.81 | 0.7912 | 0.2306 | 0.0721 | | No log | 16.0 | 200 | 0.7345 | 0.81 | 0.3042 | 1.2488 | 0.81 | 0.7956 | 0.2064 | 0.0794 | | No log | 16.96 | 212 | 0.7329 | 0.815 | 0.3018 | 1.2595 | 0.815 | 0.8043 | 0.2331 | 0.0797 | | No log | 18.0 | 225 | 0.7354 | 0.795 | 0.3055 | 1.1743 | 0.795 | 0.7765 | 0.2154 | 0.0742 | | No log | 18.96 | 237 | 0.7282 | 0.805 | 0.3001 | 1.1920 | 0.805 | 0.7844 | 0.2444 | 0.0748 | | No log | 20.0 | 250 | 0.7225 | 0.805 | 0.2981 | 1.1804 | 0.805 | 0.7874 | 0.2041 | 0.0744 | | No log | 20.96 | 262 | 0.7250 | 0.81 | 0.3000 | 1.2516 | 0.81 | 0.7900 | 0.2148 | 0.0761 | | No log | 22.0 | 275 | 0.7252 | 0.81 | 0.3009 | 1.3033 | 0.81 | 0.7954 | 0.2375 | 0.0782 | | No log | 22.96 | 287 | 0.7293 | 0.8 | 0.3016 | 1.2557 | 0.8000 | 0.7796 | 0.2243 | 0.0810 | | No log | 24.0 | 300 | 0.7344 | 0.805 | 0.3057 | 1.3122 | 0.805 | 0.7853 | 0.2096 | 0.0815 | | No log | 24.96 | 312 | 0.7314 | 0.81 | 0.3044 | 1.2501 | 0.81 | 0.7909 | 0.2068 | 0.0808 | | No log | 26.0 | 325 | 0.7293 | 0.81 | 0.3036 | 1.3066 | 0.81 | 0.7909 | 0.2015 | 0.0804 | | No log | 26.96 | 337 | 0.7323 | 0.805 | 0.3042 | 1.3147 | 0.805 | 0.7853 | 0.2242 | 0.0827 | | No log | 28.0 | 350 | 0.7288 | 0.805 | 0.3029 | 1.3109 | 0.805 | 0.7853 | 0.1976 | 0.0826 | | No log | 28.96 | 362 | 0.7343 | 0.805 | 0.3058 | 1.3108 | 0.805 | 0.7853 | 0.2128 | 0.0851 | | No log | 30.0 | 375 | 0.7351 | 0.8 | 0.3062 | 1.3129 | 0.8000 | 0.7733 | 0.2088 | 0.0845 | | No log | 30.96 | 387 | 0.7301 | 0.8 | 0.3044 | 1.2452 | 0.8000 | 0.7733 | 0.2272 | 0.0836 | | No log | 32.0 | 400 | 0.7340 | 0.8 | 0.3055 | 1.3769 | 0.8000 | 0.7806 | 0.2207 | 0.0855 | | No log | 32.96 | 412 | 0.7322 | 0.805 | 0.3052 | 1.3132 | 0.805 | 0.7784 | 0.2470 | 0.0865 | | No log | 34.0 | 425 | 0.7301 | 0.8 | 0.3045 | 1.2543 | 0.8000 | 0.7733 | 0.2021 | 0.0863 | | No log | 34.96 | 437 | 0.7297 | 0.8 | 0.3046 | 1.3056 | 0.8000 | 0.7731 | 0.1886 | 0.0836 | | No log | 36.0 | 450 | 0.7326 | 0.805 | 0.3056 | 1.3102 | 0.805 | 0.7784 | 0.2256 | 0.0892 | | No log | 36.96 | 462 | 0.7311 | 0.8 | 0.3046 | 1.3681 | 0.8000 | 0.7733 | 0.2065 | 0.0880 | | No log | 38.0 | 475 | 0.7356 | 0.8 | 0.3077 | 1.3666 | 0.8000 | 0.7733 | 0.2034 | 0.0895 | | No log | 38.96 | 487 | 0.7302 | 0.8 | 0.3042 | 1.3114 | 0.8000 | 0.7733 | 0.1996 | 0.0894 | | 0.1528 | 40.0 | 500 | 0.7289 | 0.805 | 0.3035 | 1.3669 | 0.805 | 0.7797 | 0.1951 | 0.0874 | | 0.1528 | 40.96 | 512 | 0.7292 | 0.8 | 0.3037 | 1.3685 | 0.8000 | 0.7737 | 0.2103 | 0.0878 | | 0.1528 | 42.0 | 525 | 0.7347 | 0.805 | 0.3077 | 1.3640 | 0.805 | 0.7784 | 0.2005 | 0.0894 | | 0.1528 | 42.96 | 537 | 0.7343 | 0.805 | 0.3063 | 1.3684 | 0.805 | 0.7784 | 0.2055 | 0.0899 | | 0.1528 | 44.0 | 550 | 0.7283 | 0.805 | 0.3038 | 1.3660 | 0.805 | 0.7797 | 0.2142 | 0.0881 | | 0.1528 | 44.96 | 562 | 0.7325 | 0.805 | 0.3061 | 1.3658 | 0.805 | 0.7784 | 0.1967 | 0.0896 | | 0.1528 | 46.0 | 575 | 0.7303 | 0.81 | 0.3049 | 1.3659 | 0.81 | 0.7844 | 0.1985 | 0.0883 | | 0.1528 | 46.96 | 587 | 0.7321 | 0.805 | 0.3054 | 1.3657 | 0.805 | 0.7797 | 0.1925 | 0.0888 | | 0.1528 | 48.0 | 600 | 0.7322 | 0.8 | 0.3054 | 1.3680 | 0.8000 | 0.7704 | 0.1837 | 0.0903 | | 0.1528 | 48.96 | 612 | 0.7368 | 0.8 | 0.3079 | 1.3234 | 0.8000 | 0.7704 | 0.2111 | 0.0905 | | 0.1528 | 50.0 | 625 | 0.7358 | 0.8 | 0.3074 | 1.3682 | 0.8000 | 0.7704 | 0.1971 | 0.0899 | | 0.1528 | 50.96 | 637 | 0.7318 | 0.8 | 0.3054 | 1.3661 | 0.8000 | 0.7704 | 0.2111 | 0.0889 | | 0.1528 | 52.0 | 650 | 0.7473 | 0.795 | 0.3133 | 1.3707 | 0.795 | 0.7660 | 0.2032 | 0.0954 | | 0.1528 | 52.96 | 662 | 0.7299 | 0.805 | 0.3050 | 1.3631 | 0.805 | 0.7797 | 0.1977 | 0.0891 | | 0.1528 | 54.0 | 675 | 0.7427 | 0.795 | 0.3116 | 1.3673 | 0.795 | 0.7644 | 0.2181 | 0.0923 | | 0.1528 | 54.96 | 687 | 0.7371 | 0.8 | 0.3087 | 1.3648 | 0.8000 | 0.7704 | 0.1984 | 0.0902 | | 0.1528 | 56.0 | 700 | 0.7399 | 0.8 | 0.3100 | 1.3671 | 0.8000 | 0.7704 | 0.2178 | 0.0920 | | 0.1528 | 56.96 | 712 | 0.7420 | 0.8 | 0.3114 | 1.3671 | 0.8000 | 0.7689 | 0.2035 | 0.0925 | | 0.1528 | 58.0 | 725 | 0.7380 | 0.8 | 0.3088 | 1.3646 | 0.8000 | 0.7704 | 0.2016 | 0.0916 | | 0.1528 | 58.96 | 737 | 0.7390 | 0.8 | 0.3091 | 1.3671 | 0.8000 | 0.7704 | 0.2005 | 0.0921 | | 0.1528 | 60.0 | 750 | 0.7370 | 0.8 | 0.3085 | 1.3651 | 0.8000 | 0.7704 | 0.1891 | 0.0908 | | 0.1528 | 60.96 | 762 | 0.7388 | 0.8 | 0.3097 | 1.3622 | 0.8000 | 0.7704 | 0.2010 | 0.0906 | | 0.1528 | 62.0 | 775 | 0.7403 | 0.795 | 0.3102 | 1.3691 | 0.795 | 0.7654 | 0.2142 | 0.0910 | | 0.1528 | 62.96 | 787 | 0.7390 | 0.8 | 0.3088 | 1.3950 | 0.8000 | 0.7684 | 0.2058 | 0.0910 | | 0.1528 | 64.0 | 800 | 0.7431 | 0.795 | 0.3110 | 1.3722 | 0.795 | 0.7656 | 0.1959 | 0.0914 | | 0.1528 | 64.96 | 812 | 0.7429 | 0.8 | 0.3117 | 1.3673 | 0.8000 | 0.7704 | 0.1933 | 0.0925 | | 0.1528 | 66.0 | 825 | 0.7419 | 0.8 | 0.3115 | 1.3663 | 0.8000 | 0.7704 | 0.1986 | 0.0913 | | 0.1528 | 66.96 | 837 | 0.7434 | 0.8 | 0.3120 | 1.3672 | 0.8000 | 0.7704 | 0.1929 | 0.0927 | | 0.1528 | 68.0 | 850 | 0.7414 | 0.8 | 0.3115 | 1.3649 | 0.8000 | 0.7704 | 0.1988 | 0.0923 | | 0.1528 | 68.96 | 862 | 0.7448 | 0.8 | 0.3129 | 1.3685 | 0.8000 | 0.7704 | 0.2251 | 0.0928 | | 0.1528 | 70.0 | 875 | 0.7450 | 0.8 | 0.3130 | 1.3657 | 0.8000 | 0.7704 | 0.1969 | 0.0934 | | 0.1528 | 70.96 | 887 | 0.7464 | 0.8 | 0.3132 | 1.3686 | 0.8000 | 0.7704 | 0.1988 | 0.0946 | | 0.1528 | 72.0 | 900 | 0.7465 | 0.8 | 0.3138 | 1.3682 | 0.8000 | 0.7707 | 0.2015 | 0.0935 | | 0.1528 | 72.96 | 912 | 0.7471 | 0.8 | 0.3142 | 1.3685 | 0.8000 | 0.7696 | 0.2093 | 0.0936 | | 0.1528 | 74.0 | 925 | 0.7476 | 0.8 | 0.3145 | 1.3685 | 0.8000 | 0.7704 | 0.2120 | 0.0942 | | 0.1528 | 74.96 | 937 | 0.7433 | 0.8 | 0.3126 | 1.3655 | 0.8000 | 0.7696 | 0.1907 | 0.0918 | | 0.1528 | 76.0 | 950 | 0.7519 | 0.8 | 0.3166 | 1.3683 | 0.8000 | 0.7704 | 0.2111 | 0.0949 | | 0.1528 | 76.96 | 962 | 0.7485 | 0.8 | 0.3152 | 1.3679 | 0.8000 | 0.7696 | 0.2035 | 0.0942 | | 0.1528 | 78.0 | 975 | 0.7496 | 0.8 | 0.3155 | 1.3716 | 0.8000 | 0.7704 | 0.1931 | 0.0940 | | 0.1528 | 78.96 | 987 | 0.7513 | 0.8 | 0.3163 | 1.3684 | 0.8000 | 0.7704 | 0.1888 | 0.0952 | | 0.1059 | 80.0 | 1000 | 0.7490 | 0.8 | 0.3157 | 1.3707 | 0.8000 | 0.7691 | 0.2118 | 0.0943 | | 0.1059 | 80.96 | 1012 | 0.7482 | 0.8 | 0.3151 | 1.3687 | 0.8000 | 0.7696 | 0.2060 | 0.0945 | | 0.1059 | 82.0 | 1025 | 0.7516 | 0.8 | 0.3163 | 1.3682 | 0.8000 | 0.7704 | 0.2248 | 0.0950 | | 0.1059 | 82.96 | 1037 | 0.7527 | 0.8 | 0.3174 | 1.3660 | 0.8000 | 0.7707 | 0.2204 | 0.0948 | | 0.1059 | 84.0 | 1050 | 0.7495 | 0.8 | 0.3156 | 1.3663 | 0.8000 | 0.7704 | 0.2067 | 0.0939 | | 0.1059 | 84.96 | 1062 | 0.7506 | 0.8 | 0.3161 | 1.3659 | 0.8000 | 0.7707 | 0.2086 | 0.0947 | | 0.1059 | 86.0 | 1075 | 0.7537 | 0.8 | 0.3179 | 1.3687 | 0.8000 | 0.7698 | 0.2001 | 0.0955 | | 0.1059 | 86.96 | 1087 | 0.7525 | 0.8 | 0.3172 | 1.3693 | 0.8000 | 0.7696 | 0.2096 | 0.0947 | | 0.1059 | 88.0 | 1100 | 0.7535 | 0.8 | 0.3175 | 1.3675 | 0.8000 | 0.7704 | 0.2091 | 0.0951 | | 0.1059 | 88.96 | 1112 | 0.7525 | 0.8 | 0.3172 | 1.3680 | 0.8000 | 0.7696 | 0.2009 | 0.0946 | | 0.1059 | 90.0 | 1125 | 0.7566 | 0.8 | 0.3190 | 1.3725 | 0.8000 | 0.7698 | 0.1881 | 0.0964 | | 0.1059 | 90.96 | 1137 | 0.7578 | 0.8 | 0.3195 | 1.3726 | 0.8000 | 0.7704 | 0.1880 | 0.0968 | | 0.1059 | 92.0 | 1150 | 0.7560 | 0.8 | 0.3186 | 1.3715 | 0.8000 | 0.7707 | 0.2095 | 0.0969 | | 0.1059 | 92.96 | 1162 | 0.7623 | 0.795 | 0.3219 | 1.3765 | 0.795 | 0.7681 | 0.1907 | 0.0979 | | 0.1059 | 94.0 | 1175 | 0.7567 | 0.8 | 0.3192 | 1.3709 | 0.8000 | 0.7698 | 0.2000 | 0.0953 | | 0.1059 | 94.96 | 1187 | 0.7538 | 0.8 | 0.3181 | 1.3708 | 0.8000 | 0.7691 | 0.1986 | 0.0958 | | 0.1059 | 96.0 | 1200 | 0.7530 | 0.8 | 0.3175 | 1.3725 | 0.8000 | 0.7693 | 0.2122 | 0.0958 | | 0.1059 | 96.96 | 1212 | 0.7607 | 0.8 | 0.3207 | 1.3730 | 0.8000 | 0.7709 | 0.1906 | 0.0972 | | 0.1059 | 98.0 | 1225 | 0.7647 | 0.79 | 0.3229 | 1.3411 | 0.79 | 0.7633 | 0.1889 | 0.0984 | | 0.1059 | 98.96 | 1237 | 0.7568 | 0.79 | 0.3191 | 1.4425 | 0.79 | 0.7644 | 0.1911 | 0.0976 | | 0.1059 | 100.0 | 1250 | 0.7586 | 0.8 | 0.3200 | 1.4486 | 0.8000 | 0.7696 | 0.2009 | 0.0952 | | 0.1059 | 100.96 | 1262 | 0.7552 | 0.8 | 0.3192 | 1.3728 | 0.8000 | 0.7696 | 0.1962 | 0.0953 | | 0.1059 | 102.0 | 1275 | 0.7601 | 0.8 | 0.3217 | 1.4309 | 0.8000 | 0.7691 | 0.2071 | 0.0969 | | 0.1059 | 102.96 | 1287 | 0.7608 | 0.795 | 0.3213 | 1.3702 | 0.795 | 0.7668 | 0.2068 | 0.0967 | | 0.1059 | 104.0 | 1300 | 0.7590 | 0.795 | 0.3202 | 1.3748 | 0.795 | 0.7668 | 0.1922 | 0.0967 | | 0.1059 | 104.96 | 1312 | 0.7626 | 0.795 | 0.3222 | 1.3775 | 0.795 | 0.7673 | 0.1917 | 0.0974 | | 0.1059 | 106.0 | 1325 | 0.7632 | 0.795 | 0.3228 | 1.3765 | 0.795 | 0.7664 | 0.2072 | 0.0977 | | 0.1059 | 106.96 | 1337 | 0.7612 | 0.795 | 0.3223 | 1.3764 | 0.795 | 0.7666 | 0.2011 | 0.0974 | | 0.1059 | 108.0 | 1350 | 0.7669 | 0.79 | 0.3246 | 1.3777 | 0.79 | 0.7639 | 0.2005 | 0.0981 | | 0.1059 | 108.96 | 1362 | 0.7658 | 0.795 | 0.3238 | 1.3782 | 0.795 | 0.7673 | 0.2099 | 0.0983 | | 0.1059 | 110.0 | 1375 | 0.7632 | 0.79 | 0.3232 | 1.3766 | 0.79 | 0.7639 | 0.2002 | 0.0978 | | 0.1059 | 110.96 | 1387 | 0.7651 | 0.79 | 0.3236 | 1.3758 | 0.79 | 0.7580 | 0.2134 | 0.0980 | | 0.1059 | 112.0 | 1400 | 0.7649 | 0.79 | 0.3235 | 1.3765 | 0.79 | 0.7583 | 0.1975 | 0.0982 | | 0.1059 | 112.96 | 1412 | 0.7683 | 0.79 | 0.3253 | 1.3759 | 0.79 | 0.7639 | 0.1913 | 0.0986 | | 0.1059 | 114.0 | 1425 | 0.7682 | 0.795 | 0.3253 | 1.3778 | 0.795 | 0.7668 | 0.1986 | 0.0984 | | 0.1059 | 114.96 | 1437 | 0.7666 | 0.79 | 0.3245 | 1.3771 | 0.79 | 0.7639 | 0.1838 | 0.0976 | | 0.1059 | 116.0 | 1450 | 0.7685 | 0.79 | 0.3255 | 1.3802 | 0.79 | 0.7639 | 0.1962 | 0.0988 | | 0.1059 | 116.96 | 1462 | 0.7676 | 0.785 | 0.3249 | 1.3783 | 0.785 | 0.7554 | 0.2011 | 0.0991 | | 0.1059 | 118.0 | 1475 | 0.7704 | 0.785 | 0.3262 | 1.3789 | 0.785 | 0.7516 | 0.2098 | 0.0995 | | 0.1059 | 118.96 | 1487 | 0.7701 | 0.785 | 0.3262 | 1.3806 | 0.785 | 0.7554 | 0.2167 | 0.0991 | | 0.0842 | 120.0 | 1500 | 0.7708 | 0.79 | 0.3267 | 1.3815 | 0.79 | 0.7639 | 0.1934 | 0.0990 | | 0.0842 | 120.96 | 1512 | 0.7710 | 0.785 | 0.3265 | 1.3790 | 0.785 | 0.7516 | 0.1928 | 0.0995 | | 0.0842 | 122.0 | 1525 | 0.7728 | 0.79 | 0.3274 | 1.3830 | 0.79 | 0.7639 | 0.1917 | 0.0996 | | 0.0842 | 122.96 | 1537 | 0.7708 | 0.785 | 0.3267 | 1.3826 | 0.785 | 0.7554 | 0.2057 | 0.0992 | | 0.0842 | 124.0 | 1550 | 0.7698 | 0.785 | 0.3262 | 1.3777 | 0.785 | 0.7554 | 0.2021 | 0.0996 | | 0.0842 | 124.96 | 1562 | 0.7706 | 0.785 | 0.3267 | 1.3825 | 0.785 | 0.7554 | 0.1949 | 0.0994 | | 0.0842 | 126.0 | 1575 | 0.7751 | 0.79 | 0.3290 | 1.3827 | 0.79 | 0.7639 | 0.1945 | 0.1002 | | 0.0842 | 126.96 | 1587 | 0.7734 | 0.79 | 0.3282 | 1.3855 | 0.79 | 0.7639 | 0.1882 | 0.0998 | | 0.0842 | 128.0 | 1600 | 0.7753 | 0.785 | 0.3290 | 1.3842 | 0.785 | 0.7554 | 0.1926 | 0.1006 | | 0.0842 | 128.96 | 1612 | 0.7731 | 0.78 | 0.3278 | 1.3808 | 0.78 | 0.7488 | 0.2115 | 0.0994 | | 0.0842 | 130.0 | 1625 | 0.7723 | 0.78 | 0.3276 | 1.3830 | 0.78 | 0.7486 | 0.2005 | 0.0994 | | 0.0842 | 130.96 | 1637 | 0.7746 | 0.78 | 0.3287 | 1.3810 | 0.78 | 0.7488 | 0.2103 | 0.0999 | | 0.0842 | 132.0 | 1650 | 0.7758 | 0.78 | 0.3291 | 1.3848 | 0.78 | 0.7488 | 0.2138 | 0.1004 | | 0.0842 | 132.96 | 1662 | 0.7771 | 0.78 | 0.3299 | 1.3833 | 0.78 | 0.7488 | 0.2080 | 0.1007 | | 0.0842 | 134.0 | 1675 | 0.7757 | 0.78 | 0.3295 | 1.3837 | 0.78 | 0.7488 | 0.2060 | 0.0999 | | 0.0842 | 134.96 | 1687 | 0.7754 | 0.78 | 0.3292 | 1.3836 | 0.78 | 0.7488 | 0.2070 | 0.1001 | | 0.0842 | 136.0 | 1700 | 0.7755 | 0.78 | 0.3294 | 1.3834 | 0.78 | 0.7488 | 0.2057 | 0.0999 | | 0.0842 | 136.96 | 1712 | 0.7755 | 0.78 | 0.3295 | 1.3874 | 0.78 | 0.7488 | 0.1999 | 0.0998 | | 0.0842 | 138.0 | 1725 | 0.7759 | 0.78 | 0.3296 | 1.4380 | 0.78 | 0.7488 | 0.2007 | 0.1002 | | 0.0842 | 138.96 | 1737 | 0.7776 | 0.78 | 0.3303 | 1.3868 | 0.78 | 0.7488 | 0.2091 | 0.1002 | | 0.0842 | 140.0 | 1750 | 0.7780 | 0.78 | 0.3304 | 1.3868 | 0.78 | 0.7488 | 0.2086 | 0.1005 | | 0.0842 | 140.96 | 1762 | 0.7780 | 0.78 | 0.3306 | 1.3855 | 0.78 | 0.7488 | 0.2065 | 0.1001 | | 0.0842 | 142.0 | 1775 | 0.7789 | 0.78 | 0.3311 | 1.3855 | 0.78 | 0.7488 | 0.2067 | 0.1003 | | 0.0842 | 142.96 | 1787 | 0.7798 | 0.78 | 0.3314 | 1.3856 | 0.78 | 0.7488 | 0.2083 | 0.1010 | | 0.0842 | 144.0 | 1800 | 0.7799 | 0.78 | 0.3315 | 1.3914 | 0.78 | 0.7488 | 0.2161 | 0.1004 | | 0.0842 | 144.96 | 1812 | 0.7806 | 0.78 | 0.3317 | 1.3857 | 0.78 | 0.7488 | 0.2024 | 0.1008 | | 0.0842 | 146.0 | 1825 | 0.7817 | 0.78 | 0.3322 | 1.3947 | 0.78 | 0.7488 | 0.2043 | 0.1009 | | 0.0842 | 146.96 | 1837 | 0.7815 | 0.78 | 0.3324 | 1.3898 | 0.78 | 0.7488 | 0.2118 | 0.1006 | | 0.0842 | 148.0 | 1850 | 0.7820 | 0.78 | 0.3326 | 1.3874 | 0.78 | 0.7488 | 0.2114 | 0.1008 | | 0.0842 | 148.96 | 1862 | 0.7821 | 0.78 | 0.3327 | 1.4391 | 0.78 | 0.7488 | 0.2087 | 0.1006 | | 0.0842 | 150.0 | 1875 | 0.7816 | 0.78 | 0.3324 | 1.4410 | 0.78 | 0.7488 | 0.2160 | 0.1006 | | 0.0842 | 150.96 | 1887 | 0.7826 | 0.78 | 0.3328 | 1.3927 | 0.78 | 0.7488 | 0.2011 | 0.1007 | | 0.0842 | 152.0 | 1900 | 0.7834 | 0.78 | 0.3332 | 1.4411 | 0.78 | 0.7488 | 0.1994 | 0.1009 | | 0.0842 | 152.96 | 1912 | 0.7830 | 0.78 | 0.3331 | 1.4409 | 0.78 | 0.7488 | 0.1967 | 0.1008 | | 0.0842 | 154.0 | 1925 | 0.7825 | 0.78 | 0.3329 | 1.4412 | 0.78 | 0.7488 | 0.2070 | 0.1008 | | 0.0842 | 154.96 | 1937 | 0.7827 | 0.78 | 0.3332 | 1.4399 | 0.78 | 0.7488 | 0.2097 | 0.1007 | | 0.0842 | 156.0 | 1950 | 0.7822 | 0.78 | 0.3329 | 1.4391 | 0.78 | 0.7488 | 0.1986 | 0.1006 | | 0.0842 | 156.96 | 1962 | 0.7838 | 0.78 | 0.3335 | 1.4414 | 0.78 | 0.7488 | 0.1967 | 0.1007 | | 0.0842 | 158.0 | 1975 | 0.7845 | 0.78 | 0.3337 | 1.3899 | 0.78 | 0.7488 | 0.2087 | 0.1008 | | 0.0842 | 158.96 | 1987 | 0.7846 | 0.78 | 0.3340 | 1.4419 | 0.78 | 0.7488 | 0.2002 | 0.1007 | | 0.0731 | 160.0 | 2000 | 0.7853 | 0.78 | 0.3343 | 1.4419 | 0.78 | 0.7488 | 0.1967 | 0.1010 | | 0.0731 | 160.96 | 2012 | 0.7853 | 0.78 | 0.3343 | 1.4425 | 0.78 | 0.7488 | 0.1963 | 0.1012 | | 0.0731 | 162.0 | 2025 | 0.7846 | 0.78 | 0.3341 | 1.4411 | 0.78 | 0.7488 | 0.1998 | 0.1008 | | 0.0731 | 162.96 | 2037 | 0.7856 | 0.78 | 0.3345 | 1.4419 | 0.78 | 0.7488 | 0.2000 | 0.1011 | | 0.0731 | 164.0 | 2050 | 0.7863 | 0.78 | 0.3348 | 1.4425 | 0.78 | 0.7488 | 0.1991 | 0.1014 | | 0.0731 | 164.96 | 2062 | 0.7863 | 0.78 | 0.3349 | 1.4414 | 0.78 | 0.7488 | 0.1969 | 0.1013 | | 0.0731 | 166.0 | 2075 | 0.7865 | 0.78 | 0.3349 | 1.4425 | 0.78 | 0.7488 | 0.1994 | 0.1015 | | 0.0731 | 166.96 | 2087 | 0.7863 | 0.78 | 0.3349 | 1.4412 | 0.78 | 0.7488 | 0.1966 | 0.1015 | | 0.0731 | 168.0 | 2100 | 0.7870 | 0.78 | 0.3352 | 1.4431 | 0.78 | 0.7488 | 0.1944 | 0.1016 | | 0.0731 | 168.96 | 2112 | 0.7868 | 0.78 | 0.3351 | 1.4425 | 0.78 | 0.7488 | 0.1919 | 0.1017 | | 0.0731 | 170.0 | 2125 | 0.7873 | 0.78 | 0.3354 | 1.4424 | 0.78 | 0.7488 | 0.1965 | 0.1017 | | 0.0731 | 170.96 | 2137 | 0.7870 | 0.78 | 0.3352 | 1.4418 | 0.78 | 0.7488 | 0.2081 | 0.1014 | | 0.0731 | 172.0 | 2150 | 0.7879 | 0.78 | 0.3356 | 1.4431 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 172.96 | 2162 | 0.7874 | 0.78 | 0.3355 | 1.4421 | 0.78 | 0.7488 | 0.1967 | 0.1015 | | 0.0731 | 174.0 | 2175 | 0.7872 | 0.78 | 0.3355 | 1.4418 | 0.78 | 0.7488 | 0.1965 | 0.1016 | | 0.0731 | 174.96 | 2187 | 0.7882 | 0.78 | 0.3358 | 1.4433 | 0.78 | 0.7488 | 0.1945 | 0.1017 | | 0.0731 | 176.0 | 2200 | 0.7882 | 0.78 | 0.3358 | 1.4423 | 0.78 | 0.7488 | 0.1969 | 0.1016 | | 0.0731 | 176.96 | 2212 | 0.7885 | 0.78 | 0.3361 | 1.4431 | 0.78 | 0.7488 | 0.1948 | 0.1017 | | 0.0731 | 178.0 | 2225 | 0.7883 | 0.78 | 0.3359 | 1.4428 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 178.96 | 2237 | 0.7882 | 0.78 | 0.3359 | 1.4426 | 0.78 | 0.7488 | 0.1920 | 0.1017 | | 0.0731 | 180.0 | 2250 | 0.7884 | 0.78 | 0.3360 | 1.4425 | 0.78 | 0.7488 | 0.2000 | 0.1016 | | 0.0731 | 180.96 | 2262 | 0.7886 | 0.78 | 0.3361 | 1.4431 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 182.0 | 2275 | 0.7888 | 0.78 | 0.3362 | 1.4428 | 0.78 | 0.7488 | 0.1977 | 0.1016 | | 0.0731 | 182.96 | 2287 | 0.7889 | 0.78 | 0.3362 | 1.4428 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 184.0 | 2300 | 0.7889 | 0.78 | 0.3362 | 1.4431 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 184.96 | 2312 | 0.7889 | 0.78 | 0.3362 | 1.4427 | 0.78 | 0.7488 | 0.1946 | 0.1017 | | 0.0731 | 186.0 | 2325 | 0.7889 | 0.78 | 0.3362 | 1.4431 | 0.78 | 0.7488 | 0.1922 | 0.1018 | | 0.0731 | 186.96 | 2337 | 0.7892 | 0.78 | 0.3364 | 1.4432 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 188.0 | 2350 | 0.7890 | 0.78 | 0.3363 | 1.4427 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 188.96 | 2362 | 0.7892 | 0.78 | 0.3364 | 1.4429 | 0.78 | 0.7488 | 0.1922 | 0.1017 | | 0.0731 | 190.0 | 2375 | 0.7893 | 0.78 | 0.3364 | 1.4433 | 0.78 | 0.7488 | 0.1945 | 0.1017 | | 0.0731 | 190.96 | 2387 | 0.7893 | 0.78 | 0.3364 | 1.4430 | 0.78 | 0.7488 | 0.1921 | 0.1017 | | 0.0731 | 192.0 | 2400 | 0.7893 | 0.78 | 0.3364 | 1.4430 | 0.78 | 0.7488 | 0.1922 | 0.1018 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/vit-base_tobacco_wr_0.01_wd_0.2

# vit-base_tobacco_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9831 - Accuracy: 0.755 - Brier Loss: 0.3936 - Nll: 1.4572 - F1 Micro: 0.755 - F1 Macro: 0.7245 - Ece: 0.2101 - Aurc: 0.1059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7384 | 0.815 | 0.3056 | 1.1863 | 0.815 | 0.8021 | 0.2211 | 0.0789 | | No log | 2.0 | 25 | 0.7504 | 0.795 | 0.3109 | 1.2594 | 0.795 | 0.7691 | 0.2235 | 0.0789 | | No log | 2.96 | 37 | 0.7415 | 0.765 | 0.3109 | 1.0445 | 0.765 | 0.7422 | 0.2323 | 0.0762 | | No log | 4.0 | 50 | 0.7222 | 0.805 | 0.2989 | 1.3200 | 0.805 | 0.7951 | 0.2028 | 0.0682 | | No log | 4.96 | 62 | 0.7160 | 0.815 | 0.2989 | 1.2307 | 0.815 | 0.8067 | 0.2381 | 0.0688 | | No log | 6.0 | 75 | 0.7149 | 0.825 | 0.2950 | 1.3097 | 0.825 | 0.8127 | 0.2110 | 0.0809 | | No log | 6.96 | 87 | 0.7157 | 0.815 | 0.2986 | 1.2310 | 0.815 | 0.8007 | 0.2195 | 0.0779 | | No log | 8.0 | 100 | 0.7245 | 0.81 | 0.3020 | 1.3030 | 0.81 | 0.7949 | 0.1997 | 0.0834 | | No log | 8.96 | 112 | 0.7111 | 0.815 | 0.2972 | 1.2310 | 0.815 | 0.8009 | 0.2090 | 0.0793 | | No log | 10.0 | 125 | 0.7344 | 0.81 | 0.3087 | 1.2974 | 0.81 | 0.7956 | 0.2240 | 0.0913 | | No log | 10.96 | 137 | 0.7264 | 0.81 | 0.3057 | 1.3216 | 0.81 | 0.7907 | 0.2074 | 0.0879 | | No log | 12.0 | 150 | 0.7301 | 0.805 | 0.3056 | 1.2448 | 0.805 | 0.7919 | 0.2112 | 0.0932 | | No log | 12.96 | 162 | 0.7224 | 0.805 | 0.3020 | 1.2950 | 0.805 | 0.7899 | 0.1930 | 0.0915 | | No log | 14.0 | 175 | 0.7235 | 0.81 | 0.3045 | 1.2965 | 0.81 | 0.7956 | 0.2025 | 0.0902 | | No log | 14.96 | 187 | 0.7239 | 0.81 | 0.3039 | 1.2970 | 0.81 | 0.7956 | 0.2156 | 0.0906 | | No log | 16.0 | 200 | 0.7293 | 0.81 | 0.3060 | 1.4336 | 0.81 | 0.7924 | 0.2029 | 0.0955 | | No log | 16.96 | 212 | 0.7253 | 0.805 | 0.3044 | 1.2984 | 0.805 | 0.7853 | 0.1946 | 0.0911 | | No log | 18.0 | 225 | 0.7293 | 0.805 | 0.3076 | 1.1852 | 0.805 | 0.7861 | 0.2015 | 0.0916 | | No log | 18.96 | 237 | 0.7328 | 0.8 | 0.3050 | 1.3844 | 0.8000 | 0.7836 | 0.1900 | 0.0986 | | No log | 20.0 | 250 | 0.7263 | 0.8 | 0.3058 | 1.2943 | 0.8000 | 0.7754 | 0.2003 | 0.0900 | | No log | 20.96 | 262 | 0.7370 | 0.805 | 0.3102 | 1.3612 | 0.805 | 0.7818 | 0.1998 | 0.0939 | | No log | 22.0 | 275 | 0.7412 | 0.795 | 0.3118 | 1.3673 | 0.795 | 0.7714 | 0.1952 | 0.0957 | | No log | 22.96 | 287 | 0.7326 | 0.795 | 0.3077 | 1.3572 | 0.795 | 0.7666 | 0.1935 | 0.0925 | | No log | 24.0 | 300 | 0.7308 | 0.805 | 0.3076 | 1.4265 | 0.805 | 0.7818 | 0.1968 | 0.0906 | | No log | 24.96 | 312 | 0.7424 | 0.8 | 0.3122 | 1.3666 | 0.8000 | 0.7774 | 0.1853 | 0.0960 | | No log | 26.0 | 325 | 0.7383 | 0.8 | 0.3097 | 1.3644 | 0.8000 | 0.7774 | 0.1911 | 0.0952 | | No log | 26.96 | 337 | 0.7468 | 0.805 | 0.3143 | 1.4295 | 0.805 | 0.7829 | 0.1952 | 0.0971 | | No log | 28.0 | 350 | 0.7484 | 0.795 | 0.3135 | 1.4327 | 0.795 | 0.7668 | 0.1906 | 0.0989 | | No log | 28.96 | 362 | 0.7459 | 0.8 | 0.3135 | 1.3624 | 0.8000 | 0.7704 | 0.1945 | 0.0944 | | No log | 30.0 | 375 | 0.7513 | 0.8 | 0.3160 | 1.4282 | 0.8000 | 0.7698 | 0.1991 | 0.0953 | | No log | 30.96 | 387 | 0.7535 | 0.795 | 0.3168 | 1.4336 | 0.795 | 0.7656 | 0.1799 | 0.0980 | | No log | 32.0 | 400 | 0.7540 | 0.8 | 0.3171 | 1.4469 | 0.8000 | 0.7774 | 0.1943 | 0.0959 | | No log | 32.96 | 412 | 0.7566 | 0.8 | 0.3180 | 1.3772 | 0.8000 | 0.7704 | 0.1809 | 0.0972 | | No log | 34.0 | 425 | 0.7641 | 0.795 | 0.3220 | 1.3878 | 0.795 | 0.7691 | 0.1937 | 0.1021 | | No log | 34.96 | 437 | 0.7602 | 0.795 | 0.3192 | 1.3748 | 0.795 | 0.7656 | 0.1908 | 0.0973 | | No log | 36.0 | 450 | 0.7577 | 0.8 | 0.3186 | 1.4397 | 0.8000 | 0.7704 | 0.1858 | 0.0951 | | No log | 36.96 | 462 | 0.7724 | 0.795 | 0.3233 | 1.3800 | 0.795 | 0.7656 | 0.1796 | 0.1002 | | No log | 38.0 | 475 | 0.7675 | 0.795 | 0.3223 | 1.3659 | 0.795 | 0.7654 | 0.1862 | 0.0976 | | No log | 38.96 | 487 | 0.7772 | 0.79 | 0.3261 | 1.4406 | 0.79 | 0.7633 | 0.1877 | 0.1062 | | 0.0949 | 40.0 | 500 | 0.7631 | 0.795 | 0.3211 | 1.4302 | 0.795 | 0.7654 | 0.1896 | 0.0953 | | 0.0949 | 40.96 | 512 | 0.7878 | 0.79 | 0.3304 | 1.5689 | 0.79 | 0.7631 | 0.1770 | 0.1014 | | 0.0949 | 42.0 | 525 | 0.7639 | 0.815 | 0.3198 | 1.5524 | 0.815 | 0.7879 | 0.1890 | 0.0938 | | 0.0949 | 42.96 | 537 | 0.7967 | 0.785 | 0.3350 | 1.4516 | 0.785 | 0.7606 | 0.1809 | 0.1040 | | 0.0949 | 44.0 | 550 | 0.7735 | 0.81 | 0.3237 | 1.4915 | 0.81 | 0.7784 | 0.2079 | 0.0963 | | 0.0949 | 44.96 | 562 | 0.7859 | 0.79 | 0.3300 | 1.5076 | 0.79 | 0.7594 | 0.1851 | 0.0991 | | 0.0949 | 46.0 | 575 | 0.7917 | 0.79 | 0.3323 | 1.5062 | 0.79 | 0.7631 | 0.1944 | 0.1015 | | 0.0949 | 46.96 | 587 | 0.7879 | 0.795 | 0.3310 | 1.4304 | 0.795 | 0.7656 | 0.1761 | 0.0987 | | 0.0949 | 48.0 | 600 | 0.7917 | 0.79 | 0.3320 | 1.4382 | 0.79 | 0.7629 | 0.1828 | 0.0994 | | 0.0949 | 48.96 | 612 | 0.7950 | 0.79 | 0.3333 | 1.4929 | 0.79 | 0.7549 | 0.1811 | 0.0995 | | 0.0949 | 50.0 | 625 | 0.8005 | 0.79 | 0.3363 | 1.4351 | 0.79 | 0.7631 | 0.1953 | 0.1009 | | 0.0949 | 50.96 | 637 | 0.8019 | 0.79 | 0.3363 | 1.4891 | 0.79 | 0.7631 | 0.1837 | 0.0998 | | 0.0949 | 52.0 | 650 | 0.8021 | 0.79 | 0.3354 | 1.4899 | 0.79 | 0.7586 | 0.1990 | 0.0994 | | 0.0949 | 52.96 | 662 | 0.8012 | 0.795 | 0.3346 | 1.4913 | 0.795 | 0.7648 | 0.1878 | 0.0984 | | 0.0949 | 54.0 | 675 | 0.8078 | 0.785 | 0.3388 | 1.4920 | 0.785 | 0.7559 | 0.1938 | 0.1000 | | 0.0949 | 54.96 | 687 | 0.8092 | 0.785 | 0.3391 | 1.5003 | 0.785 | 0.7561 | 0.1878 | 0.1004 | | 0.0949 | 56.0 | 700 | 0.8095 | 0.795 | 0.3378 | 1.4914 | 0.795 | 0.7634 | 0.1879 | 0.0994 | | 0.0949 | 56.96 | 712 | 0.8124 | 0.785 | 0.3396 | 1.4926 | 0.785 | 0.7555 | 0.1996 | 0.1005 | | 0.0949 | 58.0 | 725 | 0.8120 | 0.78 | 0.3391 | 1.4405 | 0.78 | 0.7469 | 0.1924 | 0.1005 | | 0.0949 | 58.96 | 737 | 0.8185 | 0.785 | 0.3406 | 1.5007 | 0.785 | 0.7523 | 0.1910 | 0.1013 | | 0.0949 | 60.0 | 750 | 0.8182 | 0.785 | 0.3421 | 1.4371 | 0.785 | 0.7555 | 0.1727 | 0.1010 | | 0.0949 | 60.96 | 762 | 0.8224 | 0.78 | 0.3443 | 1.4404 | 0.78 | 0.7475 | 0.1941 | 0.1019 | | 0.0949 | 62.0 | 775 | 0.8267 | 0.78 | 0.3463 | 1.4995 | 0.78 | 0.7535 | 0.1927 | 0.1016 | | 0.0949 | 62.96 | 787 | 0.8252 | 0.775 | 0.3447 | 1.4965 | 0.775 | 0.7465 | 0.1798 | 0.1016 | | 0.0949 | 64.0 | 800 | 0.8286 | 0.78 | 0.3446 | 1.4987 | 0.78 | 0.7475 | 0.1911 | 0.1014 | | 0.0949 | 64.96 | 812 | 0.8308 | 0.78 | 0.3467 | 1.4438 | 0.78 | 0.7469 | 0.1866 | 0.1019 | | 0.0949 | 66.0 | 825 | 0.8346 | 0.775 | 0.3476 | 1.4592 | 0.775 | 0.7444 | 0.1878 | 0.1034 | | 0.0949 | 66.96 | 837 | 0.8348 | 0.78 | 0.3484 | 1.4423 | 0.78 | 0.7515 | 0.1821 | 0.1021 | | 0.0949 | 68.0 | 850 | 0.8376 | 0.78 | 0.3481 | 1.4970 | 0.78 | 0.7469 | 0.1922 | 0.1015 | | 0.0949 | 68.96 | 862 | 0.8379 | 0.78 | 0.3497 | 1.4388 | 0.78 | 0.7515 | 0.1850 | 0.1018 | | 0.0949 | 70.0 | 875 | 0.8452 | 0.78 | 0.3512 | 1.4974 | 0.78 | 0.7525 | 0.1828 | 0.1036 | | 0.0949 | 70.96 | 887 | 0.8437 | 0.78 | 0.3511 | 1.4519 | 0.78 | 0.7469 | 0.2076 | 0.1032 | | 0.0949 | 72.0 | 900 | 0.8485 | 0.775 | 0.3535 | 1.5163 | 0.775 | 0.7444 | 0.1863 | 0.1033 | | 0.0949 | 72.96 | 912 | 0.8516 | 0.77 | 0.3555 | 1.5015 | 0.7700 | 0.7404 | 0.1868 | 0.1030 | | 0.0949 | 74.0 | 925 | 0.8507 | 0.77 | 0.3541 | 1.4407 | 0.7700 | 0.7404 | 0.1996 | 0.1026 | | 0.0949 | 74.96 | 937 | 0.8508 | 0.77 | 0.3540 | 1.4424 | 0.7700 | 0.7400 | 0.1996 | 0.1025 | | 0.0949 | 76.0 | 950 | 0.8559 | 0.77 | 0.3558 | 1.4487 | 0.7700 | 0.7400 | 0.2001 | 0.1031 | | 0.0949 | 76.96 | 962 | 0.8564 | 0.77 | 0.3568 | 1.4389 | 0.7700 | 0.7400 | 0.1804 | 0.1024 | | 0.0949 | 78.0 | 975 | 0.8611 | 0.775 | 0.3569 | 1.4966 | 0.775 | 0.7440 | 0.1831 | 0.1031 | | 0.0949 | 78.96 | 987 | 0.8573 | 0.77 | 0.3557 | 1.4946 | 0.7700 | 0.7400 | 0.1933 | 0.1013 | | 0.0366 | 80.0 | 1000 | 0.8640 | 0.775 | 0.3587 | 1.4998 | 0.775 | 0.7440 | 0.1871 | 0.1032 | | 0.0366 | 80.96 | 1012 | 0.8640 | 0.77 | 0.3585 | 1.4983 | 0.7700 | 0.7400 | 0.1921 | 0.1032 | | 0.0366 | 82.0 | 1025 | 0.8696 | 0.77 | 0.3608 | 1.5032 | 0.7700 | 0.7400 | 0.2033 | 0.1036 | | 0.0366 | 82.96 | 1037 | 0.8702 | 0.77 | 0.3613 | 1.4987 | 0.7700 | 0.7400 | 0.2022 | 0.1029 | | 0.0366 | 84.0 | 1050 | 0.8686 | 0.77 | 0.3597 | 1.4446 | 0.7700 | 0.7400 | 0.1887 | 0.1028 | | 0.0366 | 84.96 | 1062 | 0.8700 | 0.77 | 0.3607 | 1.4365 | 0.7700 | 0.7400 | 0.1900 | 0.1025 | | 0.0366 | 86.0 | 1075 | 0.8756 | 0.765 | 0.3621 | 1.5009 | 0.765 | 0.7308 | 0.1983 | 0.1040 | | 0.0366 | 86.96 | 1087 | 0.8768 | 0.76 | 0.3623 | 1.5035 | 0.76 | 0.7282 | 0.1956 | 0.1040 | | 0.0366 | 88.0 | 1100 | 0.8762 | 0.765 | 0.3618 | 1.4409 | 0.765 | 0.7308 | 0.1957 | 0.1033 | | 0.0366 | 88.96 | 1112 | 0.8777 | 0.765 | 0.3629 | 1.4427 | 0.765 | 0.7308 | 0.1974 | 0.1036 | | 0.0366 | 90.0 | 1125 | 0.8854 | 0.76 | 0.3661 | 1.4681 | 0.76 | 0.7282 | 0.2023 | 0.1049 | | 0.0366 | 90.96 | 1137 | 0.8867 | 0.76 | 0.3660 | 1.5057 | 0.76 | 0.7282 | 0.1995 | 0.1049 | | 0.0366 | 92.0 | 1150 | 0.8849 | 0.765 | 0.3648 | 1.5011 | 0.765 | 0.7308 | 0.1953 | 0.1039 | | 0.0366 | 92.96 | 1162 | 0.8898 | 0.76 | 0.3670 | 1.5077 | 0.76 | 0.7282 | 0.2083 | 0.1045 | | 0.0366 | 94.0 | 1175 | 0.8891 | 0.765 | 0.3662 | 1.4520 | 0.765 | 0.7308 | 0.2091 | 0.1040 | | 0.0366 | 94.96 | 1187 | 0.8910 | 0.755 | 0.3679 | 1.4460 | 0.755 | 0.7247 | 0.2039 | 0.1043 | | 0.0366 | 96.0 | 1200 | 0.8935 | 0.76 | 0.3684 | 1.4435 | 0.76 | 0.7273 | 0.1929 | 0.1041 | | 0.0366 | 96.96 | 1212 | 0.8964 | 0.755 | 0.3689 | 1.4526 | 0.755 | 0.7247 | 0.1980 | 0.1048 | | 0.0366 | 98.0 | 1225 | 0.8979 | 0.755 | 0.3701 | 1.4507 | 0.755 | 0.7247 | 0.2017 | 0.1045 | | 0.0366 | 98.96 | 1237 | 0.8965 | 0.755 | 0.3692 | 1.4474 | 0.755 | 0.7247 | 0.2057 | 0.1044 | | 0.0366 | 100.0 | 1250 | 0.9019 | 0.755 | 0.3716 | 1.4526 | 0.755 | 0.7247 | 0.2051 | 0.1047 | | 0.0366 | 100.96 | 1262 | 0.8994 | 0.755 | 0.3694 | 1.4485 | 0.755 | 0.7247 | 0.1979 | 0.1041 | | 0.0366 | 102.0 | 1275 | 0.9023 | 0.755 | 0.3715 | 1.4465 | 0.755 | 0.7247 | 0.2140 | 0.1044 | | 0.0366 | 102.96 | 1287 | 0.9048 | 0.755 | 0.3720 | 1.4472 | 0.755 | 0.7247 | 0.2066 | 0.1045 | | 0.0366 | 104.0 | 1300 | 0.9060 | 0.755 | 0.3719 | 1.4565 | 0.755 | 0.7247 | 0.2003 | 0.1044 | | 0.0366 | 104.96 | 1312 | 0.9105 | 0.755 | 0.3735 | 1.4625 | 0.755 | 0.7247 | 0.2094 | 0.1053 | | 0.0366 | 106.0 | 1325 | 0.9099 | 0.76 | 0.3738 | 1.4463 | 0.76 | 0.7273 | 0.2050 | 0.1044 | | 0.0366 | 106.96 | 1337 | 0.9111 | 0.755 | 0.3751 | 1.4486 | 0.755 | 0.7247 | 0.2173 | 0.1041 | | 0.0366 | 108.0 | 1350 | 0.9149 | 0.755 | 0.3745 | 1.5081 | 0.755 | 0.7247 | 0.2062 | 0.1052 | | 0.0366 | 108.96 | 1362 | 0.9146 | 0.755 | 0.3744 | 1.4513 | 0.755 | 0.7247 | 0.2073 | 0.1047 | | 0.0366 | 110.0 | 1375 | 0.9157 | 0.755 | 0.3764 | 1.4486 | 0.755 | 0.7247 | 0.2094 | 0.1046 | | 0.0366 | 110.96 | 1387 | 0.9201 | 0.755 | 0.3767 | 1.4529 | 0.755 | 0.7247 | 0.2093 | 0.1051 | | 0.0366 | 112.0 | 1400 | 0.9190 | 0.755 | 0.3759 | 1.4533 | 0.755 | 0.7247 | 0.2020 | 0.1049 | | 0.0366 | 112.96 | 1412 | 0.9230 | 0.755 | 0.3782 | 1.4529 | 0.755 | 0.7247 | 0.2054 | 0.1050 | | 0.0366 | 114.0 | 1425 | 0.9234 | 0.755 | 0.3778 | 1.4505 | 0.755 | 0.7247 | 0.2009 | 0.1049 | | 0.0366 | 114.96 | 1437 | 0.9238 | 0.755 | 0.3780 | 1.4469 | 0.755 | 0.7247 | 0.1974 | 0.1051 | | 0.0366 | 116.0 | 1450 | 0.9264 | 0.755 | 0.3786 | 1.4538 | 0.755 | 0.7247 | 0.2016 | 0.1051 | | 0.0366 | 116.96 | 1462 | 0.9275 | 0.755 | 0.3787 | 1.4553 | 0.755 | 0.7247 | 0.2012 | 0.1054 | | 0.0366 | 118.0 | 1475 | 0.9306 | 0.755 | 0.3798 | 1.4595 | 0.755 | 0.7247 | 0.2139 | 0.1057 | | 0.0366 | 118.96 | 1487 | 0.9291 | 0.76 | 0.3789 | 1.4529 | 0.76 | 0.7273 | 0.2014 | 0.1054 | | 0.0214 | 120.0 | 1500 | 0.9318 | 0.755 | 0.3804 | 1.4520 | 0.755 | 0.7247 | 0.2017 | 0.1050 | | 0.0214 | 120.96 | 1512 | 0.9325 | 0.755 | 0.3796 | 1.4535 | 0.755 | 0.7247 | 0.2087 | 0.1053 | | 0.0214 | 122.0 | 1525 | 0.9339 | 0.755 | 0.3804 | 1.4534 | 0.755 | 0.7247 | 0.2068 | 0.1056 | | 0.0214 | 122.96 | 1537 | 0.9342 | 0.755 | 0.3807 | 1.4519 | 0.755 | 0.7247 | 0.1986 | 0.1052 | | 0.0214 | 124.0 | 1550 | 0.9357 | 0.755 | 0.3808 | 1.4524 | 0.755 | 0.7247 | 0.2058 | 0.1054 | | 0.0214 | 124.96 | 1562 | 0.9360 | 0.755 | 0.3808 | 1.4514 | 0.755 | 0.7247 | 0.2061 | 0.1052 | | 0.0214 | 126.0 | 1575 | 0.9409 | 0.755 | 0.3828 | 1.4557 | 0.755 | 0.7247 | 0.2044 | 0.1058 | | 0.0214 | 126.96 | 1587 | 0.9390 | 0.755 | 0.3819 | 1.4523 | 0.755 | 0.7247 | 0.2062 | 0.1052 | | 0.0214 | 128.0 | 1600 | 0.9425 | 0.755 | 0.3833 | 1.4559 | 0.755 | 0.7247 | 0.2049 | 0.1058 | | 0.0214 | 128.96 | 1612 | 0.9421 | 0.755 | 0.3824 | 1.4534 | 0.755 | 0.7247 | 0.1978 | 0.1055 | | 0.0214 | 130.0 | 1625 | 0.9433 | 0.755 | 0.3831 | 1.4530 | 0.755 | 0.7247 | 0.2069 | 0.1056 | | 0.0214 | 130.96 | 1637 | 0.9463 | 0.755 | 0.3842 | 1.4535 | 0.755 | 0.7247 | 0.2127 | 0.1058 | | 0.0214 | 132.0 | 1650 | 0.9462 | 0.755 | 0.3835 | 1.4546 | 0.755 | 0.7247 | 0.1975 | 0.1057 | | 0.0214 | 132.96 | 1662 | 0.9479 | 0.755 | 0.3842 | 1.4562 | 0.755 | 0.7247 | 0.2012 | 0.1059 | | 0.0214 | 134.0 | 1675 | 0.9493 | 0.755 | 0.3852 | 1.4547 | 0.755 | 0.7247 | 0.2161 | 0.1057 | | 0.0214 | 134.96 | 1687 | 0.9484 | 0.755 | 0.3842 | 1.4563 | 0.755 | 0.7247 | 0.2046 | 0.1055 | | 0.0214 | 136.0 | 1700 | 0.9500 | 0.755 | 0.3849 | 1.4528 | 0.755 | 0.7247 | 0.2081 | 0.1058 | | 0.0214 | 136.96 | 1712 | 0.9510 | 0.755 | 0.3854 | 1.4510 | 0.755 | 0.7247 | 0.1976 | 0.1053 | | 0.0214 | 138.0 | 1725 | 0.9519 | 0.755 | 0.3855 | 1.4516 | 0.755 | 0.7247 | 0.2090 | 0.1056 | | 0.0214 | 138.96 | 1737 | 0.9535 | 0.755 | 0.3857 | 1.4537 | 0.755 | 0.7247 | 0.2091 | 0.1057 | | 0.0214 | 140.0 | 1750 | 0.9546 | 0.755 | 0.3862 | 1.4557 | 0.755 | 0.7247 | 0.2020 | 0.1057 | | 0.0214 | 140.96 | 1762 | 0.9558 | 0.755 | 0.3867 | 1.4530 | 0.755 | 0.7247 | 0.2018 | 0.1059 | | 0.0214 | 142.0 | 1775 | 0.9567 | 0.755 | 0.3870 | 1.4522 | 0.755 | 0.7247 | 0.2059 | 0.1055 | | 0.0214 | 142.96 | 1787 | 0.9589 | 0.755 | 0.3876 | 1.4555 | 0.755 | 0.7247 | 0.2063 | 0.1060 | | 0.0214 | 144.0 | 1800 | 0.9584 | 0.755 | 0.3872 | 1.4568 | 0.755 | 0.7245 | 0.2162 | 0.1056 | | 0.0214 | 144.96 | 1812 | 0.9613 | 0.755 | 0.3881 | 1.4594 | 0.755 | 0.7247 | 0.2085 | 0.1063 | | 0.0214 | 146.0 | 1825 | 0.9604 | 0.755 | 0.3873 | 1.5132 | 0.755 | 0.7247 | 0.2047 | 0.1056 | | 0.0214 | 146.96 | 1837 | 0.9627 | 0.755 | 0.3887 | 1.4573 | 0.755 | 0.7247 | 0.2107 | 0.1059 | | 0.0214 | 148.0 | 1850 | 0.9643 | 0.755 | 0.3890 | 1.4570 | 0.755 | 0.7247 | 0.2041 | 0.1065 | | 0.0214 | 148.96 | 1862 | 0.9633 | 0.755 | 0.3886 | 1.4526 | 0.755 | 0.7247 | 0.2085 | 0.1055 | | 0.0214 | 150.0 | 1875 | 0.9637 | 0.755 | 0.3887 | 1.4551 | 0.755 | 0.7245 | 0.2096 | 0.1054 | | 0.0214 | 150.96 | 1887 | 0.9645 | 0.755 | 0.3886 | 1.4534 | 0.755 | 0.7247 | 0.2100 | 0.1058 | | 0.0214 | 152.0 | 1900 | 0.9661 | 0.755 | 0.3891 | 1.4550 | 0.755 | 0.7247 | 0.2072 | 0.1060 | | 0.0214 | 152.96 | 1912 | 0.9665 | 0.755 | 0.3894 | 1.4543 | 0.755 | 0.7247 | 0.2092 | 0.1057 | | 0.0214 | 154.0 | 1925 | 0.9667 | 0.755 | 0.3894 | 1.4570 | 0.755 | 0.7245 | 0.2019 | 0.1056 | | 0.0214 | 154.96 | 1937 | 0.9681 | 0.755 | 0.3900 | 1.4537 | 0.755 | 0.7245 | 0.2098 | 0.1059 | | 0.0214 | 156.0 | 1950 | 0.9689 | 0.755 | 0.3903 | 1.4533 | 0.755 | 0.7245 | 0.2022 | 0.1056 | | 0.0214 | 156.96 | 1962 | 0.9689 | 0.755 | 0.3898 | 1.4553 | 0.755 | 0.7247 | 0.2074 | 0.1058 | | 0.0214 | 158.0 | 1975 | 0.9705 | 0.755 | 0.3901 | 1.4566 | 0.755 | 0.7247 | 0.2072 | 0.1062 | | 0.0214 | 158.96 | 1987 | 0.9721 | 0.755 | 0.3911 | 1.4570 | 0.755 | 0.7245 | 0.2055 | 0.1062 | | 0.0155 | 160.0 | 2000 | 0.9712 | 0.755 | 0.3904 | 1.4551 | 0.755 | 0.7245 | 0.2070 | 0.1060 | | 0.0155 | 160.96 | 2012 | 0.9721 | 0.755 | 0.3906 | 1.4563 | 0.755 | 0.7245 | 0.2108 | 0.1059 | | 0.0155 | 162.0 | 2025 | 0.9734 | 0.755 | 0.3914 | 1.4550 | 0.755 | 0.7245 | 0.2071 | 0.1057 | | 0.0155 | 162.96 | 2037 | 0.9740 | 0.755 | 0.3913 | 1.4565 | 0.755 | 0.7245 | 0.2073 | 0.1062 | | 0.0155 | 164.0 | 2050 | 0.9744 | 0.755 | 0.3915 | 1.4563 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 164.96 | 2062 | 0.9752 | 0.755 | 0.3917 | 1.4558 | 0.755 | 0.7245 | 0.2102 | 0.1059 | | 0.0155 | 166.0 | 2075 | 0.9755 | 0.755 | 0.3916 | 1.4566 | 0.755 | 0.7245 | 0.2102 | 0.1062 | | 0.0155 | 166.96 | 2087 | 0.9755 | 0.755 | 0.3917 | 1.4542 | 0.755 | 0.7245 | 0.2100 | 0.1057 | | 0.0155 | 168.0 | 2100 | 0.9768 | 0.755 | 0.3921 | 1.4578 | 0.755 | 0.7245 | 0.2103 | 0.1059 | | 0.0155 | 168.96 | 2112 | 0.9767 | 0.755 | 0.3919 | 1.4575 | 0.755 | 0.7245 | 0.2106 | 0.1057 | | 0.0155 | 170.0 | 2125 | 0.9776 | 0.755 | 0.3922 | 1.4567 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 170.96 | 2137 | 0.9783 | 0.755 | 0.3925 | 1.4560 | 0.755 | 0.7245 | 0.2080 | 0.1062 | | 0.0155 | 172.0 | 2150 | 0.9793 | 0.755 | 0.3927 | 1.4578 | 0.755 | 0.7245 | 0.2102 | 0.1064 | | 0.0155 | 172.96 | 2162 | 0.9783 | 0.755 | 0.3923 | 1.4557 | 0.755 | 0.7245 | 0.2098 | 0.1058 | | 0.0155 | 174.0 | 2175 | 0.9790 | 0.755 | 0.3927 | 1.4550 | 0.755 | 0.7245 | 0.2087 | 0.1057 | | 0.0155 | 174.96 | 2187 | 0.9801 | 0.755 | 0.3929 | 1.4577 | 0.755 | 0.7245 | 0.2100 | 0.1059 | | 0.0155 | 176.0 | 2200 | 0.9800 | 0.755 | 0.3928 | 1.4566 | 0.755 | 0.7245 | 0.2081 | 0.1058 | | 0.0155 | 176.96 | 2212 | 0.9809 | 0.755 | 0.3932 | 1.4568 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 178.0 | 2225 | 0.9808 | 0.755 | 0.3930 | 1.4571 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 178.96 | 2237 | 0.9808 | 0.755 | 0.3930 | 1.4566 | 0.755 | 0.7245 | 0.2082 | 0.1057 | | 0.0155 | 180.0 | 2250 | 0.9813 | 0.755 | 0.3931 | 1.4567 | 0.755 | 0.7245 | 0.2082 | 0.1058 | | 0.0155 | 180.96 | 2262 | 0.9817 | 0.755 | 0.3933 | 1.4571 | 0.755 | 0.7245 | 0.2032 | 0.1059 | | 0.0155 | 182.0 | 2275 | 0.9819 | 0.755 | 0.3933 | 1.4570 | 0.755 | 0.7245 | 0.2103 | 0.1058 | | 0.0155 | 182.96 | 2287 | 0.9822 | 0.755 | 0.3934 | 1.4567 | 0.755 | 0.7245 | 0.2101 | 0.1058 | | 0.0155 | 184.0 | 2300 | 0.9824 | 0.755 | 0.3934 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 184.96 | 2312 | 0.9825 | 0.755 | 0.3935 | 1.4569 | 0.755 | 0.7245 | 0.2081 | 0.1060 | | 0.0155 | 186.0 | 2325 | 0.9825 | 0.755 | 0.3934 | 1.4574 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 186.96 | 2337 | 0.9829 | 0.755 | 0.3935 | 1.4571 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 188.0 | 2350 | 0.9830 | 0.755 | 0.3936 | 1.4569 | 0.755 | 0.7245 | 0.2081 | 0.1060 | | 0.0155 | 188.96 | 2362 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2082 | 0.1059 | | 0.0155 | 190.0 | 2375 | 0.9830 | 0.755 | 0.3935 | 1.4575 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 190.96 | 2387 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | | 0.0155 | 192.0 | 2400 | 0.9831 | 0.755 | 0.3936 | 1.4572 | 0.755 | 0.7245 | 0.2101 | 0.1059 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2

# vit-base_tobacco_bs_16_lr_2e-4_e_200_wr_0.01_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0644 - Accuracy: 0.86 - Brier Loss: 0.2705 - Nll: 1.3085 - F1 Micro: 0.8600 - F1 Macro: 0.8552 - Ece: 0.1378 - Aurc: 0.0461 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7869 | 0.78 | 0.3223 | 1.3413 | 0.78 | 0.7402 | 0.2269 | 0.0782 | | No log | 2.0 | 25 | 0.9889 | 0.715 | 0.4300 | 1.9648 | 0.715 | 0.6881 | 0.2669 | 0.1397 | | No log | 2.96 | 37 | 0.7053 | 0.82 | 0.2995 | 1.2578 | 0.82 | 0.8270 | 0.2253 | 0.0758 | | No log | 4.0 | 50 | 0.7535 | 0.78 | 0.3225 | 1.3427 | 0.78 | 0.7395 | 0.2159 | 0.0616 | | No log | 4.96 | 62 | 0.8538 | 0.775 | 0.3634 | 1.5684 | 0.775 | 0.7523 | 0.2181 | 0.1149 | | No log | 6.0 | 75 | 0.7825 | 0.77 | 0.3557 | 1.3406 | 0.7700 | 0.7663 | 0.2136 | 0.0625 | | No log | 6.96 | 87 | 1.0777 | 0.67 | 0.4896 | 1.5465 | 0.67 | 0.6728 | 0.2540 | 0.1106 | | No log | 8.0 | 100 | 1.1030 | 0.73 | 0.4453 | 2.5744 | 0.7300 | 0.6939 | 0.2294 | 0.1423 | | No log | 8.96 | 112 | 1.0215 | 0.725 | 0.4339 | 2.0485 | 0.7250 | 0.7012 | 0.2348 | 0.1278 | | No log | 10.0 | 125 | 0.7940 | 0.795 | 0.3378 | 1.3057 | 0.795 | 0.7911 | 0.1828 | 0.0750 | | No log | 10.96 | 137 | 0.7648 | 0.82 | 0.2963 | 1.3907 | 0.82 | 0.8022 | 0.1597 | 0.0744 | | No log | 12.0 | 150 | 1.0755 | 0.74 | 0.4383 | 2.1271 | 0.74 | 0.7182 | 0.2281 | 0.0847 | | No log | 12.96 | 162 | 1.0091 | 0.775 | 0.3856 | 1.7383 | 0.775 | 0.7339 | 0.1969 | 0.1029 | | No log | 14.0 | 175 | 1.0531 | 0.77 | 0.4027 | 1.5532 | 0.7700 | 0.7592 | 0.2152 | 0.0888 | | No log | 14.96 | 187 | 1.0221 | 0.77 | 0.4027 | 1.5199 | 0.7700 | 0.7259 | 0.2059 | 0.1031 | | No log | 16.0 | 200 | 1.1795 | 0.735 | 0.4435 | 2.0739 | 0.735 | 0.7063 | 0.2262 | 0.1305 | | No log | 16.96 | 212 | 1.1560 | 0.745 | 0.4379 | 2.0155 | 0.745 | 0.7240 | 0.2207 | 0.1273 | | No log | 18.0 | 225 | 1.0635 | 0.76 | 0.4159 | 1.5491 | 0.76 | 0.7508 | 0.2124 | 0.0879 | | No log | 18.96 | 237 | 1.2639 | 0.73 | 0.4649 | 1.9828 | 0.7300 | 0.7276 | 0.2298 | 0.1079 | | No log | 20.0 | 250 | 1.0598 | 0.78 | 0.3866 | 1.5139 | 0.78 | 0.7676 | 0.1885 | 0.0914 | | No log | 20.96 | 262 | 0.8900 | 0.81 | 0.3241 | 1.8355 | 0.81 | 0.7925 | 0.1691 | 0.0648 | | No log | 22.0 | 275 | 1.0617 | 0.79 | 0.3788 | 1.8951 | 0.79 | 0.7783 | 0.1893 | 0.0676 | | No log | 22.96 | 287 | 1.0362 | 0.785 | 0.3646 | 1.9399 | 0.785 | 0.7653 | 0.1914 | 0.0816 | | No log | 24.0 | 300 | 1.1701 | 0.775 | 0.4060 | 2.1593 | 0.775 | 0.7718 | 0.2114 | 0.0842 | | No log | 24.96 | 312 | 1.0841 | 0.79 | 0.3799 | 1.8773 | 0.79 | 0.7795 | 0.2016 | 0.0775 | | No log | 26.0 | 325 | 1.0064 | 0.785 | 0.3650 | 1.7371 | 0.785 | 0.7674 | 0.1915 | 0.0813 | | No log | 26.96 | 337 | 0.8886 | 0.825 | 0.3114 | 1.4858 | 0.825 | 0.8116 | 0.1609 | 0.0636 | | No log | 28.0 | 350 | 1.1174 | 0.8 | 0.3751 | 1.9584 | 0.8000 | 0.7869 | 0.1928 | 0.0930 | | No log | 28.96 | 362 | 1.0922 | 0.8 | 0.3672 | 1.8702 | 0.8000 | 0.7673 | 0.1954 | 0.0771 | | No log | 30.0 | 375 | 1.0281 | 0.805 | 0.3506 | 1.6105 | 0.805 | 0.7809 | 0.1773 | 0.0936 | | No log | 30.96 | 387 | 0.9041 | 0.82 | 0.3210 | 1.3323 | 0.82 | 0.8148 | 0.1627 | 0.0651 | | No log | 32.0 | 400 | 1.1018 | 0.79 | 0.3804 | 1.9928 | 0.79 | 0.7962 | 0.1859 | 0.0574 | | No log | 32.96 | 412 | 1.1973 | 0.765 | 0.4156 | 1.4304 | 0.765 | 0.7682 | 0.2147 | 0.0760 | | No log | 34.0 | 425 | 1.0216 | 0.805 | 0.3605 | 1.4476 | 0.805 | 0.7864 | 0.1830 | 0.0633 | | No log | 34.96 | 437 | 1.2356 | 0.755 | 0.4237 | 1.9897 | 0.755 | 0.7350 | 0.2214 | 0.0890 | | No log | 36.0 | 450 | 1.0881 | 0.8 | 0.3757 | 1.3848 | 0.8000 | 0.7810 | 0.1960 | 0.0703 | | No log | 36.96 | 462 | 1.1133 | 0.795 | 0.3687 | 2.0286 | 0.795 | 0.7707 | 0.1790 | 0.0652 | | No log | 38.0 | 475 | 1.1243 | 0.78 | 0.3839 | 1.5683 | 0.78 | 0.7699 | 0.1905 | 0.0704 | | No log | 38.96 | 487 | 1.1351 | 0.785 | 0.3983 | 1.4970 | 0.785 | 0.7647 | 0.1969 | 0.0666 | | 0.0934 | 40.0 | 500 | 1.2551 | 0.775 | 0.4089 | 2.0438 | 0.775 | 0.7688 | 0.2082 | 0.1007 | | 0.0934 | 40.96 | 512 | 1.1739 | 0.775 | 0.4003 | 1.3286 | 0.775 | 0.7654 | 0.2056 | 0.0819 | | 0.0934 | 42.0 | 525 | 1.0007 | 0.83 | 0.3207 | 1.2576 | 0.83 | 0.8345 | 0.1579 | 0.0677 | | 0.0934 | 42.96 | 537 | 1.0509 | 0.805 | 0.3580 | 1.2330 | 0.805 | 0.7933 | 0.1884 | 0.0716 | | 0.0934 | 44.0 | 550 | 1.0830 | 0.805 | 0.3537 | 1.7652 | 0.805 | 0.7871 | 0.1740 | 0.0688 | | 0.0934 | 44.96 | 562 | 0.8544 | 0.83 | 0.2957 | 1.4716 | 0.83 | 0.8039 | 0.1560 | 0.0532 | | 0.0934 | 46.0 | 575 | 1.0803 | 0.815 | 0.3549 | 1.5802 | 0.815 | 0.7951 | 0.1840 | 0.0691 | | 0.0934 | 46.96 | 587 | 0.9441 | 0.815 | 0.3318 | 1.2883 | 0.815 | 0.7924 | 0.1709 | 0.0514 | | 0.0934 | 48.0 | 600 | 0.9007 | 0.845 | 0.2765 | 1.3443 | 0.845 | 0.8353 | 0.1402 | 0.0539 | | 0.0934 | 48.96 | 612 | 0.9601 | 0.84 | 0.2952 | 1.4755 | 0.8400 | 0.8306 | 0.1499 | 0.0565 | | 0.0934 | 50.0 | 625 | 0.9801 | 0.84 | 0.2992 | 1.4646 | 0.8400 | 0.8306 | 0.1529 | 0.0559 | | 0.0934 | 50.96 | 637 | 0.9747 | 0.845 | 0.2950 | 1.4544 | 0.845 | 0.8338 | 0.1526 | 0.0546 | | 0.0934 | 52.0 | 650 | 0.9651 | 0.845 | 0.2895 | 1.4442 | 0.845 | 0.8338 | 0.1469 | 0.0537 | | 0.0934 | 52.96 | 662 | 0.9583 | 0.85 | 0.2848 | 1.4367 | 0.85 | 0.8370 | 0.1465 | 0.0525 | | 0.0934 | 54.0 | 675 | 0.9534 | 0.85 | 0.2805 | 1.4300 | 0.85 | 0.8370 | 0.1455 | 0.0514 | | 0.0934 | 54.96 | 687 | 0.9503 | 0.855 | 0.2776 | 1.4252 | 0.855 | 0.8425 | 0.1408 | 0.0510 | | 0.0934 | 56.0 | 700 | 0.9480 | 0.855 | 0.2754 | 1.4207 | 0.855 | 0.8425 | 0.1407 | 0.0506 | | 0.0934 | 56.96 | 712 | 0.9471 | 0.855 | 0.2739 | 1.4175 | 0.855 | 0.8425 | 0.1442 | 0.0504 | | 0.0934 | 58.0 | 725 | 0.9471 | 0.855 | 0.2729 | 1.4147 | 0.855 | 0.8442 | 0.1435 | 0.0501 | | 0.0934 | 58.96 | 737 | 0.9474 | 0.855 | 0.2720 | 1.4125 | 0.855 | 0.8442 | 0.1432 | 0.0497 | | 0.0934 | 60.0 | 750 | 0.9482 | 0.855 | 0.2713 | 1.4101 | 0.855 | 0.8442 | 0.1420 | 0.0497 | | 0.0934 | 60.96 | 762 | 0.9490 | 0.855 | 0.2708 | 1.4082 | 0.855 | 0.8442 | 0.1421 | 0.0493 | | 0.0934 | 62.0 | 775 | 0.9500 | 0.86 | 0.2703 | 1.4063 | 0.8600 | 0.8534 | 0.1411 | 0.0493 | | 0.0934 | 62.96 | 787 | 0.9512 | 0.86 | 0.2702 | 1.4046 | 0.8600 | 0.8534 | 0.1410 | 0.0492 | | 0.0934 | 64.0 | 800 | 0.9528 | 0.86 | 0.2699 | 1.4032 | 0.8600 | 0.8534 | 0.1408 | 0.0489 | | 0.0934 | 64.96 | 812 | 0.9541 | 0.86 | 0.2697 | 1.3472 | 0.8600 | 0.8534 | 0.1349 | 0.0487 | | 0.0934 | 66.0 | 825 | 0.9558 | 0.86 | 0.2696 | 1.3431 | 0.8600 | 0.8534 | 0.1408 | 0.0487 | | 0.0934 | 66.96 | 837 | 0.9574 | 0.86 | 0.2697 | 1.3403 | 0.8600 | 0.8534 | 0.1405 | 0.0486 | | 0.0934 | 68.0 | 850 | 0.9591 | 0.86 | 0.2698 | 1.3375 | 0.8600 | 0.8534 | 0.1402 | 0.0486 | | 0.0934 | 68.96 | 862 | 0.9605 | 0.86 | 0.2698 | 1.3355 | 0.8600 | 0.8552 | 0.1394 | 0.0486 | | 0.0934 | 70.0 | 875 | 0.9624 | 0.86 | 0.2698 | 1.3338 | 0.8600 | 0.8552 | 0.1394 | 0.0486 | | 0.0934 | 70.96 | 887 | 0.9638 | 0.86 | 0.2700 | 1.3322 | 0.8600 | 0.8552 | 0.1397 | 0.0485 | | 0.0934 | 72.0 | 900 | 0.9657 | 0.86 | 0.2701 | 1.3310 | 0.8600 | 0.8552 | 0.1397 | 0.0485 | | 0.0934 | 72.96 | 912 | 0.9673 | 0.86 | 0.2702 | 1.3299 | 0.8600 | 0.8552 | 0.1397 | 0.0484 | | 0.0934 | 74.0 | 925 | 0.9691 | 0.86 | 0.2703 | 1.3289 | 0.8600 | 0.8552 | 0.1397 | 0.0484 | | 0.0934 | 74.96 | 937 | 0.9708 | 0.86 | 0.2704 | 1.3280 | 0.8600 | 0.8552 | 0.1398 | 0.0485 | | 0.0934 | 76.0 | 950 | 0.9725 | 0.86 | 0.2706 | 1.3271 | 0.8600 | 0.8552 | 0.1398 | 0.0485 | | 0.0934 | 76.96 | 962 | 0.9740 | 0.86 | 0.2707 | 1.3263 | 0.8600 | 0.8552 | 0.1398 | 0.0485 | | 0.0934 | 78.0 | 975 | 0.9757 | 0.86 | 0.2707 | 1.3256 | 0.8600 | 0.8552 | 0.1383 | 0.0485 | | 0.0934 | 78.96 | 987 | 0.9772 | 0.86 | 0.2708 | 1.3248 | 0.8600 | 0.8552 | 0.1357 | 0.0484 | | 0.0038 | 80.0 | 1000 | 0.9789 | 0.86 | 0.2709 | 1.3243 | 0.8600 | 0.8552 | 0.1359 | 0.0485 | | 0.0038 | 80.96 | 1012 | 0.9806 | 0.86 | 0.2710 | 1.3238 | 0.8600 | 0.8552 | 0.1360 | 0.0484 | | 0.0038 | 82.0 | 1025 | 0.9820 | 0.86 | 0.2711 | 1.3232 | 0.8600 | 0.8552 | 0.1361 | 0.0482 | | 0.0038 | 82.96 | 1037 | 0.9837 | 0.86 | 0.2712 | 1.3227 | 0.8600 | 0.8552 | 0.1361 | 0.0481 | | 0.0038 | 84.0 | 1050 | 0.9853 | 0.86 | 0.2713 | 1.3222 | 0.8600 | 0.8552 | 0.1362 | 0.0480 | | 0.0038 | 84.96 | 1062 | 0.9867 | 0.86 | 0.2713 | 1.3216 | 0.8600 | 0.8552 | 0.1363 | 0.0481 | | 0.0038 | 86.0 | 1075 | 0.9883 | 0.86 | 0.2714 | 1.3212 | 0.8600 | 0.8552 | 0.1364 | 0.0479 | | 0.0038 | 86.96 | 1087 | 0.9896 | 0.86 | 0.2714 | 1.3208 | 0.8600 | 0.8552 | 0.1365 | 0.0477 | | 0.0038 | 88.0 | 1100 | 0.9911 | 0.86 | 0.2715 | 1.3203 | 0.8600 | 0.8552 | 0.1366 | 0.0478 | | 0.0038 | 88.96 | 1112 | 0.9925 | 0.86 | 0.2715 | 1.3200 | 0.8600 | 0.8552 | 0.1369 | 0.0478 | | 0.0038 | 90.0 | 1125 | 0.9940 | 0.86 | 0.2715 | 1.3196 | 0.8600 | 0.8552 | 0.1369 | 0.0477 | | 0.0038 | 90.96 | 1137 | 0.9954 | 0.86 | 0.2715 | 1.3194 | 0.8600 | 0.8552 | 0.1369 | 0.0476 | | 0.0038 | 92.0 | 1150 | 0.9968 | 0.86 | 0.2716 | 1.3190 | 0.8600 | 0.8552 | 0.1368 | 0.0476 | | 0.0038 | 92.96 | 1162 | 0.9983 | 0.86 | 0.2716 | 1.3187 | 0.8600 | 0.8552 | 0.1368 | 0.0476 | | 0.0038 | 94.0 | 1175 | 0.9996 | 0.86 | 0.2716 | 1.3184 | 0.8600 | 0.8552 | 0.1394 | 0.0476 | | 0.0038 | 94.96 | 1187 | 1.0009 | 0.86 | 0.2716 | 1.3182 | 0.8600 | 0.8552 | 0.1393 | 0.0475 | | 0.0038 | 96.0 | 1200 | 1.0023 | 0.86 | 0.2717 | 1.3179 | 0.8600 | 0.8552 | 0.1392 | 0.0475 | | 0.0038 | 96.96 | 1212 | 1.0035 | 0.86 | 0.2717 | 1.3176 | 0.8600 | 0.8552 | 0.1391 | 0.0475 | | 0.0038 | 98.0 | 1225 | 1.0049 | 0.86 | 0.2717 | 1.3175 | 0.8600 | 0.8552 | 0.1391 | 0.0474 | | 0.0038 | 98.96 | 1237 | 1.0062 | 0.86 | 0.2717 | 1.3172 | 0.8600 | 0.8552 | 0.1391 | 0.0475 | | 0.0038 | 100.0 | 1250 | 1.0075 | 0.86 | 0.2717 | 1.3169 | 0.8600 | 0.8552 | 0.1367 | 0.0475 | | 0.0038 | 100.96 | 1262 | 1.0087 | 0.86 | 0.2717 | 1.3167 | 0.8600 | 0.8552 | 0.1368 | 0.0475 | | 0.0038 | 102.0 | 1275 | 1.0099 | 0.86 | 0.2717 | 1.3164 | 0.8600 | 0.8552 | 0.1375 | 0.0474 | | 0.0038 | 102.96 | 1287 | 1.0111 | 0.86 | 0.2717 | 1.3162 | 0.8600 | 0.8552 | 0.1376 | 0.0473 | | 0.0038 | 104.0 | 1300 | 1.0122 | 0.86 | 0.2717 | 1.3159 | 0.8600 | 0.8552 | 0.1378 | 0.0471 | | 0.0038 | 104.96 | 1312 | 1.0134 | 0.86 | 0.2716 | 1.3158 | 0.8600 | 0.8552 | 0.1378 | 0.0473 | | 0.0038 | 106.0 | 1325 | 1.0146 | 0.86 | 0.2717 | 1.3155 | 0.8600 | 0.8552 | 0.1379 | 0.0472 | | 0.0038 | 106.96 | 1337 | 1.0158 | 0.86 | 0.2717 | 1.3153 | 0.8600 | 0.8552 | 0.1379 | 0.0471 | | 0.0038 | 108.0 | 1350 | 1.0169 | 0.86 | 0.2716 | 1.3151 | 0.8600 | 0.8552 | 0.1380 | 0.0471 | | 0.0038 | 108.96 | 1362 | 1.0180 | 0.86 | 0.2716 | 1.3149 | 0.8600 | 0.8552 | 0.1381 | 0.0471 | | 0.0038 | 110.0 | 1375 | 1.0191 | 0.86 | 0.2716 | 1.3146 | 0.8600 | 0.8552 | 0.1381 | 0.0471 | | 0.0038 | 110.96 | 1387 | 1.0201 | 0.86 | 0.2716 | 1.3144 | 0.8600 | 0.8552 | 0.1382 | 0.0471 | | 0.0038 | 112.0 | 1400 | 1.0211 | 0.86 | 0.2716 | 1.3142 | 0.8600 | 0.8552 | 0.1382 | 0.0470 | | 0.0038 | 112.96 | 1412 | 1.0222 | 0.86 | 0.2716 | 1.3141 | 0.8600 | 0.8552 | 0.1382 | 0.0471 | | 0.0038 | 114.0 | 1425 | 1.0233 | 0.86 | 0.2715 | 1.3139 | 0.8600 | 0.8552 | 0.1383 | 0.0470 | | 0.0038 | 114.96 | 1437 | 1.0242 | 0.86 | 0.2715 | 1.3138 | 0.8600 | 0.8552 | 0.1383 | 0.0470 | | 0.0038 | 116.0 | 1450 | 1.0253 | 0.86 | 0.2715 | 1.3136 | 0.8600 | 0.8552 | 0.1383 | 0.0469 | | 0.0038 | 116.96 | 1462 | 1.0263 | 0.86 | 0.2715 | 1.3134 | 0.8600 | 0.8552 | 0.1383 | 0.0470 | | 0.0038 | 118.0 | 1475 | 1.0273 | 0.86 | 0.2715 | 1.3133 | 0.8600 | 0.8552 | 0.1384 | 0.0470 | | 0.0038 | 118.96 | 1487 | 1.0282 | 0.86 | 0.2714 | 1.3131 | 0.8600 | 0.8552 | 0.1384 | 0.0468 | | 0.0006 | 120.0 | 1500 | 1.0292 | 0.86 | 0.2714 | 1.3130 | 0.8600 | 0.8552 | 0.1385 | 0.0468 | | 0.0006 | 120.96 | 1512 | 1.0301 | 0.86 | 0.2714 | 1.3128 | 0.8600 | 0.8552 | 0.1385 | 0.0468 | | 0.0006 | 122.0 | 1525 | 1.0311 | 0.86 | 0.2714 | 1.3127 | 0.8600 | 0.8552 | 0.1386 | 0.0467 | | 0.0006 | 122.96 | 1537 | 1.0319 | 0.86 | 0.2714 | 1.3126 | 0.8600 | 0.8552 | 0.1386 | 0.0467 | | 0.0006 | 124.0 | 1550 | 1.0329 | 0.86 | 0.2714 | 1.3124 | 0.8600 | 0.8552 | 0.1387 | 0.0467 | | 0.0006 | 124.96 | 1562 | 1.0337 | 0.86 | 0.2713 | 1.3123 | 0.8600 | 0.8552 | 0.1393 | 0.0467 | | 0.0006 | 126.0 | 1575 | 1.0346 | 0.86 | 0.2713 | 1.3122 | 0.8600 | 0.8552 | 0.1374 | 0.0466 | | 0.0006 | 126.96 | 1587 | 1.0354 | 0.86 | 0.2713 | 1.3120 | 0.8600 | 0.8552 | 0.1375 | 0.0466 | | 0.0006 | 128.0 | 1600 | 1.0363 | 0.86 | 0.2713 | 1.3119 | 0.8600 | 0.8552 | 0.1375 | 0.0467 | | 0.0006 | 128.96 | 1612 | 1.0372 | 0.86 | 0.2713 | 1.3118 | 0.8600 | 0.8552 | 0.1375 | 0.0466 | | 0.0006 | 130.0 | 1625 | 1.0380 | 0.86 | 0.2712 | 1.3117 | 0.8600 | 0.8552 | 0.1375 | 0.0466 | | 0.0006 | 130.96 | 1637 | 1.0388 | 0.86 | 0.2712 | 1.3116 | 0.8600 | 0.8552 | 0.1375 | 0.0467 | | 0.0006 | 132.0 | 1650 | 1.0396 | 0.86 | 0.2712 | 1.3115 | 0.8600 | 0.8552 | 0.1375 | 0.0465 | | 0.0006 | 132.96 | 1662 | 1.0403 | 0.86 | 0.2712 | 1.3113 | 0.8600 | 0.8552 | 0.1375 | 0.0466 | | 0.0006 | 134.0 | 1675 | 1.0411 | 0.86 | 0.2712 | 1.3113 | 0.8600 | 0.8552 | 0.1376 | 0.0466 | | 0.0006 | 134.96 | 1687 | 1.0419 | 0.86 | 0.2711 | 1.3112 | 0.8600 | 0.8552 | 0.1376 | 0.0466 | | 0.0006 | 136.0 | 1700 | 1.0426 | 0.86 | 0.2711 | 1.3111 | 0.8600 | 0.8552 | 0.1376 | 0.0465 | | 0.0006 | 136.96 | 1712 | 1.0433 | 0.86 | 0.2711 | 1.3110 | 0.8600 | 0.8552 | 0.1376 | 0.0465 | | 0.0006 | 138.0 | 1725 | 1.0441 | 0.86 | 0.2711 | 1.3109 | 0.8600 | 0.8552 | 0.1376 | 0.0465 | | 0.0006 | 138.96 | 1737 | 1.0448 | 0.86 | 0.2711 | 1.3108 | 0.8600 | 0.8552 | 0.1376 | 0.0465 | | 0.0006 | 140.0 | 1750 | 1.0455 | 0.86 | 0.2710 | 1.3107 | 0.8600 | 0.8552 | 0.1377 | 0.0465 | | 0.0006 | 140.96 | 1762 | 1.0461 | 0.86 | 0.2710 | 1.3106 | 0.8600 | 0.8552 | 0.1377 | 0.0465 | | 0.0006 | 142.0 | 1775 | 1.0468 | 0.86 | 0.2710 | 1.3106 | 0.8600 | 0.8552 | 0.1377 | 0.0465 | | 0.0006 | 142.96 | 1787 | 1.0474 | 0.86 | 0.2710 | 1.3105 | 0.8600 | 0.8552 | 0.1377 | 0.0465 | | 0.0006 | 144.0 | 1800 | 1.0481 | 0.86 | 0.2710 | 1.3104 | 0.8600 | 0.8552 | 0.1377 | 0.0465 | | 0.0006 | 144.96 | 1812 | 1.0487 | 0.86 | 0.2710 | 1.3103 | 0.8600 | 0.8552 | 0.1378 | 0.0465 | | 0.0006 | 146.0 | 1825 | 1.0494 | 0.86 | 0.2709 | 1.3102 | 0.8600 | 0.8552 | 0.1378 | 0.0465 | | 0.0006 | 146.96 | 1837 | 1.0500 | 0.86 | 0.2709 | 1.3102 | 0.8600 | 0.8552 | 0.1378 | 0.0465 | | 0.0006 | 148.0 | 1850 | 1.0506 | 0.86 | 0.2709 | 1.3101 | 0.8600 | 0.8552 | 0.1378 | 0.0465 | | 0.0006 | 148.96 | 1862 | 1.0511 | 0.86 | 0.2709 | 1.3100 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 150.0 | 1875 | 1.0517 | 0.86 | 0.2709 | 1.3099 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 150.96 | 1887 | 1.0523 | 0.86 | 0.2709 | 1.3099 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 152.0 | 1900 | 1.0529 | 0.86 | 0.2708 | 1.3098 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 152.96 | 1912 | 1.0534 | 0.86 | 0.2708 | 1.3097 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 154.0 | 1925 | 1.0539 | 0.86 | 0.2708 | 1.3096 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 154.96 | 1937 | 1.0544 | 0.86 | 0.2708 | 1.3096 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 156.0 | 1950 | 1.0550 | 0.86 | 0.2708 | 1.3095 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 156.96 | 1962 | 1.0554 | 0.86 | 0.2708 | 1.3094 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 158.0 | 1975 | 1.0559 | 0.86 | 0.2707 | 1.3094 | 0.8600 | 0.8552 | 0.1378 | 0.0464 | | 0.0006 | 158.96 | 1987 | 1.0563 | 0.86 | 0.2707 | 1.3093 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 160.0 | 2000 | 1.0568 | 0.86 | 0.2707 | 1.3093 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 160.96 | 2012 | 1.0573 | 0.86 | 0.2707 | 1.3092 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 162.0 | 2025 | 1.0577 | 0.86 | 0.2707 | 1.3092 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 162.96 | 2037 | 1.0581 | 0.86 | 0.2707 | 1.3091 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 164.0 | 2050 | 1.0585 | 0.86 | 0.2707 | 1.3091 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 164.96 | 2062 | 1.0589 | 0.86 | 0.2707 | 1.3090 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 166.0 | 2075 | 1.0593 | 0.86 | 0.2707 | 1.3090 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 166.96 | 2087 | 1.0597 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 168.0 | 2100 | 1.0600 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 168.96 | 2112 | 1.0603 | 0.86 | 0.2706 | 1.3089 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 170.0 | 2125 | 1.0607 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 170.96 | 2137 | 1.0610 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 172.0 | 2150 | 1.0613 | 0.86 | 0.2706 | 1.3088 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 172.96 | 2162 | 1.0616 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 174.0 | 2175 | 1.0619 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 174.96 | 2187 | 1.0621 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 176.0 | 2200 | 1.0624 | 0.86 | 0.2706 | 1.3087 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 176.96 | 2212 | 1.0626 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 178.0 | 2225 | 1.0629 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 178.96 | 2237 | 1.0630 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 180.0 | 2250 | 1.0632 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 180.96 | 2262 | 1.0634 | 0.86 | 0.2706 | 1.3086 | 0.8600 | 0.8552 | 0.1378 | 0.0463 | | 0.0004 | 182.0 | 2275 | 1.0636 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 182.96 | 2287 | 1.0637 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 184.0 | 2300 | 1.0639 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 184.96 | 2312 | 1.0640 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 186.0 | 2325 | 1.0641 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 186.96 | 2337 | 1.0642 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0462 | | 0.0004 | 188.0 | 2350 | 1.0643 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 | | 0.0004 | 188.96 | 2362 | 1.0643 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 | | 0.0004 | 190.0 | 2375 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 | | 0.0004 | 190.96 | 2387 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 | | 0.0004 | 192.0 | 2400 | 1.0644 | 0.86 | 0.2705 | 1.3085 | 0.8600 | 0.8552 | 0.1378 | 0.0461 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.9

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6228 - Accuracy: 0.5375 - Brier Loss: 0.6009 - Nll: 2.3362 - F1 Micro: 0.5375 - F1 Macro: 0.5332 - Ece: 0.1886 - Aurc: 0.2188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.0859 | 0.0425 | 1.0718 | 7.3724 | 0.0425 | 0.0340 | 0.2811 | 0.9554 | | No log | 2.0 | 14 | 3.1575 | 0.0925 | 0.9517 | 5.8523 | 0.0925 | 0.0709 | 0.1660 | 0.8903 | | No log | 3.0 | 21 | 2.8291 | 0.2325 | 0.8866 | 5.4080 | 0.2325 | 0.1824 | 0.1601 | 0.6363 | | No log | 4.0 | 28 | 2.4189 | 0.2875 | 0.7987 | 3.5373 | 0.2875 | 0.2714 | 0.1822 | 0.4858 | | No log | 5.0 | 35 | 2.1338 | 0.3775 | 0.7203 | 2.9804 | 0.3775 | 0.3569 | 0.1731 | 0.3581 | | No log | 6.0 | 42 | 2.0168 | 0.44 | 0.6910 | 2.9903 | 0.44 | 0.4255 | 0.1854 | 0.3060 | | No log | 7.0 | 49 | 2.0458 | 0.4575 | 0.6988 | 3.1988 | 0.4575 | 0.4286 | 0.2078 | 0.3000 | | No log | 8.0 | 56 | 1.9688 | 0.4575 | 0.6928 | 2.8691 | 0.4575 | 0.4542 | 0.2122 | 0.2986 | | No log | 9.0 | 63 | 2.1143 | 0.4625 | 0.7081 | 2.8507 | 0.4625 | 0.4496 | 0.2232 | 0.2940 | | No log | 10.0 | 70 | 2.1352 | 0.455 | 0.7242 | 2.9232 | 0.455 | 0.4317 | 0.2581 | 0.2923 | | No log | 11.0 | 77 | 2.2760 | 0.44 | 0.7545 | 2.9083 | 0.44 | 0.4144 | 0.2835 | 0.3144 | | No log | 12.0 | 84 | 2.1320 | 0.4675 | 0.7389 | 2.6277 | 0.4675 | 0.4607 | 0.2715 | 0.3142 | | No log | 13.0 | 91 | 2.0889 | 0.4825 | 0.7170 | 2.6786 | 0.4825 | 0.4755 | 0.2522 | 0.2933 | | No log | 14.0 | 98 | 2.0173 | 0.475 | 0.7020 | 2.7765 | 0.4750 | 0.4555 | 0.2593 | 0.2791 | | No log | 15.0 | 105 | 2.1265 | 0.4875 | 0.7124 | 3.0127 | 0.4875 | 0.4753 | 0.2458 | 0.3069 | | No log | 16.0 | 112 | 2.1517 | 0.4825 | 0.7184 | 2.7981 | 0.4825 | 0.4719 | 0.2505 | 0.2964 | | No log | 17.0 | 119 | 1.9485 | 0.49 | 0.6828 | 2.6670 | 0.49 | 0.4773 | 0.2430 | 0.2649 | | No log | 18.0 | 126 | 1.9434 | 0.5 | 0.6802 | 2.6131 | 0.5 | 0.4941 | 0.2267 | 0.2741 | | No log | 19.0 | 133 | 1.9055 | 0.4925 | 0.6741 | 2.7035 | 0.4925 | 0.4953 | 0.2201 | 0.2700 | | No log | 20.0 | 140 | 1.8119 | 0.5 | 0.6520 | 2.4537 | 0.5 | 0.4937 | 0.2016 | 0.2449 | | No log | 21.0 | 147 | 1.8420 | 0.49 | 0.6593 | 2.6244 | 0.49 | 0.4798 | 0.2100 | 0.2668 | | No log | 22.0 | 154 | 1.9282 | 0.5 | 0.6739 | 2.6650 | 0.5 | 0.4837 | 0.2166 | 0.2652 | | No log | 23.0 | 161 | 1.8139 | 0.5125 | 0.6543 | 2.4043 | 0.5125 | 0.5009 | 0.2275 | 0.2604 | | No log | 24.0 | 168 | 1.6997 | 0.5325 | 0.6124 | 2.3950 | 0.5325 | 0.5271 | 0.1873 | 0.2248 | | No log | 25.0 | 175 | 1.8370 | 0.5025 | 0.6526 | 2.4967 | 0.5025 | 0.4926 | 0.2249 | 0.2513 | | No log | 26.0 | 182 | 1.7508 | 0.5025 | 0.6388 | 2.4321 | 0.5025 | 0.4947 | 0.2135 | 0.2416 | | No log | 27.0 | 189 | 1.7354 | 0.52 | 0.6368 | 2.3706 | 0.52 | 0.5149 | 0.2101 | 0.2440 | | No log | 28.0 | 196 | 1.7809 | 0.52 | 0.6421 | 2.4895 | 0.52 | 0.5076 | 0.2033 | 0.2446 | | No log | 29.0 | 203 | 1.6770 | 0.55 | 0.6046 | 2.3913 | 0.55 | 0.5409 | 0.1849 | 0.2189 | | No log | 30.0 | 210 | 1.6794 | 0.5325 | 0.6127 | 2.5677 | 0.5325 | 0.5263 | 0.2095 | 0.2247 | | No log | 31.0 | 217 | 1.7352 | 0.525 | 0.6325 | 2.5068 | 0.525 | 0.5062 | 0.1865 | 0.2320 | | No log | 32.0 | 224 | 1.7396 | 0.5225 | 0.6379 | 2.4798 | 0.5225 | 0.5169 | 0.1829 | 0.2402 | | No log | 33.0 | 231 | 1.7013 | 0.52 | 0.6188 | 2.5032 | 0.52 | 0.5029 | 0.1986 | 0.2233 | | No log | 34.0 | 238 | 1.7121 | 0.5075 | 0.6313 | 2.5680 | 0.5075 | 0.5074 | 0.2249 | 0.2482 | | No log | 35.0 | 245 | 1.7053 | 0.5175 | 0.6368 | 2.3967 | 0.5175 | 0.5064 | 0.2088 | 0.2389 | | No log | 36.0 | 252 | 1.6616 | 0.55 | 0.6058 | 2.5726 | 0.55 | 0.5357 | 0.1762 | 0.2174 | | No log | 37.0 | 259 | 1.7302 | 0.5075 | 0.6350 | 2.3745 | 0.5075 | 0.4993 | 0.1939 | 0.2481 | | No log | 38.0 | 266 | 1.6741 | 0.5225 | 0.6152 | 2.5862 | 0.5225 | 0.5211 | 0.2042 | 0.2234 | | No log | 39.0 | 273 | 1.6960 | 0.515 | 0.6216 | 2.4210 | 0.515 | 0.5049 | 0.1776 | 0.2270 | | No log | 40.0 | 280 | 1.6512 | 0.54 | 0.6075 | 2.4495 | 0.54 | 0.5377 | 0.1848 | 0.2217 | | No log | 41.0 | 287 | 1.6477 | 0.52 | 0.6086 | 2.5354 | 0.52 | 0.5177 | 0.1976 | 0.2219 | | No log | 42.0 | 294 | 1.6680 | 0.52 | 0.6198 | 2.2815 | 0.52 | 0.5215 | 0.1951 | 0.2314 | | No log | 43.0 | 301 | 1.6153 | 0.545 | 0.5935 | 2.3952 | 0.545 | 0.5376 | 0.1721 | 0.2088 | | No log | 44.0 | 308 | 1.6347 | 0.5375 | 0.6023 | 2.3985 | 0.5375 | 0.5340 | 0.1778 | 0.2178 | | No log | 45.0 | 315 | 1.6132 | 0.5375 | 0.6001 | 2.3149 | 0.5375 | 0.5375 | 0.1902 | 0.2163 | | No log | 46.0 | 322 | 1.6366 | 0.5275 | 0.6102 | 2.3820 | 0.5275 | 0.5210 | 0.2057 | 0.2239 | | No log | 47.0 | 329 | 1.6118 | 0.55 | 0.5963 | 2.3834 | 0.55 | 0.5463 | 0.1748 | 0.2137 | | No log | 48.0 | 336 | 1.6208 | 0.535 | 0.6038 | 2.2855 | 0.535 | 0.5340 | 0.1791 | 0.2201 | | No log | 49.0 | 343 | 1.6006 | 0.545 | 0.5960 | 2.3253 | 0.545 | 0.5440 | 0.2007 | 0.2158 | | No log | 50.0 | 350 | 1.6184 | 0.5425 | 0.6005 | 2.3181 | 0.5425 | 0.5432 | 0.1846 | 0.2176 | | No log | 51.0 | 357 | 1.6215 | 0.5425 | 0.6022 | 2.3219 | 0.5425 | 0.5371 | 0.1776 | 0.2163 | | No log | 52.0 | 364 | 1.6068 | 0.54 | 0.5978 | 2.2928 | 0.54 | 0.5383 | 0.1865 | 0.2164 | | No log | 53.0 | 371 | 1.6128 | 0.535 | 0.5979 | 2.3243 | 0.535 | 0.5335 | 0.1913 | 0.2189 | | No log | 54.0 | 378 | 1.6182 | 0.545 | 0.6002 | 2.3604 | 0.545 | 0.5432 | 0.1740 | 0.2157 | | No log | 55.0 | 385 | 1.6143 | 0.54 | 0.5981 | 2.3632 | 0.54 | 0.5348 | 0.1911 | 0.2165 | | No log | 56.0 | 392 | 1.6163 | 0.5375 | 0.6010 | 2.3551 | 0.5375 | 0.5338 | 0.1950 | 0.2191 | | No log | 57.0 | 399 | 1.6125 | 0.54 | 0.5984 | 2.3597 | 0.54 | 0.5378 | 0.1680 | 0.2159 | | No log | 58.0 | 406 | 1.6159 | 0.5425 | 0.5997 | 2.3564 | 0.5425 | 0.5388 | 0.1796 | 0.2175 | | No log | 59.0 | 413 | 1.6102 | 0.5475 | 0.5967 | 2.3879 | 0.5475 | 0.5448 | 0.1863 | 0.2152 | | No log | 60.0 | 420 | 1.6171 | 0.535 | 0.6006 | 2.3594 | 0.535 | 0.5298 | 0.1987 | 0.2183 | | No log | 61.0 | 427 | 1.6162 | 0.5375 | 0.5987 | 2.3631 | 0.5375 | 0.5341 | 0.1836 | 0.2172 | | No log | 62.0 | 434 | 1.6167 | 0.5425 | 0.5992 | 2.3344 | 0.5425 | 0.5395 | 0.2011 | 0.2170 | | No log | 63.0 | 441 | 1.6123 | 0.545 | 0.5984 | 2.3575 | 0.545 | 0.5423 | 0.1946 | 0.2174 | | No log | 64.0 | 448 | 1.6209 | 0.535 | 0.6007 | 2.3333 | 0.535 | 0.5299 | 0.1973 | 0.2180 | | No log | 65.0 | 455 | 1.6184 | 0.5475 | 0.6003 | 2.3620 | 0.5475 | 0.5442 | 0.1911 | 0.2167 | | No log | 66.0 | 462 | 1.6179 | 0.5375 | 0.5992 | 2.3321 | 0.5375 | 0.5333 | 0.2019 | 0.2182 | | No log | 67.0 | 469 | 1.6179 | 0.5425 | 0.5999 | 2.3309 | 0.5425 | 0.5397 | 0.1780 | 0.2176 | | No log | 68.0 | 476 | 1.6204 | 0.545 | 0.6004 | 2.3282 | 0.545 | 0.5408 | 0.1740 | 0.2167 | | No log | 69.0 | 483 | 1.6195 | 0.54 | 0.6005 | 2.3372 | 0.54 | 0.5370 | 0.1898 | 0.2187 | | No log | 70.0 | 490 | 1.6189 | 0.5425 | 0.5996 | 2.3328 | 0.5425 | 0.5398 | 0.1754 | 0.2169 | | No log | 71.0 | 497 | 1.6202 | 0.5425 | 0.6007 | 2.3352 | 0.5425 | 0.5394 | 0.1973 | 0.2180 | | 0.3297 | 72.0 | 504 | 1.6175 | 0.5375 | 0.5997 | 2.3297 | 0.5375 | 0.5348 | 0.1721 | 0.2184 | | 0.3297 | 73.0 | 511 | 1.6185 | 0.545 | 0.6001 | 2.3339 | 0.545 | 0.5414 | 0.1780 | 0.2175 | | 0.3297 | 74.0 | 518 | 1.6203 | 0.5425 | 0.6003 | 2.3315 | 0.5425 | 0.5391 | 0.1855 | 0.2175 | | 0.3297 | 75.0 | 525 | 1.6199 | 0.54 | 0.6002 | 2.3366 | 0.54 | 0.5363 | 0.1841 | 0.2180 | | 0.3297 | 76.0 | 532 | 1.6203 | 0.5425 | 0.6002 | 2.3332 | 0.5425 | 0.5395 | 0.1736 | 0.2176 | | 0.3297 | 77.0 | 539 | 1.6191 | 0.5425 | 0.6000 | 2.3328 | 0.5425 | 0.5395 | 0.1722 | 0.2176 | | 0.3297 | 78.0 | 546 | 1.6202 | 0.54 | 0.6001 | 2.3317 | 0.54 | 0.5362 | 0.1909 | 0.2177 | | 0.3297 | 79.0 | 553 | 1.6199 | 0.54 | 0.6002 | 2.3335 | 0.54 | 0.5370 | 0.1982 | 0.2181 | | 0.3297 | 80.0 | 560 | 1.6207 | 0.54 | 0.6006 | 2.3336 | 0.54 | 0.5363 | 0.1983 | 0.2185 | | 0.3297 | 81.0 | 567 | 1.6210 | 0.5425 | 0.6003 | 2.3353 | 0.5425 | 0.5389 | 0.1875 | 0.2174 | | 0.3297 | 82.0 | 574 | 1.6216 | 0.5425 | 0.6005 | 2.3340 | 0.5425 | 0.5386 | 0.1792 | 0.2178 | | 0.3297 | 83.0 | 581 | 1.6213 | 0.5375 | 0.6007 | 2.3360 | 0.5375 | 0.5340 | 0.1797 | 0.2193 | | 0.3297 | 84.0 | 588 | 1.6208 | 0.54 | 0.6007 | 2.3345 | 0.54 | 0.5363 | 0.1848 | 0.2189 | | 0.3297 | 85.0 | 595 | 1.6221 | 0.54 | 0.6005 | 2.3360 | 0.54 | 0.5369 | 0.1836 | 0.2184 | | 0.3297 | 86.0 | 602 | 1.6218 | 0.5425 | 0.6005 | 2.3342 | 0.5425 | 0.5391 | 0.1922 | 0.2178 | | 0.3297 | 87.0 | 609 | 1.6224 | 0.54 | 0.6006 | 2.3365 | 0.54 | 0.5363 | 0.1849 | 0.2185 | | 0.3297 | 88.0 | 616 | 1.6216 | 0.54 | 0.6005 | 2.3355 | 0.54 | 0.5363 | 0.1912 | 0.2188 | | 0.3297 | 89.0 | 623 | 1.6222 | 0.535 | 0.6008 | 2.3359 | 0.535 | 0.5310 | 0.1890 | 0.2195 | | 0.3297 | 90.0 | 630 | 1.6218 | 0.5425 | 0.6007 | 2.3356 | 0.5425 | 0.5386 | 0.1930 | 0.2179 | | 0.3297 | 91.0 | 637 | 1.6226 | 0.54 | 0.6009 | 2.3363 | 0.54 | 0.5363 | 0.1941 | 0.2188 | | 0.3297 | 92.0 | 644 | 1.6224 | 0.54 | 0.6009 | 2.3360 | 0.54 | 0.5363 | 0.1890 | 0.2188 | | 0.3297 | 93.0 | 651 | 1.6226 | 0.5425 | 0.6009 | 2.3359 | 0.5425 | 0.5386 | 0.1964 | 0.2179 | | 0.3297 | 94.0 | 658 | 1.6227 | 0.54 | 0.6009 | 2.3363 | 0.54 | 0.5363 | 0.1877 | 0.2187 | | 0.3297 | 95.0 | 665 | 1.6226 | 0.54 | 0.6008 | 2.3360 | 0.54 | 0.5363 | 0.1916 | 0.2186 | | 0.3297 | 96.0 | 672 | 1.6225 | 0.54 | 0.6008 | 2.3362 | 0.54 | 0.5363 | 0.1972 | 0.2186 | | 0.3297 | 97.0 | 679 | 1.6227 | 0.5375 | 0.6009 | 2.3362 | 0.5375 | 0.5332 | 0.1949 | 0.2188 | | 0.3297 | 98.0 | 686 | 1.6228 | 0.5375 | 0.6009 | 2.3362 | 0.5375 | 0.5332 | 0.1924 | 0.2188 | | 0.3297 | 99.0 | 693 | 1.6228 | 0.5375 | 0.6009 | 2.3363 | 0.5375 | 0.5332 | 0.1924 | 0.2189 | | 0.3297 | 100.0 | 700 | 1.6228 | 0.5375 | 0.6009 | 2.3362 | 0.5375 | 0.5332 | 0.1886 | 0.2188 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.5

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6906 - Accuracy: 0.5675 - Brier Loss: 0.5696 - Nll: 2.4654 - F1 Micro: 0.5675 - F1 Macro: 0.5648 - Ece: 0.1638 - Aurc: 0.1969 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.6645 | 0.0425 | 1.0732 | 7.4792 | 0.0425 | 0.0329 | 0.2822 | 0.9534 | | No log | 2.0 | 14 | 3.6090 | 0.1 | 0.9462 | 5.7033 | 0.1000 | 0.0909 | 0.1715 | 0.8908 | | No log | 3.0 | 21 | 3.2938 | 0.1975 | 0.8945 | 5.2434 | 0.1975 | 0.1585 | 0.1594 | 0.6803 | | No log | 4.0 | 28 | 2.8501 | 0.29 | 0.7966 | 3.6125 | 0.29 | 0.2703 | 0.1664 | 0.4859 | | No log | 5.0 | 35 | 2.5122 | 0.39 | 0.7162 | 3.1122 | 0.39 | 0.3562 | 0.1838 | 0.3484 | | No log | 6.0 | 42 | 2.3667 | 0.4175 | 0.6955 | 3.3546 | 0.4175 | 0.3757 | 0.1683 | 0.3138 | | No log | 7.0 | 49 | 2.2629 | 0.4725 | 0.6766 | 2.9737 | 0.4725 | 0.4417 | 0.1917 | 0.2939 | | No log | 8.0 | 56 | 2.1495 | 0.485 | 0.6530 | 2.7257 | 0.485 | 0.4798 | 0.1901 | 0.2762 | | No log | 9.0 | 63 | 2.1491 | 0.495 | 0.6517 | 2.7709 | 0.495 | 0.4982 | 0.1934 | 0.2664 | | No log | 10.0 | 70 | 2.2541 | 0.465 | 0.6794 | 2.6880 | 0.465 | 0.4654 | 0.1923 | 0.2870 | | No log | 11.0 | 77 | 2.1607 | 0.4925 | 0.6564 | 2.7773 | 0.4925 | 0.4769 | 0.1904 | 0.2545 | | No log | 12.0 | 84 | 2.1581 | 0.505 | 0.6714 | 2.7017 | 0.505 | 0.4876 | 0.2023 | 0.2669 | | No log | 13.0 | 91 | 2.0025 | 0.515 | 0.6324 | 2.5557 | 0.515 | 0.5048 | 0.2194 | 0.2348 | | No log | 14.0 | 98 | 2.0267 | 0.5025 | 0.6474 | 2.4764 | 0.5025 | 0.4876 | 0.2071 | 0.2474 | | No log | 15.0 | 105 | 1.9258 | 0.535 | 0.6106 | 2.4995 | 0.535 | 0.5310 | 0.1960 | 0.2251 | | No log | 16.0 | 112 | 1.8650 | 0.545 | 0.6031 | 2.4944 | 0.545 | 0.5383 | 0.1847 | 0.2187 | | No log | 17.0 | 119 | 1.8902 | 0.5375 | 0.6118 | 2.5468 | 0.5375 | 0.5284 | 0.1852 | 0.2278 | | No log | 18.0 | 126 | 1.9187 | 0.53 | 0.6188 | 2.5580 | 0.53 | 0.5174 | 0.1792 | 0.2285 | | No log | 19.0 | 133 | 1.8399 | 0.54 | 0.6049 | 2.5246 | 0.54 | 0.5362 | 0.1758 | 0.2191 | | No log | 20.0 | 140 | 1.8912 | 0.53 | 0.6117 | 2.5403 | 0.53 | 0.5139 | 0.1819 | 0.2210 | | No log | 21.0 | 147 | 1.8930 | 0.5325 | 0.6099 | 2.6739 | 0.5325 | 0.5294 | 0.1736 | 0.2281 | | No log | 22.0 | 154 | 1.8930 | 0.5275 | 0.6070 | 2.6060 | 0.5275 | 0.5244 | 0.1832 | 0.2214 | | No log | 23.0 | 161 | 1.8596 | 0.5375 | 0.6030 | 2.5066 | 0.5375 | 0.5260 | 0.1864 | 0.2185 | | No log | 24.0 | 168 | 1.8328 | 0.5375 | 0.5942 | 2.5127 | 0.5375 | 0.5302 | 0.1807 | 0.2079 | | No log | 25.0 | 175 | 1.8069 | 0.53 | 0.6004 | 2.4747 | 0.53 | 0.5241 | 0.1910 | 0.2189 | | No log | 26.0 | 182 | 1.7937 | 0.5425 | 0.5912 | 2.5083 | 0.5425 | 0.5366 | 0.1841 | 0.2123 | | No log | 27.0 | 189 | 1.7986 | 0.55 | 0.5945 | 2.5267 | 0.55 | 0.5476 | 0.1901 | 0.2134 | | No log | 28.0 | 196 | 1.8090 | 0.5525 | 0.5918 | 2.4467 | 0.5525 | 0.5466 | 0.1765 | 0.2125 | | No log | 29.0 | 203 | 1.7911 | 0.55 | 0.5931 | 2.4536 | 0.55 | 0.5462 | 0.1619 | 0.2140 | | No log | 30.0 | 210 | 1.7702 | 0.5425 | 0.5911 | 2.5037 | 0.5425 | 0.5340 | 0.1819 | 0.2080 | | No log | 31.0 | 217 | 1.8248 | 0.53 | 0.6063 | 2.4965 | 0.53 | 0.5145 | 0.1757 | 0.2217 | | No log | 32.0 | 224 | 1.7866 | 0.545 | 0.5920 | 2.5543 | 0.545 | 0.5365 | 0.1745 | 0.2082 | | No log | 33.0 | 231 | 1.7793 | 0.555 | 0.5867 | 2.4751 | 0.555 | 0.5503 | 0.1657 | 0.2107 | | No log | 34.0 | 238 | 1.7417 | 0.5525 | 0.5799 | 2.4174 | 0.5525 | 0.5517 | 0.1778 | 0.2049 | | No log | 35.0 | 245 | 1.7557 | 0.54 | 0.5869 | 2.4065 | 0.54 | 0.5379 | 0.1640 | 0.2119 | | No log | 36.0 | 252 | 1.7567 | 0.54 | 0.5884 | 2.5031 | 0.54 | 0.5374 | 0.1519 | 0.2122 | | No log | 37.0 | 259 | 1.7306 | 0.56 | 0.5781 | 2.5313 | 0.56 | 0.5551 | 0.1906 | 0.2042 | | No log | 38.0 | 266 | 1.7615 | 0.5425 | 0.5888 | 2.4510 | 0.5425 | 0.5423 | 0.1783 | 0.2121 | | No log | 39.0 | 273 | 1.7607 | 0.555 | 0.5855 | 2.5044 | 0.555 | 0.5473 | 0.1808 | 0.2062 | | No log | 40.0 | 280 | 1.7430 | 0.5275 | 0.5840 | 2.4265 | 0.5275 | 0.5228 | 0.1822 | 0.2125 | | No log | 41.0 | 287 | 1.7515 | 0.5625 | 0.5800 | 2.5432 | 0.5625 | 0.5500 | 0.1812 | 0.2019 | | No log | 42.0 | 294 | 1.7229 | 0.5475 | 0.5778 | 2.4597 | 0.5475 | 0.5416 | 0.1656 | 0.2042 | | No log | 43.0 | 301 | 1.7566 | 0.555 | 0.5818 | 2.4305 | 0.555 | 0.5517 | 0.1521 | 0.2036 | | No log | 44.0 | 308 | 1.7093 | 0.575 | 0.5745 | 2.4087 | 0.575 | 0.5761 | 0.1678 | 0.2019 | | No log | 45.0 | 315 | 1.7319 | 0.56 | 0.5788 | 2.5339 | 0.56 | 0.5550 | 0.1647 | 0.2028 | | No log | 46.0 | 322 | 1.7331 | 0.5475 | 0.5863 | 2.4386 | 0.5475 | 0.5470 | 0.1737 | 0.2113 | | No log | 47.0 | 329 | 1.7243 | 0.56 | 0.5776 | 2.5336 | 0.56 | 0.5548 | 0.1642 | 0.2037 | | No log | 48.0 | 336 | 1.7205 | 0.5675 | 0.5817 | 2.4578 | 0.5675 | 0.5661 | 0.1800 | 0.2091 | | No log | 49.0 | 343 | 1.7210 | 0.5725 | 0.5721 | 2.4792 | 0.5725 | 0.5656 | 0.1550 | 0.1970 | | No log | 50.0 | 350 | 1.6994 | 0.58 | 0.5719 | 2.4645 | 0.58 | 0.5773 | 0.1580 | 0.1957 | | No log | 51.0 | 357 | 1.7082 | 0.5775 | 0.5737 | 2.4805 | 0.5775 | 0.5701 | 0.1734 | 0.1992 | | No log | 52.0 | 364 | 1.7144 | 0.5575 | 0.5693 | 2.4501 | 0.5575 | 0.5519 | 0.1812 | 0.1974 | | No log | 53.0 | 371 | 1.7196 | 0.57 | 0.5796 | 2.4555 | 0.57 | 0.5618 | 0.1728 | 0.2022 | | No log | 54.0 | 378 | 1.7149 | 0.5675 | 0.5746 | 2.4363 | 0.5675 | 0.5605 | 0.1901 | 0.1999 | | No log | 55.0 | 385 | 1.7030 | 0.555 | 0.5775 | 2.4253 | 0.555 | 0.5528 | 0.1823 | 0.2029 | | No log | 56.0 | 392 | 1.7209 | 0.5675 | 0.5747 | 2.4926 | 0.5675 | 0.5634 | 0.1644 | 0.2008 | | No log | 57.0 | 399 | 1.7103 | 0.5525 | 0.5760 | 2.4528 | 0.5525 | 0.5487 | 0.1738 | 0.2025 | | No log | 58.0 | 406 | 1.7005 | 0.5575 | 0.5771 | 2.4678 | 0.5575 | 0.5547 | 0.1514 | 0.2041 | | No log | 59.0 | 413 | 1.7098 | 0.56 | 0.5763 | 2.4368 | 0.56 | 0.5585 | 0.1650 | 0.2022 | | No log | 60.0 | 420 | 1.6976 | 0.5775 | 0.5681 | 2.4633 | 0.5775 | 0.5737 | 0.1729 | 0.1963 | | No log | 61.0 | 427 | 1.7057 | 0.5575 | 0.5739 | 2.4717 | 0.5575 | 0.5522 | 0.1932 | 0.2007 | | No log | 62.0 | 434 | 1.6884 | 0.5725 | 0.5646 | 2.4538 | 0.5725 | 0.5693 | 0.1692 | 0.1930 | | No log | 63.0 | 441 | 1.6979 | 0.56 | 0.5731 | 2.4635 | 0.56 | 0.5562 | 0.1690 | 0.2016 | | No log | 64.0 | 448 | 1.6848 | 0.55 | 0.5686 | 2.4583 | 0.55 | 0.5452 | 0.1782 | 0.1980 | | No log | 65.0 | 455 | 1.7072 | 0.5575 | 0.5774 | 2.4596 | 0.5575 | 0.5549 | 0.1627 | 0.2036 | | No log | 66.0 | 462 | 1.7057 | 0.5625 | 0.5759 | 2.4644 | 0.5625 | 0.5586 | 0.1780 | 0.2009 | | No log | 67.0 | 469 | 1.7016 | 0.56 | 0.5719 | 2.4976 | 0.56 | 0.5546 | 0.1734 | 0.2004 | | No log | 68.0 | 476 | 1.6951 | 0.5675 | 0.5725 | 2.4025 | 0.5675 | 0.5648 | 0.1866 | 0.1980 | | No log | 69.0 | 483 | 1.7012 | 0.555 | 0.5750 | 2.4970 | 0.555 | 0.5527 | 0.1958 | 0.2022 | | No log | 70.0 | 490 | 1.6983 | 0.575 | 0.5689 | 2.4763 | 0.575 | 0.5708 | 0.1648 | 0.1950 | | No log | 71.0 | 497 | 1.6954 | 0.5675 | 0.5729 | 2.4642 | 0.5675 | 0.5638 | 0.1762 | 0.2004 | | 0.437 | 72.0 | 504 | 1.6973 | 0.5625 | 0.5718 | 2.4639 | 0.5625 | 0.5605 | 0.1680 | 0.1991 | | 0.437 | 73.0 | 511 | 1.6942 | 0.565 | 0.5714 | 2.4629 | 0.565 | 0.5610 | 0.1776 | 0.1980 | | 0.437 | 74.0 | 518 | 1.6923 | 0.5725 | 0.5694 | 2.4717 | 0.5725 | 0.5698 | 0.1676 | 0.1967 | | 0.437 | 75.0 | 525 | 1.6926 | 0.5675 | 0.5699 | 2.4674 | 0.5675 | 0.5633 | 0.1741 | 0.1975 | | 0.437 | 76.0 | 532 | 1.6906 | 0.5675 | 0.5692 | 2.4673 | 0.5675 | 0.5641 | 0.1785 | 0.1962 | | 0.437 | 77.0 | 539 | 1.6912 | 0.565 | 0.5692 | 2.4671 | 0.565 | 0.5617 | 0.1568 | 0.1958 | | 0.437 | 78.0 | 546 | 1.6879 | 0.565 | 0.5685 | 2.4629 | 0.565 | 0.5620 | 0.1860 | 0.1954 | | 0.437 | 79.0 | 553 | 1.6886 | 0.565 | 0.5695 | 2.4650 | 0.565 | 0.5625 | 0.1777 | 0.1968 | | 0.437 | 80.0 | 560 | 1.6882 | 0.5625 | 0.5700 | 2.4632 | 0.5625 | 0.5583 | 0.1791 | 0.1982 | | 0.437 | 81.0 | 567 | 1.6918 | 0.565 | 0.5704 | 2.4638 | 0.565 | 0.5622 | 0.1630 | 0.1978 | | 0.437 | 82.0 | 574 | 1.6909 | 0.5675 | 0.5697 | 2.4646 | 0.5675 | 0.5641 | 0.1862 | 0.1959 | | 0.437 | 83.0 | 581 | 1.6881 | 0.565 | 0.5687 | 2.4665 | 0.565 | 0.5621 | 0.1494 | 0.1956 | | 0.437 | 84.0 | 588 | 1.6897 | 0.565 | 0.5692 | 2.4648 | 0.565 | 0.5626 | 0.1716 | 0.1969 | | 0.437 | 85.0 | 595 | 1.6910 | 0.5675 | 0.5697 | 2.4654 | 0.5675 | 0.5652 | 0.1747 | 0.1971 | | 0.437 | 86.0 | 602 | 1.6905 | 0.57 | 0.5694 | 2.4648 | 0.57 | 0.5667 | 0.1659 | 0.1959 | | 0.437 | 87.0 | 609 | 1.6896 | 0.5675 | 0.5693 | 2.4648 | 0.5675 | 0.5642 | 0.1681 | 0.1958 | | 0.437 | 88.0 | 616 | 1.6902 | 0.5675 | 0.5695 | 2.4655 | 0.5675 | 0.5642 | 0.1709 | 0.1961 | | 0.437 | 89.0 | 623 | 1.6907 | 0.5675 | 0.5697 | 2.4655 | 0.5675 | 0.5648 | 0.1676 | 0.1971 | | 0.437 | 90.0 | 630 | 1.6903 | 0.5675 | 0.5694 | 2.4657 | 0.5675 | 0.5648 | 0.1699 | 0.1966 | | 0.437 | 91.0 | 637 | 1.6906 | 0.565 | 0.5696 | 2.4656 | 0.565 | 0.5624 | 0.1689 | 0.1970 | | 0.437 | 92.0 | 644 | 1.6904 | 0.565 | 0.5695 | 2.4654 | 0.565 | 0.5622 | 0.1668 | 0.1970 | | 0.437 | 93.0 | 651 | 1.6904 | 0.5675 | 0.5696 | 2.4655 | 0.5675 | 0.5649 | 0.1619 | 0.1970 | | 0.437 | 94.0 | 658 | 1.6903 | 0.565 | 0.5694 | 2.4655 | 0.565 | 0.5622 | 0.1662 | 0.1969 | | 0.437 | 95.0 | 665 | 1.6905 | 0.565 | 0.5695 | 2.4653 | 0.565 | 0.5622 | 0.1710 | 0.1969 | | 0.437 | 96.0 | 672 | 1.6905 | 0.5675 | 0.5695 | 2.4655 | 0.5675 | 0.5648 | 0.1638 | 0.1968 | | 0.437 | 97.0 | 679 | 1.6906 | 0.565 | 0.5695 | 2.4654 | 0.565 | 0.5622 | 0.1664 | 0.1970 | | 0.437 | 98.0 | 686 | 1.6906 | 0.5675 | 0.5695 | 2.4655 | 0.5675 | 0.5648 | 0.1638 | 0.1969 | | 0.437 | 99.0 | 693 | 1.6906 | 0.5675 | 0.5696 | 2.4654 | 0.5675 | 0.5648 | 0.1638 | 0.1969 | | 0.437 | 100.0 | 700 | 1.6906 | 0.5675 | 0.5696 | 2.4654 | 0.5675 | 0.5648 | 0.1638 | 0.1969 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.7

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6224 - Accuracy: 0.555 - Brier Loss: 0.5813 - Nll: 2.4451 - F1 Micro: 0.555 - F1 Macro: 0.5481 - Ece: 0.1732 - Aurc: 0.2090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.2974 | 0.04 | 1.0725 | 7.3803 | 0.04 | 0.0315 | 0.2825 | 0.9545 | | No log | 2.0 | 14 | 3.3036 | 0.095 | 0.9476 | 5.7516 | 0.095 | 0.0792 | 0.1590 | 0.8944 | | No log | 3.0 | 21 | 2.9979 | 0.215 | 0.8918 | 5.3309 | 0.2150 | 0.1763 | 0.1514 | 0.6635 | | No log | 4.0 | 28 | 2.5846 | 0.2875 | 0.7979 | 3.5812 | 0.2875 | 0.2707 | 0.1619 | 0.4840 | | No log | 5.0 | 35 | 2.2908 | 0.3925 | 0.7162 | 3.1082 | 0.3925 | 0.3675 | 0.1724 | 0.3500 | | No log | 6.0 | 42 | 2.1582 | 0.4275 | 0.6903 | 3.3486 | 0.4275 | 0.3933 | 0.1723 | 0.3106 | | No log | 7.0 | 49 | 2.1910 | 0.445 | 0.7011 | 2.9994 | 0.445 | 0.4233 | 0.1889 | 0.3105 | | No log | 8.0 | 56 | 2.0287 | 0.485 | 0.6673 | 2.8482 | 0.485 | 0.4803 | 0.1781 | 0.2848 | | No log | 9.0 | 63 | 2.1037 | 0.4775 | 0.6684 | 2.8143 | 0.4775 | 0.4715 | 0.2010 | 0.2690 | | No log | 10.0 | 70 | 2.1168 | 0.4825 | 0.6846 | 2.8143 | 0.4825 | 0.4703 | 0.2082 | 0.2818 | | No log | 11.0 | 77 | 2.1094 | 0.495 | 0.6825 | 2.9020 | 0.495 | 0.4834 | 0.1990 | 0.2632 | | No log | 12.0 | 84 | 2.0835 | 0.48 | 0.6897 | 2.7522 | 0.48 | 0.4651 | 0.2261 | 0.2741 | | No log | 13.0 | 91 | 1.9606 | 0.505 | 0.6631 | 2.4794 | 0.505 | 0.4988 | 0.2034 | 0.2712 | | No log | 14.0 | 98 | 1.9519 | 0.4975 | 0.6567 | 2.7608 | 0.4975 | 0.4833 | 0.2042 | 0.2563 | | No log | 15.0 | 105 | 1.8794 | 0.52 | 0.6304 | 2.6588 | 0.52 | 0.5121 | 0.1814 | 0.2337 | | No log | 16.0 | 112 | 1.7934 | 0.5375 | 0.6191 | 2.5142 | 0.5375 | 0.5234 | 0.1853 | 0.2272 | | No log | 17.0 | 119 | 1.8110 | 0.5225 | 0.6242 | 2.5106 | 0.5225 | 0.5071 | 0.1918 | 0.2336 | | No log | 18.0 | 126 | 1.8027 | 0.515 | 0.6283 | 2.4142 | 0.515 | 0.4983 | 0.2020 | 0.2359 | | No log | 19.0 | 133 | 1.8123 | 0.5375 | 0.6318 | 2.5551 | 0.5375 | 0.5235 | 0.2132 | 0.2358 | | No log | 20.0 | 140 | 1.7937 | 0.5225 | 0.6292 | 2.5237 | 0.5225 | 0.5145 | 0.2070 | 0.2336 | | No log | 21.0 | 147 | 1.7272 | 0.5175 | 0.6046 | 2.4278 | 0.5175 | 0.5103 | 0.1806 | 0.2286 | | No log | 22.0 | 154 | 1.8337 | 0.5325 | 0.6396 | 2.5603 | 0.5325 | 0.5136 | 0.1884 | 0.2405 | | No log | 23.0 | 161 | 1.7416 | 0.5275 | 0.6102 | 2.5228 | 0.5275 | 0.5077 | 0.1784 | 0.2232 | | No log | 24.0 | 168 | 1.7036 | 0.55 | 0.6063 | 2.4933 | 0.55 | 0.5380 | 0.1776 | 0.2230 | | No log | 25.0 | 175 | 1.7330 | 0.545 | 0.6084 | 2.4943 | 0.545 | 0.5365 | 0.1989 | 0.2251 | | No log | 26.0 | 182 | 1.6911 | 0.55 | 0.5993 | 2.4401 | 0.55 | 0.5416 | 0.1792 | 0.2208 | | No log | 27.0 | 189 | 1.7329 | 0.5475 | 0.6162 | 2.4824 | 0.5475 | 0.5380 | 0.1830 | 0.2317 | | No log | 28.0 | 196 | 1.6890 | 0.5475 | 0.5992 | 2.4828 | 0.5475 | 0.5401 | 0.1725 | 0.2178 | | No log | 29.0 | 203 | 1.7256 | 0.5425 | 0.6124 | 2.5121 | 0.5425 | 0.5299 | 0.1765 | 0.2260 | | No log | 30.0 | 210 | 1.6854 | 0.5375 | 0.5952 | 2.4275 | 0.5375 | 0.5282 | 0.2015 | 0.2163 | | No log | 31.0 | 217 | 1.7010 | 0.5475 | 0.6030 | 2.4832 | 0.5475 | 0.5385 | 0.1862 | 0.2236 | | No log | 32.0 | 224 | 1.6840 | 0.535 | 0.5934 | 2.4512 | 0.535 | 0.5310 | 0.1794 | 0.2182 | | No log | 33.0 | 231 | 1.6808 | 0.545 | 0.6016 | 2.4424 | 0.545 | 0.5396 | 0.1828 | 0.2222 | | No log | 34.0 | 238 | 1.6965 | 0.535 | 0.6000 | 2.5453 | 0.535 | 0.5270 | 0.1846 | 0.2243 | | No log | 35.0 | 245 | 1.6650 | 0.545 | 0.5930 | 2.4901 | 0.545 | 0.5418 | 0.1706 | 0.2130 | | No log | 36.0 | 252 | 1.6494 | 0.54 | 0.5979 | 2.4011 | 0.54 | 0.5319 | 0.1731 | 0.2201 | | No log | 37.0 | 259 | 1.6738 | 0.54 | 0.5892 | 2.4632 | 0.54 | 0.5257 | 0.1781 | 0.2088 | | No log | 38.0 | 266 | 1.6502 | 0.55 | 0.5889 | 2.4733 | 0.55 | 0.5416 | 0.1957 | 0.2087 | | No log | 39.0 | 273 | 1.6539 | 0.55 | 0.5832 | 2.5105 | 0.55 | 0.5424 | 0.1584 | 0.2120 | | No log | 40.0 | 280 | 1.6399 | 0.545 | 0.5919 | 2.4450 | 0.545 | 0.5439 | 0.1877 | 0.2168 | | No log | 41.0 | 287 | 1.6968 | 0.535 | 0.6006 | 2.5310 | 0.535 | 0.5281 | 0.1895 | 0.2221 | | No log | 42.0 | 294 | 1.6430 | 0.5425 | 0.5955 | 2.4020 | 0.5425 | 0.5449 | 0.1722 | 0.2203 | | No log | 43.0 | 301 | 1.6743 | 0.535 | 0.5970 | 2.5138 | 0.535 | 0.5239 | 0.1869 | 0.2167 | | No log | 44.0 | 308 | 1.6544 | 0.5475 | 0.5949 | 2.4259 | 0.5475 | 0.5408 | 0.1651 | 0.2164 | | No log | 45.0 | 315 | 1.6763 | 0.535 | 0.5980 | 2.4379 | 0.535 | 0.5249 | 0.1868 | 0.2174 | | No log | 46.0 | 322 | 1.6509 | 0.525 | 0.5933 | 2.4351 | 0.525 | 0.5137 | 0.1861 | 0.2194 | | No log | 47.0 | 329 | 1.6530 | 0.5475 | 0.5929 | 2.4628 | 0.5475 | 0.5419 | 0.1859 | 0.2148 | | No log | 48.0 | 336 | 1.6410 | 0.555 | 0.5835 | 2.4992 | 0.555 | 0.5490 | 0.1805 | 0.2110 | | No log | 49.0 | 343 | 1.6398 | 0.5525 | 0.5857 | 2.5060 | 0.5525 | 0.5409 | 0.1706 | 0.2101 | | No log | 50.0 | 350 | 1.6343 | 0.5525 | 0.5814 | 2.4890 | 0.5525 | 0.5442 | 0.1608 | 0.2065 | | No log | 51.0 | 357 | 1.6335 | 0.5475 | 0.5846 | 2.4407 | 0.5475 | 0.5392 | 0.1720 | 0.2109 | | No log | 52.0 | 364 | 1.6309 | 0.555 | 0.5844 | 2.4944 | 0.555 | 0.5488 | 0.1697 | 0.2091 | | No log | 53.0 | 371 | 1.6308 | 0.5575 | 0.5826 | 2.4815 | 0.5575 | 0.5505 | 0.1704 | 0.2080 | | No log | 54.0 | 378 | 1.6279 | 0.56 | 0.5832 | 2.4741 | 0.56 | 0.5525 | 0.1724 | 0.2067 | | No log | 55.0 | 385 | 1.6226 | 0.55 | 0.5825 | 2.4048 | 0.55 | 0.5425 | 0.1656 | 0.2094 | | No log | 56.0 | 392 | 1.6141 | 0.555 | 0.5797 | 2.4716 | 0.555 | 0.5474 | 0.1813 | 0.2076 | | No log | 57.0 | 399 | 1.6179 | 0.56 | 0.5760 | 2.4682 | 0.56 | 0.5549 | 0.1474 | 0.2030 | | No log | 58.0 | 406 | 1.6278 | 0.56 | 0.5831 | 2.4758 | 0.56 | 0.5540 | 0.1681 | 0.2075 | | No log | 59.0 | 413 | 1.6257 | 0.5525 | 0.5817 | 2.4462 | 0.5525 | 0.5455 | 0.1648 | 0.2084 | | No log | 60.0 | 420 | 1.6306 | 0.5575 | 0.5861 | 2.5090 | 0.5575 | 0.5505 | 0.1687 | 0.2108 | | No log | 61.0 | 427 | 1.6314 | 0.555 | 0.5821 | 2.5024 | 0.555 | 0.5480 | 0.1725 | 0.2092 | | No log | 62.0 | 434 | 1.6322 | 0.545 | 0.5846 | 2.4694 | 0.545 | 0.5407 | 0.1848 | 0.2123 | | No log | 63.0 | 441 | 1.6229 | 0.5575 | 0.5829 | 2.4413 | 0.5575 | 0.5508 | 0.1698 | 0.2098 | | No log | 64.0 | 448 | 1.6187 | 0.56 | 0.5809 | 2.4420 | 0.56 | 0.5522 | 0.1848 | 0.2083 | | No log | 65.0 | 455 | 1.6160 | 0.555 | 0.5794 | 2.4349 | 0.555 | 0.5506 | 0.1761 | 0.2076 | | No log | 66.0 | 462 | 1.6254 | 0.55 | 0.5822 | 2.4752 | 0.55 | 0.5432 | 0.1629 | 0.2086 | | No log | 67.0 | 469 | 1.6259 | 0.55 | 0.5839 | 2.4425 | 0.55 | 0.5423 | 0.1769 | 0.2113 | | No log | 68.0 | 476 | 1.6245 | 0.55 | 0.5822 | 2.4382 | 0.55 | 0.5421 | 0.1736 | 0.2091 | | No log | 69.0 | 483 | 1.6243 | 0.5575 | 0.5830 | 2.4422 | 0.5575 | 0.5497 | 0.1808 | 0.2094 | | No log | 70.0 | 490 | 1.6223 | 0.5575 | 0.5810 | 2.4787 | 0.5575 | 0.5507 | 0.1556 | 0.2089 | | No log | 71.0 | 497 | 1.6204 | 0.5575 | 0.5809 | 2.4408 | 0.5575 | 0.5515 | 0.1555 | 0.2083 | | 0.3852 | 72.0 | 504 | 1.6225 | 0.55 | 0.5816 | 2.4404 | 0.55 | 0.5424 | 0.1750 | 0.2104 | | 0.3852 | 73.0 | 511 | 1.6237 | 0.55 | 0.5822 | 2.4403 | 0.55 | 0.5429 | 0.1772 | 0.2107 | | 0.3852 | 74.0 | 518 | 1.6220 | 0.55 | 0.5815 | 2.4441 | 0.55 | 0.5420 | 0.1649 | 0.2105 | | 0.3852 | 75.0 | 525 | 1.6228 | 0.5475 | 0.5818 | 2.4736 | 0.5475 | 0.5405 | 0.1882 | 0.2109 | | 0.3852 | 76.0 | 532 | 1.6224 | 0.5525 | 0.5814 | 2.4442 | 0.5525 | 0.5446 | 0.1817 | 0.2108 | | 0.3852 | 77.0 | 539 | 1.6225 | 0.5525 | 0.5815 | 2.4431 | 0.5525 | 0.5448 | 0.1798 | 0.2098 | | 0.3852 | 78.0 | 546 | 1.6213 | 0.555 | 0.5812 | 2.4417 | 0.555 | 0.5471 | 0.1680 | 0.2096 | | 0.3852 | 79.0 | 553 | 1.6208 | 0.5575 | 0.5808 | 2.4423 | 0.5575 | 0.5501 | 0.1784 | 0.2082 | | 0.3852 | 80.0 | 560 | 1.6218 | 0.5525 | 0.5811 | 2.4425 | 0.5525 | 0.5447 | 0.1683 | 0.2095 | | 0.3852 | 81.0 | 567 | 1.6225 | 0.5525 | 0.5814 | 2.4429 | 0.5525 | 0.5447 | 0.1856 | 0.2098 | | 0.3852 | 82.0 | 574 | 1.6222 | 0.5575 | 0.5812 | 2.4469 | 0.5575 | 0.5501 | 0.1953 | 0.2085 | | 0.3852 | 83.0 | 581 | 1.6219 | 0.555 | 0.5811 | 2.4442 | 0.555 | 0.5471 | 0.1940 | 0.2093 | | 0.3852 | 84.0 | 588 | 1.6220 | 0.555 | 0.5813 | 2.4443 | 0.555 | 0.5471 | 0.1867 | 0.2095 | | 0.3852 | 85.0 | 595 | 1.6223 | 0.555 | 0.5813 | 2.4446 | 0.555 | 0.5471 | 0.1885 | 0.2094 | | 0.3852 | 86.0 | 602 | 1.6222 | 0.5525 | 0.5812 | 2.4448 | 0.5525 | 0.5447 | 0.1749 | 0.2095 | | 0.3852 | 87.0 | 609 | 1.6222 | 0.555 | 0.5813 | 2.4454 | 0.555 | 0.5481 | 0.1745 | 0.2091 | | 0.3852 | 88.0 | 616 | 1.6222 | 0.5575 | 0.5813 | 2.4446 | 0.5575 | 0.5504 | 0.1767 | 0.2087 | | 0.3852 | 89.0 | 623 | 1.6222 | 0.5575 | 0.5813 | 2.4445 | 0.5575 | 0.5504 | 0.1839 | 0.2087 | | 0.3852 | 90.0 | 630 | 1.6221 | 0.555 | 0.5812 | 2.4447 | 0.555 | 0.5481 | 0.1814 | 0.2091 | | 0.3852 | 91.0 | 637 | 1.6222 | 0.5575 | 0.5813 | 2.4446 | 0.5575 | 0.5504 | 0.1790 | 0.2087 | | 0.3852 | 92.0 | 644 | 1.6222 | 0.555 | 0.5813 | 2.4447 | 0.555 | 0.5481 | 0.1755 | 0.2091 | | 0.3852 | 93.0 | 651 | 1.6223 | 0.5575 | 0.5813 | 2.4446 | 0.5575 | 0.5504 | 0.1747 | 0.2087 | | 0.3852 | 94.0 | 658 | 1.6223 | 0.5575 | 0.5813 | 2.4449 | 0.5575 | 0.5504 | 0.1747 | 0.2087 | | 0.3852 | 95.0 | 665 | 1.6223 | 0.555 | 0.5813 | 2.4448 | 0.555 | 0.5481 | 0.1732 | 0.2090 | | 0.3852 | 96.0 | 672 | 1.6224 | 0.555 | 0.5813 | 2.4451 | 0.555 | 0.5481 | 0.1720 | 0.2091 | | 0.3852 | 97.0 | 679 | 1.6223 | 0.5575 | 0.5813 | 2.4451 | 0.5575 | 0.5504 | 0.1735 | 0.2088 | | 0.3852 | 98.0 | 686 | 1.6223 | 0.5575 | 0.5813 | 2.4451 | 0.5575 | 0.5504 | 0.1747 | 0.2087 | | 0.3852 | 99.0 | 693 | 1.6224 | 0.555 | 0.5813 | 2.4451 | 0.555 | 0.5481 | 0.1732 | 0.2090 | | 0.3852 | 100.0 | 700 | 1.6224 | 0.555 | 0.5813 | 2.4451 | 0.555 | 0.5481 | 0.1732 | 0.2090 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9

# vit-tiny_rvl_cdip_100_examples_per_class_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5750 - Accuracy: 0.5325 - Brier Loss: 0.5990 - Nll: 2.5263 - F1 Micro: 0.5325 - F1 Macro: 0.5240 - Ece: 0.1659 - Aurc: 0.2152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 3.9285 | 0.04 | 1.0722 | 7.3572 | 0.04 | 0.0319 | 0.2792 | 0.9556 | | No log | 2.0 | 14 | 3.0027 | 0.095 | 0.9510 | 5.8779 | 0.095 | 0.0766 | 0.1668 | 0.8900 | | No log | 3.0 | 21 | 2.6988 | 0.225 | 0.8896 | 5.3750 | 0.225 | 0.1801 | 0.1669 | 0.6473 | | No log | 4.0 | 28 | 2.3179 | 0.285 | 0.8016 | 3.5658 | 0.285 | 0.2657 | 0.1679 | 0.4916 | | No log | 5.0 | 35 | 2.0566 | 0.37 | 0.7203 | 2.9834 | 0.37 | 0.3493 | 0.1684 | 0.3612 | | No log | 6.0 | 42 | 1.9505 | 0.4325 | 0.6892 | 3.0719 | 0.4325 | 0.4127 | 0.1775 | 0.3084 | | No log | 7.0 | 49 | 1.9995 | 0.4375 | 0.7008 | 3.1569 | 0.4375 | 0.4084 | 0.2032 | 0.3103 | | No log | 8.0 | 56 | 1.9133 | 0.445 | 0.6906 | 2.8574 | 0.445 | 0.4464 | 0.2016 | 0.3062 | | No log | 9.0 | 63 | 1.9876 | 0.4625 | 0.6918 | 2.9267 | 0.4625 | 0.4538 | 0.2228 | 0.2868 | | No log | 10.0 | 70 | 2.0051 | 0.4725 | 0.6971 | 2.9249 | 0.4725 | 0.4553 | 0.2234 | 0.2814 | | No log | 11.0 | 77 | 2.1834 | 0.465 | 0.7319 | 2.9998 | 0.465 | 0.4426 | 0.2444 | 0.3006 | | No log | 12.0 | 84 | 1.9953 | 0.4825 | 0.7087 | 2.7128 | 0.4825 | 0.4731 | 0.2386 | 0.2980 | | No log | 13.0 | 91 | 1.8834 | 0.4975 | 0.6771 | 2.6879 | 0.4975 | 0.4954 | 0.2240 | 0.2748 | | No log | 14.0 | 98 | 1.9647 | 0.4675 | 0.6987 | 2.8305 | 0.4675 | 0.4429 | 0.2409 | 0.2902 | | No log | 15.0 | 105 | 1.8810 | 0.5 | 0.6785 | 2.6402 | 0.5 | 0.4847 | 0.2171 | 0.2725 | | No log | 16.0 | 112 | 1.8777 | 0.4875 | 0.6877 | 2.6940 | 0.4875 | 0.4871 | 0.2210 | 0.2846 | | No log | 17.0 | 119 | 1.9260 | 0.4925 | 0.6796 | 2.7055 | 0.4925 | 0.4834 | 0.2012 | 0.2744 | | No log | 18.0 | 126 | 1.7864 | 0.505 | 0.6547 | 2.6724 | 0.505 | 0.4912 | 0.2081 | 0.2434 | | No log | 19.0 | 133 | 1.7618 | 0.4975 | 0.6430 | 2.5951 | 0.4975 | 0.4915 | 0.2172 | 0.2490 | | No log | 20.0 | 140 | 1.7496 | 0.515 | 0.6513 | 2.5263 | 0.515 | 0.5025 | 0.1975 | 0.2502 | | No log | 21.0 | 147 | 1.7082 | 0.5275 | 0.6438 | 2.4039 | 0.5275 | 0.5224 | 0.2017 | 0.2450 | | No log | 22.0 | 154 | 1.7482 | 0.4975 | 0.6682 | 2.5194 | 0.4975 | 0.4911 | 0.2247 | 0.2571 | | No log | 23.0 | 161 | 1.7377 | 0.5075 | 0.6482 | 2.4136 | 0.5075 | 0.4900 | 0.2221 | 0.2396 | | No log | 24.0 | 168 | 1.7094 | 0.515 | 0.6372 | 2.5605 | 0.515 | 0.5083 | 0.2137 | 0.2474 | | No log | 25.0 | 175 | 1.6884 | 0.5175 | 0.6422 | 2.5270 | 0.5175 | 0.5104 | 0.2111 | 0.2444 | | No log | 26.0 | 182 | 1.6489 | 0.5275 | 0.6246 | 2.5344 | 0.5275 | 0.5211 | 0.2066 | 0.2333 | | No log | 27.0 | 189 | 1.6165 | 0.53 | 0.6191 | 2.5418 | 0.53 | 0.5256 | 0.2021 | 0.2305 | | No log | 28.0 | 196 | 1.6316 | 0.5275 | 0.6181 | 2.6568 | 0.5275 | 0.5212 | 0.2004 | 0.2300 | | No log | 29.0 | 203 | 1.6595 | 0.5175 | 0.6306 | 2.4298 | 0.5175 | 0.5096 | 0.2020 | 0.2427 | | No log | 30.0 | 210 | 1.6193 | 0.5325 | 0.6157 | 2.5455 | 0.5325 | 0.5272 | 0.1779 | 0.2278 | | No log | 31.0 | 217 | 1.6517 | 0.5325 | 0.6274 | 2.4579 | 0.5325 | 0.5259 | 0.2006 | 0.2362 | | No log | 32.0 | 224 | 1.6434 | 0.5325 | 0.6167 | 2.5805 | 0.5325 | 0.5229 | 0.1995 | 0.2273 | | No log | 33.0 | 231 | 1.6660 | 0.5225 | 0.6269 | 2.6794 | 0.5225 | 0.5132 | 0.2244 | 0.2283 | | No log | 34.0 | 238 | 1.6353 | 0.515 | 0.6194 | 2.6085 | 0.515 | 0.5069 | 0.1839 | 0.2303 | | No log | 35.0 | 245 | 1.5920 | 0.5325 | 0.6051 | 2.5645 | 0.5325 | 0.5248 | 0.1868 | 0.2208 | | No log | 36.0 | 252 | 1.5909 | 0.54 | 0.6028 | 2.4786 | 0.54 | 0.5323 | 0.1902 | 0.2194 | | No log | 37.0 | 259 | 1.5730 | 0.5425 | 0.5983 | 2.4877 | 0.5425 | 0.5368 | 0.1799 | 0.2177 | | No log | 38.0 | 266 | 1.5800 | 0.535 | 0.6029 | 2.4736 | 0.535 | 0.5282 | 0.1761 | 0.2196 | | No log | 39.0 | 273 | 1.5594 | 0.54 | 0.5955 | 2.5093 | 0.54 | 0.5327 | 0.1900 | 0.2126 | | No log | 40.0 | 280 | 1.5685 | 0.53 | 0.5979 | 2.6068 | 0.53 | 0.5208 | 0.1893 | 0.2173 | | No log | 41.0 | 287 | 1.5757 | 0.53 | 0.5995 | 2.5655 | 0.53 | 0.5218 | 0.1862 | 0.2164 | | No log | 42.0 | 294 | 1.5797 | 0.535 | 0.6039 | 2.5445 | 0.535 | 0.5273 | 0.1834 | 0.2182 | | No log | 43.0 | 301 | 1.5900 | 0.53 | 0.6074 | 2.5201 | 0.53 | 0.5189 | 0.1747 | 0.2206 | | No log | 44.0 | 308 | 1.5760 | 0.5325 | 0.5986 | 2.4974 | 0.5325 | 0.5225 | 0.1870 | 0.2148 | | No log | 45.0 | 315 | 1.5768 | 0.53 | 0.6013 | 2.5174 | 0.53 | 0.5204 | 0.1979 | 0.2158 | | No log | 46.0 | 322 | 1.5774 | 0.53 | 0.6011 | 2.5199 | 0.53 | 0.5206 | 0.1882 | 0.2165 | | No log | 47.0 | 329 | 1.5714 | 0.54 | 0.5983 | 2.5329 | 0.54 | 0.5303 | 0.1884 | 0.2135 | | No log | 48.0 | 336 | 1.5834 | 0.5325 | 0.6026 | 2.5253 | 0.5325 | 0.5238 | 0.1658 | 0.2190 | | No log | 49.0 | 343 | 1.5724 | 0.5375 | 0.5979 | 2.5569 | 0.5375 | 0.5299 | 0.1617 | 0.2151 | | No log | 50.0 | 350 | 1.5685 | 0.5375 | 0.5985 | 2.5189 | 0.5375 | 0.5285 | 0.1919 | 0.2151 | | No log | 51.0 | 357 | 1.5708 | 0.54 | 0.5986 | 2.5002 | 0.54 | 0.5305 | 0.1755 | 0.2149 | | No log | 52.0 | 364 | 1.5665 | 0.535 | 0.5977 | 2.5224 | 0.535 | 0.5267 | 0.1842 | 0.2160 | | No log | 53.0 | 371 | 1.5713 | 0.5325 | 0.5993 | 2.5515 | 0.5325 | 0.5250 | 0.1753 | 0.2160 | | No log | 54.0 | 378 | 1.5693 | 0.535 | 0.5986 | 2.5516 | 0.535 | 0.5276 | 0.1841 | 0.2158 | | No log | 55.0 | 385 | 1.5693 | 0.5375 | 0.5984 | 2.5190 | 0.5375 | 0.5285 | 0.1842 | 0.2144 | | No log | 56.0 | 392 | 1.5725 | 0.535 | 0.5992 | 2.5527 | 0.535 | 0.5262 | 0.1776 | 0.2150 | | No log | 57.0 | 399 | 1.5674 | 0.5425 | 0.5976 | 2.5502 | 0.5425 | 0.5326 | 0.1902 | 0.2137 | | No log | 58.0 | 406 | 1.5675 | 0.5375 | 0.5974 | 2.5517 | 0.5375 | 0.5288 | 0.1794 | 0.2139 | | No log | 59.0 | 413 | 1.5713 | 0.535 | 0.5988 | 2.5515 | 0.535 | 0.5257 | 0.1791 | 0.2147 | | No log | 60.0 | 420 | 1.5729 | 0.535 | 0.5988 | 2.5512 | 0.535 | 0.5262 | 0.1796 | 0.2148 | | No log | 61.0 | 427 | 1.5702 | 0.5375 | 0.5976 | 2.5521 | 0.5375 | 0.5281 | 0.1817 | 0.2139 | | No log | 62.0 | 434 | 1.5728 | 0.535 | 0.5988 | 2.5514 | 0.535 | 0.5266 | 0.1722 | 0.2149 | | No log | 63.0 | 441 | 1.5720 | 0.5325 | 0.5985 | 2.5206 | 0.5325 | 0.5231 | 0.1790 | 0.2149 | | No log | 64.0 | 448 | 1.5704 | 0.5325 | 0.5975 | 2.5510 | 0.5325 | 0.5236 | 0.1706 | 0.2139 | | No log | 65.0 | 455 | 1.5724 | 0.5325 | 0.5986 | 2.5225 | 0.5325 | 0.5236 | 0.1557 | 0.2148 | | No log | 66.0 | 462 | 1.5718 | 0.5325 | 0.5985 | 2.5246 | 0.5325 | 0.5241 | 0.1772 | 0.2148 | | No log | 67.0 | 469 | 1.5710 | 0.5325 | 0.5981 | 2.5511 | 0.5325 | 0.5237 | 0.1625 | 0.2146 | | No log | 68.0 | 476 | 1.5716 | 0.54 | 0.5981 | 2.5001 | 0.54 | 0.5304 | 0.1622 | 0.2141 | | No log | 69.0 | 483 | 1.5732 | 0.5325 | 0.5988 | 2.5517 | 0.5325 | 0.5232 | 0.1641 | 0.2150 | | No log | 70.0 | 490 | 1.5733 | 0.5325 | 0.5987 | 2.5522 | 0.5325 | 0.5237 | 0.1715 | 0.2149 | | No log | 71.0 | 497 | 1.5729 | 0.5325 | 0.5985 | 2.5523 | 0.5325 | 0.5241 | 0.1670 | 0.2147 | | 0.3153 | 72.0 | 504 | 1.5730 | 0.5325 | 0.5987 | 2.5236 | 0.5325 | 0.5237 | 0.1656 | 0.2149 | | 0.3153 | 73.0 | 511 | 1.5723 | 0.5325 | 0.5985 | 2.5212 | 0.5325 | 0.5238 | 0.1893 | 0.2145 | | 0.3153 | 74.0 | 518 | 1.5738 | 0.5325 | 0.5989 | 2.5515 | 0.5325 | 0.5238 | 0.1744 | 0.2147 | | 0.3153 | 75.0 | 525 | 1.5740 | 0.5325 | 0.5988 | 2.5318 | 0.5325 | 0.5237 | 0.1683 | 0.2150 | | 0.3153 | 76.0 | 532 | 1.5734 | 0.535 | 0.5985 | 2.5525 | 0.535 | 0.5261 | 0.1763 | 0.2145 | | 0.3153 | 77.0 | 539 | 1.5740 | 0.5325 | 0.5989 | 2.5516 | 0.5325 | 0.5243 | 0.1726 | 0.2149 | | 0.3153 | 78.0 | 546 | 1.5738 | 0.5325 | 0.5987 | 2.5289 | 0.5325 | 0.5241 | 0.1692 | 0.2148 | | 0.3153 | 79.0 | 553 | 1.5736 | 0.5325 | 0.5987 | 2.5255 | 0.5325 | 0.5242 | 0.1807 | 0.2147 | | 0.3153 | 80.0 | 560 | 1.5739 | 0.5325 | 0.5988 | 2.5522 | 0.5325 | 0.5237 | 0.1769 | 0.2150 | | 0.3153 | 81.0 | 567 | 1.5743 | 0.5325 | 0.5989 | 2.5519 | 0.5325 | 0.5238 | 0.1837 | 0.2151 | | 0.3153 | 82.0 | 574 | 1.5742 | 0.5325 | 0.5989 | 2.5232 | 0.5325 | 0.5240 | 0.1712 | 0.2149 | | 0.3153 | 83.0 | 581 | 1.5744 | 0.5325 | 0.5989 | 2.5256 | 0.5325 | 0.5239 | 0.1803 | 0.2151 | | 0.3153 | 84.0 | 588 | 1.5741 | 0.5325 | 0.5988 | 2.5233 | 0.5325 | 0.5233 | 0.1655 | 0.2147 | | 0.3153 | 85.0 | 595 | 1.5747 | 0.5325 | 0.5990 | 2.5274 | 0.5325 | 0.5237 | 0.1696 | 0.2152 | | 0.3153 | 86.0 | 602 | 1.5747 | 0.5325 | 0.5989 | 2.5263 | 0.5325 | 0.5238 | 0.1689 | 0.2150 | | 0.3153 | 87.0 | 609 | 1.5745 | 0.5325 | 0.5989 | 2.5251 | 0.5325 | 0.5237 | 0.1654 | 0.2149 | | 0.3153 | 88.0 | 616 | 1.5747 | 0.5325 | 0.5989 | 2.5283 | 0.5325 | 0.5241 | 0.1693 | 0.2151 | | 0.3153 | 89.0 | 623 | 1.5748 | 0.5325 | 0.5990 | 2.5275 | 0.5325 | 0.5239 | 0.1596 | 0.2152 | | 0.3153 | 90.0 | 630 | 1.5749 | 0.5325 | 0.5990 | 2.5278 | 0.5325 | 0.5240 | 0.1602 | 0.2151 | | 0.3153 | 91.0 | 637 | 1.5750 | 0.5325 | 0.5990 | 2.5337 | 0.5325 | 0.5239 | 0.1623 | 0.2152 | | 0.3153 | 92.0 | 644 | 1.5749 | 0.5325 | 0.5990 | 2.5272 | 0.5325 | 0.5238 | 0.1653 | 0.2151 | | 0.3153 | 93.0 | 651 | 1.5751 | 0.5325 | 0.5990 | 2.5281 | 0.5325 | 0.5240 | 0.1663 | 0.2149 | | 0.3153 | 94.0 | 658 | 1.5750 | 0.5325 | 0.5990 | 2.5249 | 0.5325 | 0.5239 | 0.1715 | 0.2152 | | 0.3153 | 95.0 | 665 | 1.5749 | 0.535 | 0.5990 | 2.5257 | 0.535 | 0.5263 | 0.1625 | 0.2149 | | 0.3153 | 96.0 | 672 | 1.5750 | 0.5325 | 0.5990 | 2.5266 | 0.5325 | 0.5239 | 0.1655 | 0.2151 | | 0.3153 | 97.0 | 679 | 1.5750 | 0.5325 | 0.5990 | 2.5268 | 0.5325 | 0.5239 | 0.1686 | 0.2152 | | 0.3153 | 98.0 | 686 | 1.5750 | 0.5325 | 0.5990 | 2.5275 | 0.5325 | 0.5240 | 0.1664 | 0.2152 | | 0.3153 | 99.0 | 693 | 1.5750 | 0.5325 | 0.5990 | 2.5269 | 0.5325 | 0.5240 | 0.1678 | 0.2152 | | 0.3153 | 100.0 | 700 | 1.5750 | 0.5325 | 0.5990 | 2.5263 | 0.5325 | 0.5240 | 0.1659 | 0.2152 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "letter", "form", "email", "handwritten", "advertisement", "scientific_report", "scientific_publication", "specification", "file_folder", "news_article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo" ]

ayanban011/vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split

# vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0411 - Accuracy: 0.8333 - Brier Loss: 0.3084 - Nll: 1.3568 - F1 Micro: 0.8333 - F1 Macro: 0.8183 - Ece: 0.1563 - Aurc: 0.0847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.99 | 43 | 0.7544 | 0.7960 | 0.3088 | 1.3391 | 0.7960 | 0.7715 | 0.1991 | 0.0817 | | No log | 2.0 | 87 | 0.7158 | 0.8218 | 0.2920 | 1.1888 | 0.8218 | 0.7941 | 0.1863 | 0.0741 | | No log | 2.98 | 130 | 0.7144 | 0.7989 | 0.2932 | 1.2958 | 0.7989 | 0.7701 | 0.1628 | 0.0749 | | No log | 3.99 | 174 | 0.6762 | 0.8305 | 0.2749 | 1.1916 | 0.8305 | 0.8076 | 0.1844 | 0.0678 | | No log | 4.98 | 217 | 0.6710 | 0.8362 | 0.2745 | 1.0739 | 0.8362 | 0.8076 | 0.1696 | 0.0664 | | No log | 5.99 | 261 | 0.6532 | 0.8362 | 0.2675 | 1.0011 | 0.8362 | 0.8115 | 0.1750 | 0.0602 | | No log | 6.98 | 304 | 0.6404 | 0.8362 | 0.2635 | 1.0072 | 0.8362 | 0.8106 | 0.1714 | 0.0633 | | No log | 7.99 | 348 | 0.6635 | 0.8218 | 0.2707 | 1.0903 | 0.8218 | 0.8030 | 0.1513 | 0.0770 | | No log | 9.0 | 392 | 0.6167 | 0.8420 | 0.2534 | 1.0176 | 0.8420 | 0.8259 | 0.1613 | 0.0796 | | No log | 9.99 | 435 | 0.6496 | 0.8276 | 0.2703 | 0.9646 | 0.8276 | 0.8085 | 0.1643 | 0.0588 | | No log | 11.0 | 479 | 0.6091 | 0.8506 | 0.2467 | 1.1036 | 0.8506 | 0.8308 | 0.1483 | 0.0650 | | 0.4309 | 11.98 | 522 | 0.6075 | 0.8420 | 0.2483 | 0.9144 | 0.8420 | 0.8246 | 0.1391 | 0.0519 | | 0.4309 | 12.99 | 566 | 0.6164 | 0.8276 | 0.2576 | 0.9703 | 0.8276 | 0.8092 | 0.1467 | 0.0645 | | 0.4309 | 13.98 | 609 | 0.5893 | 0.8592 | 0.2347 | 1.1493 | 0.8592 | 0.8483 | 0.1347 | 0.0715 | | 0.4309 | 14.99 | 653 | 0.6123 | 0.8477 | 0.2485 | 1.1889 | 0.8477 | 0.8232 | 0.1587 | 0.0764 | | 0.4309 | 16.0 | 697 | 0.6352 | 0.8420 | 0.2615 | 1.1999 | 0.8420 | 0.8403 | 0.1368 | 0.0668 | | 0.4309 | 16.99 | 740 | 0.6329 | 0.8333 | 0.2625 | 1.1748 | 0.8333 | 0.8249 | 0.1267 | 0.0744 | | 0.4309 | 18.0 | 784 | 0.6350 | 0.8448 | 0.2590 | 1.2154 | 0.8448 | 0.8386 | 0.1423 | 0.0688 | | 0.4309 | 18.98 | 827 | 0.5892 | 0.8592 | 0.2383 | 1.1001 | 0.8592 | 0.8515 | 0.1293 | 0.0630 | | 0.4309 | 19.99 | 871 | 0.5981 | 0.8477 | 0.2476 | 1.0104 | 0.8477 | 0.8375 | 0.1345 | 0.0630 | | 0.4309 | 20.98 | 914 | 0.6484 | 0.8420 | 0.2642 | 1.3553 | 0.8420 | 0.8292 | 0.1490 | 0.0770 | | 0.4309 | 21.99 | 958 | 0.6298 | 0.8305 | 0.2657 | 1.1220 | 0.8305 | 0.8208 | 0.1292 | 0.0670 | | 0.1285 | 22.98 | 1001 | 0.6325 | 0.8391 | 0.2633 | 1.2549 | 0.8391 | 0.8362 | 0.1328 | 0.0708 | | 0.1285 | 23.99 | 1045 | 0.6032 | 0.8534 | 0.2486 | 1.1258 | 0.8534 | 0.8444 | 0.1229 | 0.0706 | | 0.1285 | 25.0 | 1089 | 0.6080 | 0.8534 | 0.2460 | 1.2033 | 0.8534 | 0.8414 | 0.1257 | 0.0755 | | 0.1285 | 25.99 | 1132 | 0.6321 | 0.8391 | 0.2667 | 1.2242 | 0.8391 | 0.8355 | 0.1349 | 0.0697 | | 0.1285 | 27.0 | 1176 | 0.6325 | 0.8592 | 0.2522 | 1.2029 | 0.8592 | 0.8493 | 0.1278 | 0.0778 | | 0.1285 | 27.98 | 1219 | 0.6585 | 0.8534 | 0.2546 | 1.3669 | 0.8534 | 0.8378 | 0.1368 | 0.0890 | | 0.1285 | 28.99 | 1263 | 0.6302 | 0.8563 | 0.2517 | 1.2419 | 0.8563 | 0.8508 | 0.1294 | 0.0751 | | 0.1285 | 29.98 | 1306 | 0.6663 | 0.8477 | 0.2637 | 1.4132 | 0.8477 | 0.8339 | 0.1399 | 0.0828 | | 0.1285 | 30.99 | 1350 | 0.7063 | 0.8362 | 0.2799 | 1.4323 | 0.8362 | 0.8330 | 0.1441 | 0.0863 | | 0.1285 | 32.0 | 1394 | 0.6564 | 0.8506 | 0.2570 | 1.1583 | 0.8506 | 0.8417 | 0.1358 | 0.0847 | | 0.1285 | 32.99 | 1437 | 0.6738 | 0.8477 | 0.2647 | 1.3855 | 0.8477 | 0.8398 | 0.1305 | 0.0775 | | 0.1285 | 34.0 | 1481 | 0.6528 | 0.8563 | 0.2559 | 1.2601 | 0.8563 | 0.8462 | 0.1310 | 0.0789 | | 0.0385 | 34.98 | 1524 | 0.6534 | 0.8563 | 0.2537 | 1.2931 | 0.8563 | 0.8461 | 0.1241 | 0.0773 | | 0.0385 | 35.99 | 1568 | 0.6541 | 0.8534 | 0.2525 | 1.2589 | 0.8534 | 0.8449 | 0.1315 | 0.0833 | | 0.0385 | 36.98 | 1611 | 0.6769 | 0.8592 | 0.2545 | 1.4351 | 0.8592 | 0.8492 | 0.1242 | 0.0792 | | 0.0385 | 37.99 | 1655 | 0.6824 | 0.8592 | 0.2576 | 1.2241 | 0.8592 | 0.8472 | 0.1327 | 0.0810 | | 0.0385 | 38.98 | 1698 | 0.6843 | 0.8563 | 0.2589 | 1.3394 | 0.8563 | 0.8450 | 0.1311 | 0.0802 | | 0.0385 | 39.99 | 1742 | 0.6964 | 0.8506 | 0.2630 | 1.2625 | 0.8506 | 0.8405 | 0.1310 | 0.0789 | | 0.0385 | 41.0 | 1786 | 0.7051 | 0.8534 | 0.2671 | 1.3296 | 0.8534 | 0.8434 | 0.1353 | 0.0794 | | 0.0385 | 41.99 | 1829 | 0.7006 | 0.8506 | 0.2645 | 1.2965 | 0.8506 | 0.8400 | 0.1373 | 0.0796 | | 0.0385 | 43.0 | 1873 | 0.7054 | 0.8563 | 0.2646 | 1.2973 | 0.8563 | 0.8450 | 0.1313 | 0.0790 | | 0.0385 | 43.98 | 1916 | 0.7143 | 0.8506 | 0.2673 | 1.2640 | 0.8506 | 0.8399 | 0.1359 | 0.0803 | | 0.0385 | 44.99 | 1960 | 0.7168 | 0.8534 | 0.2665 | 1.3058 | 0.8534 | 0.8429 | 0.1389 | 0.0820 | | 0.0206 | 45.98 | 2003 | 0.7204 | 0.8506 | 0.2669 | 1.3009 | 0.8506 | 0.8384 | 0.1336 | 0.0805 | | 0.0206 | 46.99 | 2047 | 0.7265 | 0.8534 | 0.2683 | 1.2633 | 0.8534 | 0.8415 | 0.1319 | 0.0806 | | 0.0206 | 48.0 | 2091 | 0.7311 | 0.8506 | 0.2695 | 1.2725 | 0.8506 | 0.8396 | 0.1372 | 0.0811 | | 0.0206 | 48.99 | 2134 | 0.7384 | 0.8477 | 0.2729 | 1.3385 | 0.8477 | 0.8364 | 0.1387 | 0.0807 | | 0.0206 | 50.0 | 2178 | 0.7383 | 0.8534 | 0.2695 | 1.1951 | 0.8534 | 0.8406 | 0.1344 | 0.0827 | | 0.0206 | 50.98 | 2221 | 0.7440 | 0.8506 | 0.2740 | 1.3360 | 0.8506 | 0.8394 | 0.1418 | 0.0812 | | 0.0206 | 51.99 | 2265 | 0.7455 | 0.8506 | 0.2727 | 1.2704 | 0.8506 | 0.8388 | 0.1351 | 0.0816 | | 0.0206 | 52.98 | 2308 | 0.7474 | 0.8506 | 0.2708 | 1.2622 | 0.8506 | 0.8384 | 0.1334 | 0.0823 | | 0.0206 | 53.99 | 2352 | 0.7581 | 0.8477 | 0.2750 | 1.3446 | 0.8477 | 0.8374 | 0.1406 | 0.0826 | | 0.0206 | 54.98 | 2395 | 0.7571 | 0.8477 | 0.2751 | 1.3703 | 0.8477 | 0.8363 | 0.1378 | 0.0814 | | 0.0206 | 55.99 | 2439 | 0.7618 | 0.8477 | 0.2752 | 1.3702 | 0.8477 | 0.8363 | 0.1363 | 0.0827 | | 0.0206 | 57.0 | 2483 | 0.7638 | 0.8477 | 0.2749 | 1.3774 | 0.8477 | 0.8363 | 0.1394 | 0.0819 | | 0.0135 | 57.99 | 2526 | 0.7693 | 0.8477 | 0.2760 | 1.3370 | 0.8477 | 0.8363 | 0.1378 | 0.0824 | | 0.0135 | 59.0 | 2570 | 0.7724 | 0.8448 | 0.2779 | 1.3710 | 0.8448 | 0.8344 | 0.1431 | 0.0823 | | 0.0135 | 59.98 | 2613 | 0.7780 | 0.8477 | 0.2784 | 1.3328 | 0.8477 | 0.8363 | 0.1463 | 0.0828 | | 0.0135 | 60.99 | 2657 | 0.7818 | 0.8477 | 0.2795 | 1.3289 | 0.8477 | 0.8363 | 0.1466 | 0.0828 | | 0.0135 | 61.98 | 2700 | 0.7847 | 0.8420 | 0.2805 | 1.3308 | 0.8420 | 0.8308 | 0.1418 | 0.0830 | | 0.0135 | 62.99 | 2744 | 0.7851 | 0.8448 | 0.2782 | 1.3650 | 0.8448 | 0.8344 | 0.1411 | 0.0834 | | 0.0135 | 64.0 | 2788 | 0.7925 | 0.8420 | 0.2829 | 1.4383 | 0.8420 | 0.8319 | 0.1425 | 0.0821 | | 0.0135 | 64.99 | 2831 | 0.7959 | 0.8448 | 0.2826 | 1.4130 | 0.8448 | 0.8353 | 0.1431 | 0.0826 | | 0.0135 | 66.0 | 2875 | 0.7989 | 0.8420 | 0.2821 | 1.4040 | 0.8420 | 0.8285 | 0.1446 | 0.0833 | | 0.0135 | 66.98 | 2918 | 0.7996 | 0.8477 | 0.2807 | 1.3296 | 0.8477 | 0.8363 | 0.1464 | 0.0837 | | 0.0135 | 67.99 | 2962 | 0.8042 | 0.8448 | 0.2824 | 1.3637 | 0.8448 | 0.8344 | 0.1434 | 0.0837 | | 0.0097 | 68.98 | 3005 | 0.8095 | 0.8391 | 0.2845 | 1.3635 | 0.8391 | 0.8275 | 0.1468 | 0.0835 | | 0.0097 | 69.99 | 3049 | 0.8073 | 0.8448 | 0.2824 | 1.3640 | 0.8448 | 0.8344 | 0.1413 | 0.0833 | | 0.0097 | 70.98 | 3092 | 0.8140 | 0.8477 | 0.2834 | 1.3617 | 0.8477 | 0.8363 | 0.1444 | 0.0837 | | 0.0097 | 71.99 | 3136 | 0.8152 | 0.8420 | 0.2842 | 1.4009 | 0.8420 | 0.8277 | 0.1439 | 0.0840 | | 0.0097 | 73.0 | 3180 | 0.8163 | 0.8391 | 0.2858 | 1.4029 | 0.8391 | 0.8246 | 0.1482 | 0.0836 | | 0.0097 | 73.99 | 3223 | 0.8192 | 0.8391 | 0.2844 | 1.3644 | 0.8391 | 0.8240 | 0.1475 | 0.0843 | | 0.0097 | 75.0 | 3267 | 0.8225 | 0.8448 | 0.2836 | 1.3593 | 0.8448 | 0.8344 | 0.1473 | 0.0847 | | 0.0097 | 75.98 | 3310 | 0.8267 | 0.8362 | 0.2859 | 1.3642 | 0.8362 | 0.8207 | 0.1473 | 0.0840 | | 0.0097 | 76.99 | 3354 | 0.8275 | 0.8391 | 0.2847 | 1.3618 | 0.8391 | 0.8240 | 0.1450 | 0.0849 | | 0.0097 | 77.98 | 3397 | 0.8325 | 0.8362 | 0.2879 | 1.3686 | 0.8362 | 0.8207 | 0.1491 | 0.0843 | | 0.0097 | 78.99 | 3441 | 0.8389 | 0.8448 | 0.2885 | 1.3629 | 0.8448 | 0.8329 | 0.1504 | 0.0833 | | 0.0097 | 80.0 | 3485 | 0.8420 | 0.8420 | 0.2887 | 1.3610 | 0.8420 | 0.8261 | 0.1458 | 0.0837 | | 0.0073 | 80.99 | 3528 | 0.8452 | 0.8362 | 0.2900 | 1.4064 | 0.8362 | 0.8221 | 0.1488 | 0.0833 | | 0.0073 | 82.0 | 3572 | 0.8492 | 0.8362 | 0.2898 | 1.4076 | 0.8362 | 0.8221 | 0.1500 | 0.0837 | | 0.0073 | 82.98 | 3615 | 0.8478 | 0.8362 | 0.2895 | 1.3609 | 0.8362 | 0.8207 | 0.1485 | 0.0847 | | 0.0073 | 83.99 | 3659 | 0.8483 | 0.8391 | 0.2880 | 1.3622 | 0.8391 | 0.8243 | 0.1480 | 0.0842 | | 0.0073 | 84.98 | 3702 | 0.8534 | 0.8420 | 0.2892 | 1.3609 | 0.8420 | 0.8261 | 0.1468 | 0.0843 | | 0.0073 | 85.99 | 3746 | 0.8547 | 0.8333 | 0.2898 | 1.4028 | 0.8333 | 0.8186 | 0.1513 | 0.0846 | | 0.0073 | 86.98 | 3789 | 0.8618 | 0.8391 | 0.2906 | 1.3597 | 0.8391 | 0.8243 | 0.1445 | 0.0846 | | 0.0073 | 87.99 | 3833 | 0.8594 | 0.8420 | 0.2885 | 1.3265 | 0.8420 | 0.8311 | 0.1462 | 0.0848 | | 0.0073 | 89.0 | 3877 | 0.8669 | 0.8391 | 0.2911 | 1.3592 | 0.8391 | 0.8243 | 0.1471 | 0.0843 | | 0.0073 | 89.99 | 3920 | 0.8664 | 0.8391 | 0.2901 | 1.3597 | 0.8391 | 0.8243 | 0.1468 | 0.0852 | | 0.0073 | 91.0 | 3964 | 0.8678 | 0.8420 | 0.2905 | 1.3253 | 0.8420 | 0.8296 | 0.1462 | 0.0854 | | 0.0057 | 91.98 | 4007 | 0.8719 | 0.8391 | 0.2909 | 1.3585 | 0.8391 | 0.8243 | 0.1475 | 0.0853 | | 0.0057 | 92.99 | 4051 | 0.8768 | 0.8391 | 0.2930 | 1.3595 | 0.8391 | 0.8243 | 0.1493 | 0.0852 | | 0.0057 | 93.98 | 4094 | 0.8785 | 0.8333 | 0.2928 | 1.4034 | 0.8333 | 0.8203 | 0.1529 | 0.0849 | | 0.0057 | 94.99 | 4138 | 0.8859 | 0.8333 | 0.2942 | 1.3684 | 0.8333 | 0.8183 | 0.1543 | 0.0844 | | 0.0057 | 96.0 | 4182 | 0.8839 | 0.8362 | 0.2937 | 1.3597 | 0.8362 | 0.8221 | 0.1497 | 0.0852 | | 0.0057 | 96.99 | 4225 | 0.8864 | 0.8333 | 0.2940 | 1.4012 | 0.8333 | 0.8203 | 0.1532 | 0.0850 | | 0.0057 | 98.0 | 4269 | 0.8879 | 0.8362 | 0.2941 | 1.3607 | 0.8362 | 0.8221 | 0.1504 | 0.0849 | | 0.0057 | 98.98 | 4312 | 0.8921 | 0.8333 | 0.2954 | 1.3609 | 0.8333 | 0.8183 | 0.1521 | 0.0851 | | 0.0057 | 99.99 | 4356 | 0.8949 | 0.8391 | 0.2945 | 1.3575 | 0.8391 | 0.8243 | 0.1491 | 0.0854 | | 0.0057 | 100.98 | 4399 | 0.8945 | 0.8362 | 0.2945 | 1.3591 | 0.8362 | 0.8221 | 0.1500 | 0.0856 | | 0.0057 | 101.99 | 4443 | 0.8985 | 0.8333 | 0.2944 | 1.3599 | 0.8333 | 0.8183 | 0.1530 | 0.0854 | | 0.0057 | 102.98 | 4486 | 0.8987 | 0.8391 | 0.2951 | 1.3586 | 0.8391 | 0.8246 | 0.1499 | 0.0850 | | 0.0045 | 103.99 | 4530 | 0.9025 | 0.8362 | 0.2957 | 1.3592 | 0.8362 | 0.8221 | 0.1510 | 0.0857 | | 0.0045 | 105.0 | 4574 | 0.9082 | 0.8305 | 0.2972 | 1.3625 | 0.8305 | 0.8165 | 0.1568 | 0.0852 | | 0.0045 | 105.99 | 4617 | 0.9087 | 0.8362 | 0.2958 | 1.3579 | 0.8362 | 0.8221 | 0.1505 | 0.0858 | | 0.0045 | 107.0 | 4661 | 0.9105 | 0.8305 | 0.2977 | 1.3619 | 0.8305 | 0.8165 | 0.1561 | 0.0844 | | 0.0045 | 107.98 | 4704 | 0.9136 | 0.8305 | 0.2978 | 1.3994 | 0.8305 | 0.8165 | 0.1559 | 0.0851 | | 0.0045 | 108.99 | 4748 | 0.9148 | 0.8391 | 0.2968 | 1.3573 | 0.8391 | 0.8243 | 0.1504 | 0.0856 | | 0.0045 | 109.98 | 4791 | 0.9188 | 0.8333 | 0.2974 | 1.3569 | 0.8333 | 0.8183 | 0.1532 | 0.0850 | | 0.0045 | 110.99 | 4835 | 0.9164 | 0.8362 | 0.2959 | 1.3595 | 0.8362 | 0.8221 | 0.1507 | 0.0857 | | 0.0045 | 112.0 | 4879 | 0.9221 | 0.8333 | 0.2977 | 1.3573 | 0.8333 | 0.8183 | 0.1550 | 0.0857 | | 0.0045 | 112.99 | 4922 | 0.9256 | 0.8305 | 0.2990 | 1.3599 | 0.8305 | 0.8165 | 0.1574 | 0.0852 | | 0.0045 | 114.0 | 4966 | 0.9284 | 0.8305 | 0.2994 | 1.3610 | 0.8305 | 0.8165 | 0.1572 | 0.0848 | | 0.0037 | 114.98 | 5009 | 0.9312 | 0.8333 | 0.2998 | 1.3565 | 0.8333 | 0.8183 | 0.1537 | 0.0857 | | 0.0037 | 115.99 | 5053 | 0.9322 | 0.8333 | 0.2995 | 1.3583 | 0.8333 | 0.8183 | 0.1543 | 0.0852 | | 0.0037 | 116.98 | 5096 | 0.9385 | 0.8305 | 0.3007 | 1.3593 | 0.8305 | 0.8165 | 0.1577 | 0.0852 | | 0.0037 | 117.99 | 5140 | 0.9386 | 0.8305 | 0.3009 | 1.4329 | 0.8305 | 0.8165 | 0.1582 | 0.0851 | | 0.0037 | 118.98 | 5183 | 0.9386 | 0.8333 | 0.2996 | 1.3570 | 0.8333 | 0.8183 | 0.1542 | 0.0855 | | 0.0037 | 119.99 | 5227 | 0.9406 | 0.8333 | 0.2995 | 1.3554 | 0.8333 | 0.8183 | 0.1540 | 0.0848 | | 0.0037 | 121.0 | 5271 | 0.9442 | 0.8305 | 0.3006 | 1.3589 | 0.8305 | 0.8165 | 0.1570 | 0.0849 | | 0.0037 | 121.99 | 5314 | 0.9435 | 0.8333 | 0.3000 | 1.3551 | 0.8333 | 0.8183 | 0.1546 | 0.0855 | | 0.0037 | 123.0 | 5358 | 0.9456 | 0.8333 | 0.2996 | 1.3550 | 0.8333 | 0.8183 | 0.1544 | 0.0848 | | 0.0037 | 123.98 | 5401 | 0.9490 | 0.8333 | 0.3008 | 1.3561 | 0.8333 | 0.8183 | 0.1547 | 0.0850 | | 0.0037 | 124.99 | 5445 | 0.9500 | 0.8333 | 0.3011 | 1.3592 | 0.8333 | 0.8183 | 0.1551 | 0.0846 | | 0.0037 | 125.98 | 5488 | 0.9513 | 0.8333 | 0.3003 | 1.3549 | 0.8333 | 0.8183 | 0.1544 | 0.0845 | | 0.0031 | 126.99 | 5532 | 0.9575 | 0.8305 | 0.3024 | 1.3580 | 0.8305 | 0.8165 | 0.1581 | 0.0849 | | 0.0031 | 128.0 | 5576 | 0.9593 | 0.8305 | 0.3025 | 1.4028 | 0.8305 | 0.8165 | 0.1591 | 0.0851 | | 0.0031 | 128.99 | 5619 | 0.9594 | 0.8305 | 0.3021 | 1.3619 | 0.8305 | 0.8165 | 0.1579 | 0.0849 | | 0.0031 | 130.0 | 5663 | 0.9628 | 0.8305 | 0.3025 | 1.3589 | 0.8305 | 0.8165 | 0.1587 | 0.0847 | | 0.0031 | 130.98 | 5706 | 0.9652 | 0.8305 | 0.3031 | 1.3599 | 0.8305 | 0.8165 | 0.1593 | 0.0844 | | 0.0031 | 131.99 | 5750 | 0.9646 | 0.8362 | 0.3005 | 1.3353 | 0.8362 | 0.8205 | 0.1520 | 0.0851 | | 0.0031 | 132.98 | 5793 | 0.9658 | 0.8333 | 0.3021 | 1.3562 | 0.8333 | 0.8183 | 0.1555 | 0.0849 | | 0.0031 | 133.99 | 5837 | 0.9698 | 0.8333 | 0.3023 | 1.3545 | 0.8333 | 0.8183 | 0.1554 | 0.0845 | | 0.0031 | 134.98 | 5880 | 0.9716 | 0.8333 | 0.3032 | 1.3559 | 0.8333 | 0.8183 | 0.1555 | 0.0852 | | 0.0031 | 135.99 | 5924 | 0.9736 | 0.8305 | 0.3037 | 1.3624 | 0.8305 | 0.8165 | 0.1584 | 0.0849 | | 0.0031 | 137.0 | 5968 | 0.9760 | 0.8333 | 0.3039 | 1.3575 | 0.8333 | 0.8183 | 0.1551 | 0.0845 | | 0.0026 | 137.99 | 6011 | 0.9789 | 0.8305 | 0.3041 | 1.3569 | 0.8305 | 0.8165 | 0.1592 | 0.0848 | | 0.0026 | 139.0 | 6055 | 0.9801 | 0.8305 | 0.3040 | 1.3574 | 0.8305 | 0.8165 | 0.1598 | 0.0854 | | 0.0026 | 139.98 | 6098 | 0.9806 | 0.8333 | 0.3035 | 1.3552 | 0.8333 | 0.8183 | 0.1557 | 0.0852 | | 0.0026 | 140.99 | 6142 | 0.9835 | 0.8333 | 0.3041 | 1.3574 | 0.8333 | 0.8183 | 0.1564 | 0.0846 | | 0.0026 | 141.98 | 6185 | 0.9838 | 0.8333 | 0.3037 | 1.3549 | 0.8333 | 0.8183 | 0.1557 | 0.0849 | | 0.0026 | 142.99 | 6229 | 0.9872 | 0.8333 | 0.3044 | 1.3544 | 0.8333 | 0.8183 | 0.1557 | 0.0851 | | 0.0026 | 144.0 | 6273 | 0.9900 | 0.8305 | 0.3056 | 1.3654 | 0.8305 | 0.8165 | 0.1597 | 0.0847 | | 0.0026 | 144.99 | 6316 | 0.9907 | 0.8333 | 0.3049 | 1.3551 | 0.8333 | 0.8183 | 0.1565 | 0.0854 | | 0.0026 | 146.0 | 6360 | 0.9896 | 0.8333 | 0.3044 | 1.3569 | 0.8333 | 0.8183 | 0.1563 | 0.0843 | | 0.0026 | 146.98 | 6403 | 0.9938 | 0.8333 | 0.3053 | 1.3550 | 0.8333 | 0.8183 | 0.1562 | 0.0844 | | 0.0026 | 147.99 | 6447 | 0.9962 | 0.8305 | 0.3056 | 1.3615 | 0.8305 | 0.8165 | 0.1594 | 0.0844 | | 0.0026 | 148.98 | 6490 | 0.9954 | 0.8305 | 0.3051 | 1.3601 | 0.8305 | 0.8165 | 0.1590 | 0.0847 | | 0.0022 | 149.99 | 6534 | 0.9961 | 0.8333 | 0.3043 | 1.3550 | 0.8333 | 0.8183 | 0.1554 | 0.0847 | | 0.0022 | 150.98 | 6577 | 1.0026 | 0.8333 | 0.3059 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0853 | | 0.0022 | 151.99 | 6621 | 1.0004 | 0.8333 | 0.3049 | 1.3544 | 0.8333 | 0.8183 | 0.1566 | 0.0847 | | 0.0022 | 153.0 | 6665 | 1.0024 | 0.8305 | 0.3058 | 1.3606 | 0.8305 | 0.8165 | 0.1595 | 0.0846 | | 0.0022 | 153.99 | 6708 | 1.0054 | 0.8305 | 0.3064 | 1.3598 | 0.8305 | 0.8165 | 0.1591 | 0.0848 | | 0.0022 | 155.0 | 6752 | 1.0053 | 0.8333 | 0.3054 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0845 | | 0.0022 | 155.98 | 6795 | 1.0068 | 0.8333 | 0.3053 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.0022 | 156.99 | 6839 | 1.0076 | 0.8333 | 0.3055 | 1.3551 | 0.8333 | 0.8183 | 0.1561 | 0.0844 | | 0.0022 | 157.98 | 6882 | 1.0105 | 0.8333 | 0.3059 | 1.3546 | 0.8333 | 0.8183 | 0.1563 | 0.0845 | | 0.0022 | 158.99 | 6926 | 1.0114 | 0.8333 | 0.3061 | 1.3555 | 0.8333 | 0.8183 | 0.1559 | 0.0851 | | 0.0022 | 160.0 | 6970 | 1.0108 | 0.8333 | 0.3061 | 1.3586 | 0.8333 | 0.8183 | 0.1561 | 0.0848 | | 0.002 | 160.99 | 7013 | 1.0129 | 0.8333 | 0.3064 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0845 | | 0.002 | 162.0 | 7057 | 1.0141 | 0.8333 | 0.3060 | 1.3542 | 0.8333 | 0.8183 | 0.1562 | 0.0845 | | 0.002 | 162.98 | 7100 | 1.0150 | 0.8333 | 0.3063 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0847 | | 0.002 | 163.99 | 7144 | 1.0181 | 0.8305 | 0.3071 | 1.3616 | 0.8305 | 0.8165 | 0.1587 | 0.0847 | | 0.002 | 164.98 | 7187 | 1.0197 | 0.8305 | 0.3073 | 1.3610 | 0.8305 | 0.8165 | 0.1585 | 0.0847 | | 0.002 | 165.99 | 7231 | 1.0203 | 0.8333 | 0.3071 | 1.3566 | 0.8333 | 0.8183 | 0.1565 | 0.0846 | | 0.002 | 166.98 | 7274 | 1.0214 | 0.8333 | 0.3070 | 1.3561 | 0.8333 | 0.8183 | 0.1564 | 0.0845 | | 0.002 | 167.99 | 7318 | 1.0211 | 0.8333 | 0.3067 | 1.3558 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.002 | 169.0 | 7362 | 1.0255 | 0.8305 | 0.3077 | 1.3564 | 0.8305 | 0.8165 | 0.1592 | 0.0846 | | 0.002 | 169.99 | 7405 | 1.0238 | 0.8333 | 0.3066 | 1.3535 | 0.8333 | 0.8183 | 0.1567 | 0.0844 | | 0.002 | 171.0 | 7449 | 1.0258 | 0.8333 | 0.3075 | 1.3580 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.002 | 171.98 | 7492 | 1.0260 | 0.8333 | 0.3073 | 1.3594 | 0.8333 | 0.8183 | 0.1559 | 0.0846 | | 0.0018 | 172.99 | 7536 | 1.0281 | 0.8305 | 0.3077 | 1.3584 | 0.8305 | 0.8165 | 0.1586 | 0.0847 | | 0.0018 | 173.98 | 7579 | 1.0274 | 0.8333 | 0.3073 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0851 | | 0.0018 | 174.99 | 7623 | 1.0323 | 0.8305 | 0.3082 | 1.3577 | 0.8305 | 0.8165 | 0.1596 | 0.0848 | | 0.0018 | 176.0 | 7667 | 1.0303 | 0.8333 | 0.3076 | 1.3579 | 0.8333 | 0.8183 | 0.1561 | 0.0846 | | 0.0018 | 176.99 | 7710 | 1.0325 | 0.8333 | 0.3081 | 1.3567 | 0.8333 | 0.8183 | 0.1565 | 0.0845 | | 0.0018 | 178.0 | 7754 | 1.0319 | 0.8333 | 0.3077 | 1.3569 | 0.8333 | 0.8183 | 0.1560 | 0.0847 | | 0.0018 | 178.98 | 7797 | 1.0340 | 0.8333 | 0.3081 | 1.3568 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0018 | 179.99 | 7841 | 1.0331 | 0.8333 | 0.3072 | 1.3550 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0018 | 180.98 | 7884 | 1.0346 | 0.8333 | 0.3079 | 1.3563 | 0.8333 | 0.8183 | 0.1561 | 0.0847 | | 0.0018 | 181.99 | 7928 | 1.0344 | 0.8333 | 0.3079 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0847 | | 0.0018 | 182.98 | 7971 | 1.0363 | 0.8333 | 0.3080 | 1.3556 | 0.8333 | 0.8183 | 0.1566 | 0.0850 | | 0.0016 | 183.99 | 8015 | 1.0368 | 0.8333 | 0.3080 | 1.3569 | 0.8333 | 0.8183 | 0.1561 | 0.0847 | | 0.0016 | 185.0 | 8059 | 1.0369 | 0.8333 | 0.3080 | 1.3563 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0016 | 185.99 | 8102 | 1.0373 | 0.8333 | 0.3080 | 1.3565 | 0.8333 | 0.8183 | 0.1561 | 0.0850 | | 0.0016 | 187.0 | 8146 | 1.0377 | 0.8333 | 0.3080 | 1.3568 | 0.8333 | 0.8183 | 0.1561 | 0.0846 | | 0.0016 | 187.98 | 8189 | 1.0392 | 0.8333 | 0.3084 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0846 | | 0.0016 | 188.99 | 8233 | 1.0391 | 0.8333 | 0.3082 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0848 | | 0.0016 | 189.98 | 8276 | 1.0393 | 0.8333 | 0.3081 | 1.3561 | 0.8333 | 0.8183 | 0.1562 | 0.0847 | | 0.0016 | 190.99 | 8320 | 1.0398 | 0.8333 | 0.3084 | 1.3582 | 0.8333 | 0.8183 | 0.1562 | 0.0846 | | 0.0016 | 192.0 | 8364 | 1.0405 | 0.8333 | 0.3083 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 192.99 | 8407 | 1.0401 | 0.8333 | 0.3082 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 194.0 | 8451 | 1.0407 | 0.8333 | 0.3083 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0016 | 194.98 | 8494 | 1.0414 | 0.8333 | 0.3086 | 1.3573 | 0.8333 | 0.8183 | 0.1564 | 0.0847 | | 0.0015 | 195.99 | 8538 | 1.0410 | 0.8333 | 0.3084 | 1.3567 | 0.8333 | 0.8183 | 0.1564 | 0.0848 | | 0.0015 | 196.98 | 8581 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0846 | | 0.0015 | 197.42 | 8600 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0847 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hbenitez/AV_classifier1

# hbenitez/AV_classifier1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6840 - Validation Loss: 0.6659 - Train Accuracy: 0.5 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 80, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6840 | 0.6659 | 0.5 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.13.0-rc2 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "brake", "dont_brake" ]

arunboss/triage_R5_model

# triage_R5_model This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0123 - Accuracy: 0.6837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0452 | 1.0 | 159 | 1.9622 | 0.3814 | | 1.7034 | 2.0 | 319 | 1.5695 | 0.4923 | | 1.441 | 3.0 | 479 | 1.4427 | 0.5433 | | 1.2908 | 4.0 | 639 | 1.2970 | 0.5895 | | 1.2294 | 5.0 | 798 | 1.2293 | 0.6071 | | 1.1097 | 6.0 | 958 | 1.1892 | 0.6300 | | 1.0342 | 7.0 | 1118 | 1.1048 | 0.6546 | | 0.9644 | 8.0 | 1278 | 1.0731 | 0.6678 | | 0.8534 | 9.0 | 1437 | 1.0367 | 0.6766 | | 0.8037 | 10.0 | 1597 | 1.0211 | 0.6802 | | 0.7765 | 11.0 | 1757 | 1.0073 | 0.6885 | | 0.7658 | 11.94 | 1908 | 1.0123 | 0.6837 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "acne and rosacea", "actinic keratosis bcc other malignant lesions", "allergic reactions & similar", "atopic dermatitis eczema", "bullous disease", "hair loss alopecia and other hair diseases", "melanoma (skin cancer) nevi and moles", "nail fungus and other nail disease", "pigmentation disorders", "psoriasis & related diseases", "seborrheic keratoses and other benign tumors", "tinea ringworm candida", "urticaria (hives)", "vascular tumors", "vasculitis", "viral exanthems", "warts (molluscum) and other viral infections" ]

jordyvl/vit-tiny_tobacco3482_simkd__tNone_gNone__logits

# vit-tiny_tobacco3482_simkd__tNone_gNone__logits This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0388 - Accuracy: 0.82 - Brier Loss: 0.7227 - Nll: 2.6079 - F1 Micro: 0.82 - F1 Macro: 0.7960 - Ece: 0.6182 - Aurc: 0.0575 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 100 | 0.0492 | 0.03 | 0.9002 | 15.7630 | 0.03 | 0.0130 | 0.1184 | 0.9407 | | No log | 2.0 | 200 | 0.0479 | 0.045 | 0.8991 | 12.1839 | 0.045 | 0.0140 | 0.1350 | 0.9268 | | No log | 3.0 | 300 | 0.0472 | 0.305 | 0.8968 | 12.2323 | 0.305 | 0.1551 | 0.2930 | 0.5596 | | No log | 4.0 | 400 | 0.0463 | 0.305 | 0.8938 | 10.0711 | 0.305 | 0.1809 | 0.3077 | 0.5037 | | 0.0541 | 5.0 | 500 | 0.0453 | 0.325 | 0.8898 | 9.3778 | 0.325 | 0.2046 | 0.3193 | 0.4698 | | 0.0541 | 6.0 | 600 | 0.0442 | 0.4 | 0.8787 | 9.6363 | 0.4000 | 0.2403 | 0.3713 | 0.3759 | | 0.0541 | 7.0 | 700 | 0.0433 | 0.4 | 0.8639 | 9.3483 | 0.4000 | 0.2510 | 0.3685 | 0.3742 | | 0.0541 | 8.0 | 800 | 0.0428 | 0.475 | 0.8521 | 9.0007 | 0.4750 | 0.2666 | 0.4177 | 0.2978 | | 0.0541 | 9.0 | 900 | 0.0422 | 0.515 | 0.8438 | 7.6934 | 0.515 | 0.3644 | 0.4480 | 0.2431 | | 0.0451 | 10.0 | 1000 | 0.0417 | 0.515 | 0.8210 | 7.6243 | 0.515 | 0.3743 | 0.4387 | 0.2146 | | 0.0451 | 11.0 | 1100 | 0.0411 | 0.68 | 0.8190 | 3.6435 | 0.68 | 0.5226 | 0.5677 | 0.1272 | | 0.0451 | 12.0 | 1200 | 0.0403 | 0.655 | 0.7803 | 5.4537 | 0.655 | 0.5379 | 0.5202 | 0.1352 | | 0.0451 | 13.0 | 1300 | 0.0398 | 0.75 | 0.7745 | 4.1150 | 0.75 | 0.6543 | 0.5945 | 0.0749 | | 0.0451 | 14.0 | 1400 | 0.0390 | 0.77 | 0.7561 | 2.8538 | 0.7700 | 0.6703 | 0.6085 | 0.0753 | | 0.0406 | 15.0 | 1500 | 0.0392 | 0.745 | 0.7637 | 3.7047 | 0.745 | 0.6673 | 0.5796 | 0.1060 | | 0.0406 | 16.0 | 1600 | 0.0398 | 0.73 | 0.7603 | 4.3010 | 0.7300 | 0.6681 | 0.5693 | 0.0949 | | 0.0406 | 17.0 | 1700 | 0.0399 | 0.705 | 0.7610 | 3.6375 | 0.705 | 0.6564 | 0.5590 | 0.0923 | | 0.0406 | 18.0 | 1800 | 0.0397 | 0.705 | 0.7536 | 4.2628 | 0.705 | 0.6170 | 0.5472 | 0.1332 | | 0.0406 | 19.0 | 1900 | 0.0390 | 0.745 | 0.7400 | 2.8861 | 0.745 | 0.6686 | 0.5644 | 0.1070 | | 0.0379 | 20.0 | 2000 | 0.0394 | 0.785 | 0.7238 | 3.3111 | 0.785 | 0.7255 | 0.5995 | 0.0695 | | 0.0379 | 21.0 | 2100 | 0.0396 | 0.76 | 0.7419 | 3.1935 | 0.76 | 0.7463 | 0.5885 | 0.0793 | | 0.0379 | 22.0 | 2200 | 0.0396 | 0.785 | 0.7423 | 3.7954 | 0.785 | 0.7737 | 0.6043 | 0.0873 | | 0.0379 | 23.0 | 2300 | 0.0395 | 0.78 | 0.7321 | 3.8067 | 0.78 | 0.7491 | 0.5885 | 0.0908 | | 0.0379 | 24.0 | 2400 | 0.0387 | 0.8 | 0.7228 | 2.9339 | 0.8000 | 0.7758 | 0.6038 | 0.0609 | | 0.037 | 25.0 | 2500 | 0.0387 | 0.795 | 0.7222 | 2.6252 | 0.795 | 0.7601 | 0.6094 | 0.0606 | | 0.037 | 26.0 | 2600 | 0.0387 | 0.8 | 0.7241 | 2.6253 | 0.8000 | 0.7628 | 0.6110 | 0.0607 | | 0.037 | 27.0 | 2700 | 0.0387 | 0.795 | 0.7235 | 2.4818 | 0.795 | 0.7616 | 0.6093 | 0.0629 | | 0.037 | 28.0 | 2800 | 0.0387 | 0.795 | 0.7245 | 2.6226 | 0.795 | 0.7586 | 0.6032 | 0.0604 | | 0.037 | 29.0 | 2900 | 0.0387 | 0.805 | 0.7253 | 2.7588 | 0.805 | 0.7725 | 0.6144 | 0.0609 | | 0.0364 | 30.0 | 3000 | 0.0387 | 0.805 | 0.7233 | 2.4956 | 0.805 | 0.7701 | 0.6204 | 0.0594 | | 0.0364 | 31.0 | 3100 | 0.0387 | 0.81 | 0.7241 | 2.7695 | 0.81 | 0.7797 | 0.6188 | 0.0602 | | 0.0364 | 32.0 | 3200 | 0.0386 | 0.81 | 0.7239 | 2.6185 | 0.81 | 0.7797 | 0.6190 | 0.0580 | | 0.0364 | 33.0 | 3300 | 0.0387 | 0.805 | 0.7238 | 2.9106 | 0.805 | 0.7717 | 0.6182 | 0.0586 | | 0.0364 | 34.0 | 3400 | 0.0386 | 0.805 | 0.7231 | 2.9062 | 0.805 | 0.7725 | 0.6133 | 0.0590 | | 0.0364 | 35.0 | 3500 | 0.0387 | 0.805 | 0.7247 | 2.7645 | 0.805 | 0.7717 | 0.6141 | 0.0590 | | 0.0364 | 36.0 | 3600 | 0.0386 | 0.805 | 0.7238 | 2.9152 | 0.805 | 0.7717 | 0.6104 | 0.0578 | | 0.0364 | 37.0 | 3700 | 0.0387 | 0.805 | 0.7229 | 2.9094 | 0.805 | 0.7717 | 0.6142 | 0.0588 | | 0.0364 | 38.0 | 3800 | 0.0386 | 0.805 | 0.7237 | 2.9185 | 0.805 | 0.7717 | 0.6173 | 0.0565 | | 0.0364 | 39.0 | 3900 | 0.0386 | 0.805 | 0.7230 | 2.9178 | 0.805 | 0.7717 | 0.6131 | 0.0578 | | 0.0364 | 40.0 | 4000 | 0.0386 | 0.805 | 0.7233 | 2.9155 | 0.805 | 0.7717 | 0.6131 | 0.0561 | | 0.0364 | 41.0 | 4100 | 0.0386 | 0.805 | 0.7235 | 2.9142 | 0.805 | 0.7717 | 0.6173 | 0.0574 | | 0.0364 | 42.0 | 4200 | 0.0387 | 0.805 | 0.7225 | 2.9162 | 0.805 | 0.7717 | 0.6196 | 0.0572 | | 0.0364 | 43.0 | 4300 | 0.0387 | 0.805 | 0.7231 | 3.0596 | 0.805 | 0.7717 | 0.6139 | 0.0560 | | 0.0364 | 44.0 | 4400 | 0.0387 | 0.805 | 0.7229 | 3.0584 | 0.805 | 0.7717 | 0.6140 | 0.0558 | | 0.0364 | 45.0 | 4500 | 0.0386 | 0.815 | 0.7231 | 2.9107 | 0.815 | 0.7856 | 0.6224 | 0.0551 | | 0.0364 | 46.0 | 4600 | 0.0386 | 0.8 | 0.7228 | 3.0609 | 0.8000 | 0.7683 | 0.6154 | 0.0570 | | 0.0364 | 47.0 | 4700 | 0.0386 | 0.8 | 0.7229 | 3.0539 | 0.8000 | 0.7683 | 0.6141 | 0.0564 | | 0.0364 | 48.0 | 4800 | 0.0387 | 0.805 | 0.7228 | 2.9149 | 0.805 | 0.7753 | 0.6164 | 0.0559 | | 0.0364 | 49.0 | 4900 | 0.0387 | 0.805 | 0.7239 | 3.0631 | 0.805 | 0.7729 | 0.6144 | 0.0569 | | 0.0364 | 50.0 | 5000 | 0.0387 | 0.8 | 0.7231 | 3.0551 | 0.8000 | 0.7683 | 0.6094 | 0.0562 | | 0.0364 | 51.0 | 5100 | 0.0387 | 0.815 | 0.7232 | 3.0662 | 0.815 | 0.7868 | 0.6212 | 0.0569 | | 0.0364 | 52.0 | 5200 | 0.0386 | 0.805 | 0.7226 | 2.9067 | 0.805 | 0.7753 | 0.6241 | 0.0550 | | 0.0364 | 53.0 | 5300 | 0.0387 | 0.81 | 0.7236 | 2.9086 | 0.81 | 0.7761 | 0.6209 | 0.0558 | | 0.0364 | 54.0 | 5400 | 0.0387 | 0.805 | 0.7240 | 2.9108 | 0.805 | 0.7729 | 0.6072 | 0.0577 | | 0.0364 | 55.0 | 5500 | 0.0387 | 0.815 | 0.7228 | 2.9235 | 0.815 | 0.7832 | 0.6202 | 0.0556 | | 0.0364 | 56.0 | 5600 | 0.0387 | 0.82 | 0.7229 | 2.9335 | 0.82 | 0.7898 | 0.6273 | 0.0544 | | 0.0364 | 57.0 | 5700 | 0.0387 | 0.82 | 0.7230 | 2.9210 | 0.82 | 0.7900 | 0.6292 | 0.0561 | | 0.0364 | 58.0 | 5800 | 0.0387 | 0.82 | 0.7227 | 2.9211 | 0.82 | 0.7898 | 0.6325 | 0.0560 | | 0.0364 | 59.0 | 5900 | 0.0386 | 0.82 | 0.7238 | 3.0664 | 0.82 | 0.7898 | 0.6249 | 0.0548 | | 0.0364 | 60.0 | 6000 | 0.0387 | 0.82 | 0.7218 | 2.9137 | 0.82 | 0.7900 | 0.6397 | 0.0545 | | 0.0364 | 61.0 | 6100 | 0.0387 | 0.82 | 0.7233 | 3.0756 | 0.82 | 0.7900 | 0.6225 | 0.0563 | | 0.0364 | 62.0 | 6200 | 0.0387 | 0.815 | 0.7231 | 3.0725 | 0.815 | 0.7878 | 0.6190 | 0.0558 | | 0.0364 | 63.0 | 6300 | 0.0387 | 0.82 | 0.7218 | 3.0589 | 0.82 | 0.7900 | 0.6223 | 0.0554 | | 0.0364 | 64.0 | 6400 | 0.0387 | 0.82 | 0.7231 | 3.0632 | 0.82 | 0.7900 | 0.6289 | 0.0551 | | 0.0363 | 65.0 | 6500 | 0.0387 | 0.82 | 0.7227 | 3.0595 | 0.82 | 0.7900 | 0.6339 | 0.0560 | | 0.0363 | 66.0 | 6600 | 0.0387 | 0.82 | 0.7229 | 2.9000 | 0.82 | 0.7900 | 0.6320 | 0.0551 | | 0.0363 | 67.0 | 6700 | 0.0387 | 0.82 | 0.7222 | 2.9092 | 0.82 | 0.7900 | 0.6292 | 0.0548 | | 0.0363 | 68.0 | 6800 | 0.0387 | 0.82 | 0.7233 | 2.7662 | 0.82 | 0.7900 | 0.6299 | 0.0564 | | 0.0363 | 69.0 | 6900 | 0.0387 | 0.82 | 0.7230 | 2.9095 | 0.82 | 0.7900 | 0.6239 | 0.0555 | | 0.0363 | 70.0 | 7000 | 0.0387 | 0.82 | 0.7226 | 2.9175 | 0.82 | 0.7908 | 0.6274 | 0.0549 | | 0.0363 | 71.0 | 7100 | 0.0387 | 0.82 | 0.7237 | 3.0548 | 0.82 | 0.7900 | 0.6337 | 0.0550 | | 0.0363 | 72.0 | 7200 | 0.0387 | 0.815 | 0.7229 | 2.9144 | 0.815 | 0.7841 | 0.6207 | 0.0570 | | 0.0363 | 73.0 | 7300 | 0.0387 | 0.82 | 0.7235 | 2.9041 | 0.82 | 0.7900 | 0.6310 | 0.0564 | | 0.0363 | 74.0 | 7400 | 0.0387 | 0.82 | 0.7227 | 2.9094 | 0.82 | 0.7908 | 0.6291 | 0.0558 | | 0.0363 | 75.0 | 7500 | 0.0387 | 0.825 | 0.7236 | 2.9105 | 0.825 | 0.7983 | 0.6319 | 0.0543 | | 0.0363 | 76.0 | 7600 | 0.0387 | 0.82 | 0.7225 | 2.9172 | 0.82 | 0.7908 | 0.6260 | 0.0550 | | 0.0363 | 77.0 | 7700 | 0.0387 | 0.815 | 0.7227 | 2.9050 | 0.815 | 0.7841 | 0.6325 | 0.0557 | | 0.0363 | 78.0 | 7800 | 0.0387 | 0.825 | 0.7236 | 2.9242 | 0.825 | 0.7983 | 0.6264 | 0.0575 | | 0.0363 | 79.0 | 7900 | 0.0387 | 0.82 | 0.7231 | 2.9167 | 0.82 | 0.7900 | 0.6263 | 0.0572 | | 0.0363 | 80.0 | 8000 | 0.0387 | 0.825 | 0.7229 | 2.7707 | 0.825 | 0.8004 | 0.6311 | 0.0569 | | 0.0363 | 81.0 | 8100 | 0.0387 | 0.81 | 0.7230 | 2.9083 | 0.81 | 0.7812 | 0.6295 | 0.0573 | | 0.0363 | 82.0 | 8200 | 0.0387 | 0.82 | 0.7227 | 2.7600 | 0.82 | 0.7927 | 0.6263 | 0.0576 | | 0.0363 | 83.0 | 8300 | 0.0387 | 0.815 | 0.7226 | 2.7902 | 0.815 | 0.7930 | 0.6169 | 0.0576 | | 0.0363 | 84.0 | 8400 | 0.0387 | 0.815 | 0.7226 | 2.7666 | 0.815 | 0.7841 | 0.6254 | 0.0571 | | 0.0363 | 85.0 | 8500 | 0.0387 | 0.82 | 0.7228 | 2.7730 | 0.82 | 0.7960 | 0.6226 | 0.0566 | | 0.0363 | 86.0 | 8600 | 0.0387 | 0.815 | 0.7228 | 2.6311 | 0.815 | 0.7878 | 0.6186 | 0.0572 | | 0.0363 | 87.0 | 8700 | 0.0388 | 0.82 | 0.7230 | 2.6272 | 0.82 | 0.7924 | 0.6321 | 0.0575 | | 0.0363 | 88.0 | 8800 | 0.0387 | 0.82 | 0.7227 | 2.7579 | 0.82 | 0.7924 | 0.6318 | 0.0568 | | 0.0363 | 89.0 | 8900 | 0.0388 | 0.82 | 0.7227 | 2.7688 | 0.82 | 0.7960 | 0.6238 | 0.0575 | | 0.0363 | 90.0 | 9000 | 0.0387 | 0.82 | 0.7232 | 2.6100 | 0.82 | 0.7986 | 0.6290 | 0.0569 | | 0.0363 | 91.0 | 9100 | 0.0387 | 0.82 | 0.7228 | 2.6088 | 0.82 | 0.7960 | 0.6258 | 0.0574 | | 0.0363 | 92.0 | 9200 | 0.0387 | 0.825 | 0.7228 | 2.6134 | 0.825 | 0.8038 | 0.6239 | 0.0575 | | 0.0363 | 93.0 | 9300 | 0.0387 | 0.825 | 0.7229 | 2.6136 | 0.825 | 0.7990 | 0.6283 | 0.0576 | | 0.0363 | 94.0 | 9400 | 0.0387 | 0.82 | 0.7228 | 2.6112 | 0.82 | 0.7924 | 0.6235 | 0.0580 | | 0.0363 | 95.0 | 9500 | 0.0388 | 0.82 | 0.7228 | 2.6077 | 0.82 | 0.7960 | 0.6185 | 0.0575 | | 0.0363 | 96.0 | 9600 | 0.0388 | 0.825 | 0.7229 | 2.6073 | 0.825 | 0.8038 | 0.6206 | 0.0572 | | 0.0363 | 97.0 | 9700 | 0.0388 | 0.82 | 0.7227 | 2.6077 | 0.82 | 0.7960 | 0.6213 | 0.0572 | | 0.0363 | 98.0 | 9800 | 0.0388 | 0.815 | 0.7228 | 2.6094 | 0.815 | 0.7893 | 0.6161 | 0.0579 | | 0.0363 | 99.0 | 9900 | 0.0388 | 0.815 | 0.7228 | 2.6086 | 0.815 | 0.7893 | 0.6160 | 0.0579 | | 0.0363 | 100.0 | 10000 | 0.0388 | 0.82 | 0.7227 | 2.6079 | 0.82 | 0.7960 | 0.6182 | 0.0575 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

daxiboy/vit-base-patch16-224-finetuned-flower

# vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]

ayanban011/vit-base_tobacco_bs_16_lr_5e-6_e_300_wr_0.1_wd_0.2

# vit-base_tobacco_bs_16_lr_5e-6_e_300_wr_0.1_wd_0.2 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8461 - Accuracy: 0.775 - Brier Loss: 0.3632 - Nll: 1.4570 - F1 Micro: 0.775 - F1 Macro: 0.7418 - Ece: 0.2043 - Aurc: 0.1066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7447 | 0.815 | 0.3078 | 1.1882 | 0.815 | 0.7942 | 0.2385 | 0.0731 | | No log | 2.0 | 25 | 0.7442 | 0.815 | 0.3075 | 1.1872 | 0.815 | 0.7922 | 0.2401 | 0.0736 | | No log | 2.96 | 37 | 0.7439 | 0.815 | 0.3075 | 1.1883 | 0.815 | 0.7942 | 0.2292 | 0.0722 | | No log | 4.0 | 50 | 0.7463 | 0.815 | 0.3083 | 1.1904 | 0.815 | 0.7942 | 0.2454 | 0.0762 | | No log | 4.96 | 62 | 0.7441 | 0.805 | 0.3077 | 1.1886 | 0.805 | 0.7819 | 0.2322 | 0.0731 | | No log | 6.0 | 75 | 0.7408 | 0.81 | 0.3064 | 1.1842 | 0.81 | 0.7914 | 0.2217 | 0.0704 | | No log | 6.96 | 87 | 0.7448 | 0.81 | 0.3082 | 1.1852 | 0.81 | 0.7847 | 0.2341 | 0.0748 | | No log | 8.0 | 100 | 0.7454 | 0.815 | 0.3084 | 1.1882 | 0.815 | 0.7942 | 0.2129 | 0.0767 | | No log | 8.96 | 112 | 0.7462 | 0.815 | 0.3080 | 1.1954 | 0.815 | 0.7922 | 0.2535 | 0.0775 | | No log | 10.0 | 125 | 0.7427 | 0.81 | 0.3067 | 1.1924 | 0.81 | 0.7876 | 0.2280 | 0.0767 | | No log | 10.96 | 137 | 0.7420 | 0.815 | 0.3067 | 1.2033 | 0.815 | 0.7942 | 0.2611 | 0.0755 | | No log | 12.0 | 150 | 0.7417 | 0.805 | 0.3063 | 1.1881 | 0.805 | 0.7820 | 0.2456 | 0.0774 | | No log | 12.96 | 162 | 0.7442 | 0.815 | 0.3089 | 1.1895 | 0.815 | 0.8059 | 0.2230 | 0.0768 | | No log | 14.0 | 175 | 0.7398 | 0.805 | 0.3061 | 1.2547 | 0.805 | 0.7843 | 0.2310 | 0.0766 | | No log | 14.96 | 187 | 0.7355 | 0.81 | 0.3046 | 1.1887 | 0.81 | 0.7914 | 0.2328 | 0.0746 | | No log | 16.0 | 200 | 0.7368 | 0.81 | 0.3053 | 1.1894 | 0.81 | 0.7922 | 0.2256 | 0.0774 | | No log | 16.96 | 212 | 0.7355 | 0.81 | 0.3037 | 1.2537 | 0.81 | 0.7947 | 0.2077 | 0.0788 | | No log | 18.0 | 225 | 0.7407 | 0.81 | 0.3065 | 1.1882 | 0.81 | 0.7871 | 0.2421 | 0.0767 | | No log | 18.96 | 237 | 0.7279 | 0.8 | 0.2999 | 1.2540 | 0.8000 | 0.7796 | 0.2159 | 0.0742 | | No log | 20.0 | 250 | 0.7324 | 0.805 | 0.3042 | 1.1811 | 0.805 | 0.7841 | 0.2269 | 0.0763 | | No log | 20.96 | 262 | 0.7421 | 0.805 | 0.3079 | 1.1827 | 0.805 | 0.7850 | 0.2339 | 0.0797 | | No log | 22.0 | 275 | 0.7343 | 0.81 | 0.3050 | 1.1689 | 0.81 | 0.7877 | 0.2223 | 0.0784 | | No log | 22.96 | 287 | 0.7308 | 0.81 | 0.3032 | 1.1901 | 0.81 | 0.7922 | 0.2190 | 0.0774 | | No log | 24.0 | 300 | 0.7381 | 0.805 | 0.3057 | 1.3200 | 0.805 | 0.7853 | 0.2500 | 0.0819 | | No log | 24.96 | 312 | 0.7336 | 0.81 | 0.3042 | 1.3123 | 0.81 | 0.7903 | 0.2082 | 0.0795 | | No log | 26.0 | 325 | 0.7282 | 0.805 | 0.3020 | 1.2465 | 0.805 | 0.7847 | 0.2248 | 0.0792 | | No log | 26.96 | 337 | 0.7346 | 0.81 | 0.3050 | 1.2538 | 0.81 | 0.7956 | 0.2095 | 0.0818 | | No log | 28.0 | 350 | 0.7305 | 0.805 | 0.3031 | 1.2443 | 0.805 | 0.7850 | 0.2488 | 0.0823 | | No log | 28.96 | 362 | 0.7395 | 0.8 | 0.3071 | 1.3235 | 0.8000 | 0.7818 | 0.2223 | 0.0843 | | No log | 30.0 | 375 | 0.7349 | 0.8 | 0.3058 | 1.2511 | 0.8000 | 0.7733 | 0.2004 | 0.0817 | | No log | 30.96 | 387 | 0.7344 | 0.8 | 0.3048 | 1.2516 | 0.8000 | 0.7818 | 0.2183 | 0.0837 | | No log | 32.0 | 400 | 0.7332 | 0.795 | 0.3037 | 1.3836 | 0.795 | 0.7686 | 0.2185 | 0.0844 | | No log | 32.96 | 412 | 0.7306 | 0.81 | 0.3042 | 1.1767 | 0.81 | 0.7905 | 0.2117 | 0.0837 | | No log | 34.0 | 425 | 0.7326 | 0.8 | 0.3040 | 1.2058 | 0.8000 | 0.7783 | 0.2106 | 0.0857 | | No log | 34.96 | 437 | 0.7317 | 0.8 | 0.3045 | 1.3068 | 0.8000 | 0.7733 | 0.2337 | 0.0843 | | No log | 36.0 | 450 | 0.7345 | 0.805 | 0.3073 | 1.3065 | 0.805 | 0.7782 | 0.1928 | 0.0823 | | No log | 36.96 | 462 | 0.7367 | 0.8 | 0.3074 | 1.3259 | 0.8000 | 0.7733 | 0.1941 | 0.0860 | | No log | 38.0 | 475 | 0.7349 | 0.8 | 0.3073 | 1.3074 | 0.8000 | 0.7731 | 0.2138 | 0.0853 | | No log | 38.96 | 487 | 0.7331 | 0.81 | 0.3057 | 1.3149 | 0.81 | 0.7909 | 0.1981 | 0.0865 | | 0.1577 | 40.0 | 500 | 0.7269 | 0.8 | 0.3018 | 1.3700 | 0.8000 | 0.7746 | 0.2033 | 0.0865 | | 0.1577 | 40.96 | 512 | 0.7270 | 0.8 | 0.3020 | 1.3687 | 0.8000 | 0.7737 | 0.2108 | 0.0860 | | 0.1577 | 42.0 | 525 | 0.7356 | 0.805 | 0.3078 | 1.3105 | 0.805 | 0.7784 | 0.2053 | 0.0892 | | 0.1577 | 42.96 | 537 | 0.7291 | 0.8 | 0.3031 | 1.3687 | 0.8000 | 0.7746 | 0.2066 | 0.0876 | | 0.1577 | 44.0 | 550 | 0.7276 | 0.81 | 0.3034 | 1.3655 | 0.81 | 0.7844 | 0.2189 | 0.0872 | | 0.1577 | 44.96 | 562 | 0.7318 | 0.805 | 0.3050 | 1.3684 | 0.805 | 0.7793 | 0.2209 | 0.0893 | | 0.1577 | 46.0 | 575 | 0.7300 | 0.805 | 0.3041 | 1.3679 | 0.805 | 0.7793 | 0.2040 | 0.0885 | | 0.1577 | 46.96 | 587 | 0.7342 | 0.805 | 0.3060 | 1.3679 | 0.805 | 0.7797 | 0.2059 | 0.0893 | | 0.1577 | 48.0 | 600 | 0.7303 | 0.805 | 0.3045 | 1.3672 | 0.805 | 0.7797 | 0.1862 | 0.0889 | | 0.1577 | 48.96 | 612 | 0.7401 | 0.8 | 0.3090 | 1.3710 | 0.8000 | 0.7746 | 0.1930 | 0.0915 | | 0.1577 | 50.0 | 625 | 0.7329 | 0.795 | 0.3054 | 1.3696 | 0.795 | 0.7654 | 0.1984 | 0.0891 | | 0.1577 | 50.96 | 637 | 0.7363 | 0.795 | 0.3072 | 1.3689 | 0.795 | 0.7654 | 0.2196 | 0.0907 | | 0.1577 | 52.0 | 650 | 0.7402 | 0.805 | 0.3101 | 1.3646 | 0.805 | 0.7784 | 0.2028 | 0.0911 | | 0.1577 | 52.96 | 662 | 0.7347 | 0.8 | 0.3065 | 1.3687 | 0.8000 | 0.7746 | 0.2062 | 0.0894 | | 0.1577 | 54.0 | 675 | 0.7388 | 0.805 | 0.3097 | 1.3649 | 0.805 | 0.7784 | 0.2027 | 0.0907 | | 0.1577 | 54.96 | 687 | 0.7381 | 0.8 | 0.3087 | 1.3681 | 0.8000 | 0.7704 | 0.2120 | 0.0908 | | 0.1577 | 56.0 | 700 | 0.7372 | 0.805 | 0.3088 | 1.3646 | 0.805 | 0.7749 | 0.1866 | 0.0903 | | 0.1577 | 56.96 | 712 | 0.7403 | 0.805 | 0.3102 | 1.3682 | 0.805 | 0.7749 | 0.2287 | 0.0922 | | 0.1577 | 58.0 | 725 | 0.7352 | 0.8 | 0.3069 | 1.3680 | 0.8000 | 0.7704 | 0.2117 | 0.0900 | | 0.1577 | 58.96 | 737 | 0.7373 | 0.8 | 0.3079 | 1.3699 | 0.8000 | 0.7704 | 0.1990 | 0.0923 | | 0.1577 | 60.0 | 750 | 0.7353 | 0.795 | 0.3065 | 1.3690 | 0.795 | 0.7656 | 0.2078 | 0.0900 | | 0.1577 | 60.96 | 762 | 0.7357 | 0.805 | 0.3071 | 1.3657 | 0.805 | 0.7732 | 0.2076 | 0.0899 | | 0.1577 | 62.0 | 775 | 0.7409 | 0.79 | 0.3103 | 1.3737 | 0.79 | 0.7623 | 0.2066 | 0.0920 | | 0.1577 | 62.96 | 787 | 0.7393 | 0.795 | 0.3082 | 1.4518 | 0.795 | 0.7670 | 0.2047 | 0.0912 | | 0.1577 | 64.0 | 800 | 0.7417 | 0.8 | 0.3093 | 1.3304 | 0.8000 | 0.7684 | 0.1955 | 0.0917 | | 0.1577 | 64.96 | 812 | 0.7438 | 0.8 | 0.3121 | 1.3714 | 0.8000 | 0.7707 | 0.1782 | 0.0920 | | 0.1577 | 66.0 | 825 | 0.7408 | 0.8 | 0.3100 | 1.3758 | 0.8000 | 0.7709 | 0.1965 | 0.0931 | | 0.1577 | 66.96 | 837 | 0.7434 | 0.8 | 0.3112 | 1.3767 | 0.8000 | 0.7707 | 0.2124 | 0.0935 | | 0.1577 | 68.0 | 850 | 0.7393 | 0.8 | 0.3107 | 1.3038 | 0.8000 | 0.7704 | 0.1786 | 0.0901 | | 0.1577 | 68.96 | 862 | 0.7383 | 0.8 | 0.3090 | 1.3689 | 0.8000 | 0.7704 | 0.2041 | 0.0913 | | 0.1577 | 70.0 | 875 | 0.7436 | 0.8 | 0.3119 | 1.3658 | 0.8000 | 0.7704 | 0.1983 | 0.0932 | | 0.1577 | 70.96 | 887 | 0.7463 | 0.8 | 0.3130 | 1.3700 | 0.8000 | 0.7707 | 0.1932 | 0.0947 | | 0.1577 | 72.0 | 900 | 0.7464 | 0.795 | 0.3135 | 1.3720 | 0.795 | 0.7656 | 0.2089 | 0.0932 | | 0.1577 | 72.96 | 912 | 0.7469 | 0.8 | 0.3137 | 1.3703 | 0.8000 | 0.7707 | 0.2004 | 0.0943 | | 0.1577 | 74.0 | 925 | 0.7435 | 0.8 | 0.3124 | 1.3674 | 0.8000 | 0.7704 | 0.1958 | 0.0930 | | 0.1577 | 74.96 | 937 | 0.7427 | 0.8 | 0.3117 | 1.3708 | 0.8000 | 0.7707 | 0.2224 | 0.0921 | | 0.1577 | 76.0 | 950 | 0.7420 | 0.8 | 0.3111 | 1.3664 | 0.8000 | 0.7704 | 0.2145 | 0.0928 | | 0.1577 | 76.96 | 962 | 0.7457 | 0.8 | 0.3135 | 1.3690 | 0.8000 | 0.7707 | 0.2178 | 0.0934 | | 0.1577 | 78.0 | 975 | 0.7513 | 0.8 | 0.3163 | 1.3707 | 0.8000 | 0.7707 | 0.1964 | 0.0947 | | 0.1577 | 78.96 | 987 | 0.7466 | 0.8 | 0.3139 | 1.3722 | 0.8000 | 0.7704 | 0.2001 | 0.0936 | | 0.1081 | 80.0 | 1000 | 0.7491 | 0.8 | 0.3154 | 1.3712 | 0.8000 | 0.7707 | 0.2100 | 0.0943 | | 0.1081 | 80.96 | 1012 | 0.7483 | 0.8 | 0.3150 | 1.3675 | 0.8000 | 0.7704 | 0.2083 | 0.0939 | | 0.1081 | 82.0 | 1025 | 0.7523 | 0.8 | 0.3163 | 1.3742 | 0.8000 | 0.7707 | 0.2095 | 0.0958 | | 0.1081 | 82.96 | 1037 | 0.7511 | 0.8 | 0.3166 | 1.3703 | 0.8000 | 0.7707 | 0.2034 | 0.0944 | | 0.1081 | 84.0 | 1050 | 0.7481 | 0.8 | 0.3150 | 1.3687 | 0.8000 | 0.7704 | 0.2113 | 0.0941 | | 0.1081 | 84.96 | 1062 | 0.7501 | 0.8 | 0.3164 | 1.3668 | 0.8000 | 0.7693 | 0.2053 | 0.0932 | | 0.1081 | 86.0 | 1075 | 0.7539 | 0.8 | 0.3177 | 1.3725 | 0.8000 | 0.7707 | 0.2025 | 0.0951 | | 0.1081 | 86.96 | 1087 | 0.7550 | 0.8 | 0.3182 | 1.3731 | 0.8000 | 0.7707 | 0.1969 | 0.0953 | | 0.1081 | 88.0 | 1100 | 0.7553 | 0.8 | 0.3183 | 1.3697 | 0.8000 | 0.7707 | 0.1972 | 0.0952 | | 0.1081 | 88.96 | 1112 | 0.7535 | 0.8 | 0.3176 | 1.3719 | 0.8000 | 0.7707 | 0.2073 | 0.0945 | | 0.1081 | 90.0 | 1125 | 0.7558 | 0.795 | 0.3186 | 1.3742 | 0.795 | 0.7681 | 0.2018 | 0.0959 | | 0.1081 | 90.96 | 1137 | 0.7573 | 0.8 | 0.3193 | 1.3739 | 0.8000 | 0.7704 | 0.1919 | 0.0965 | | 0.1081 | 92.0 | 1150 | 0.7565 | 0.8 | 0.3193 | 1.3743 | 0.8000 | 0.7698 | 0.1967 | 0.0959 | | 0.1081 | 92.96 | 1162 | 0.7619 | 0.795 | 0.3218 | 1.3758 | 0.795 | 0.7681 | 0.1989 | 0.0974 | | 0.1081 | 94.0 | 1175 | 0.7577 | 0.8 | 0.3198 | 1.3793 | 0.8000 | 0.7696 | 0.1996 | 0.0957 | | 0.1081 | 94.96 | 1187 | 0.7575 | 0.795 | 0.3201 | 1.3781 | 0.795 | 0.7666 | 0.1954 | 0.0964 | | 0.1081 | 96.0 | 1200 | 0.7573 | 0.8 | 0.3199 | 1.3752 | 0.8000 | 0.7693 | 0.1863 | 0.0955 | | 0.1081 | 96.96 | 1212 | 0.7615 | 0.795 | 0.3216 | 1.3753 | 0.795 | 0.7681 | 0.1997 | 0.0975 | | 0.1081 | 98.0 | 1225 | 0.7603 | 0.795 | 0.3215 | 1.3731 | 0.795 | 0.7681 | 0.2051 | 0.0963 | | 0.1081 | 98.96 | 1237 | 0.7596 | 0.795 | 0.3209 | 1.3744 | 0.795 | 0.7673 | 0.2081 | 0.0959 | | 0.1081 | 100.0 | 1250 | 0.7582 | 0.795 | 0.3203 | 1.3743 | 0.795 | 0.7673 | 0.2024 | 0.0955 | | 0.1081 | 100.96 | 1262 | 0.7609 | 0.795 | 0.3223 | 1.3761 | 0.795 | 0.7681 | 0.1823 | 0.0968 | | 0.1081 | 102.0 | 1275 | 0.7632 | 0.785 | 0.3233 | 1.3758 | 0.785 | 0.7528 | 0.1833 | 0.0970 | | 0.1081 | 102.96 | 1287 | 0.7618 | 0.785 | 0.3219 | 1.3785 | 0.785 | 0.7516 | 0.2141 | 0.0970 | | 0.1081 | 104.0 | 1300 | 0.7633 | 0.795 | 0.3230 | 1.4970 | 0.795 | 0.7664 | 0.1956 | 0.0952 | | 0.1081 | 104.96 | 1312 | 0.7657 | 0.79 | 0.3243 | 1.4406 | 0.79 | 0.7639 | 0.1960 | 0.0961 | | 0.1081 | 106.0 | 1325 | 0.7673 | 0.785 | 0.3251 | 1.4424 | 0.785 | 0.7516 | 0.2083 | 0.0978 | | 0.1081 | 106.96 | 1337 | 0.7667 | 0.79 | 0.3250 | 1.4392 | 0.79 | 0.7639 | 0.1875 | 0.0976 | | 0.1081 | 108.0 | 1350 | 0.7690 | 0.785 | 0.3250 | 1.3876 | 0.785 | 0.7526 | 0.2078 | 0.0990 | | 0.1081 | 108.96 | 1362 | 0.7676 | 0.785 | 0.3252 | 1.3872 | 0.785 | 0.7554 | 0.2073 | 0.0985 | | 0.1081 | 110.0 | 1375 | 0.7662 | 0.79 | 0.3249 | 1.4335 | 0.79 | 0.7639 | 0.1939 | 0.0980 | | 0.1081 | 110.96 | 1387 | 0.7723 | 0.785 | 0.3273 | 1.4567 | 0.785 | 0.7554 | 0.2066 | 0.0995 | | 0.1081 | 112.0 | 1400 | 0.7665 | 0.78 | 0.3250 | 1.3960 | 0.78 | 0.7488 | 0.2066 | 0.0976 | | 0.1081 | 112.96 | 1412 | 0.7722 | 0.785 | 0.3275 | 1.4410 | 0.785 | 0.7573 | 0.2063 | 0.0991 | | 0.1081 | 114.0 | 1425 | 0.7722 | 0.79 | 0.3271 | 1.4039 | 0.79 | 0.7639 | 0.1902 | 0.0990 | | 0.1081 | 114.96 | 1437 | 0.7699 | 0.79 | 0.3264 | 1.3849 | 0.79 | 0.7644 | 0.1914 | 0.0982 | | 0.1081 | 116.0 | 1450 | 0.7749 | 0.785 | 0.3285 | 1.3854 | 0.785 | 0.7573 | 0.1942 | 0.0999 | | 0.1081 | 116.96 | 1462 | 0.7722 | 0.78 | 0.3279 | 1.4365 | 0.78 | 0.7488 | 0.1973 | 0.0991 | | 0.1081 | 118.0 | 1475 | 0.7763 | 0.78 | 0.3293 | 1.3823 | 0.78 | 0.7488 | 0.2050 | 0.1006 | | 0.1081 | 118.96 | 1487 | 0.7740 | 0.78 | 0.3287 | 1.3822 | 0.78 | 0.7488 | 0.2105 | 0.0991 | | 0.0821 | 120.0 | 1500 | 0.7761 | 0.785 | 0.3294 | 1.4414 | 0.785 | 0.7573 | 0.1996 | 0.0995 | | 0.0821 | 120.96 | 1512 | 0.7749 | 0.78 | 0.3289 | 1.4387 | 0.78 | 0.7488 | 0.1981 | 0.0991 | | 0.0821 | 122.0 | 1525 | 0.7763 | 0.78 | 0.3297 | 1.4395 | 0.78 | 0.7488 | 0.2175 | 0.0993 | | 0.0821 | 122.96 | 1537 | 0.7775 | 0.78 | 0.3305 | 1.4407 | 0.78 | 0.7488 | 0.2073 | 0.0993 | | 0.0821 | 124.0 | 1550 | 0.7770 | 0.78 | 0.3299 | 1.4411 | 0.78 | 0.7488 | 0.2096 | 0.0996 | | 0.0821 | 124.96 | 1562 | 0.7785 | 0.78 | 0.3309 | 1.4415 | 0.78 | 0.7488 | 0.2174 | 0.1004 | | 0.0821 | 126.0 | 1575 | 0.7808 | 0.78 | 0.3321 | 1.4431 | 0.78 | 0.7488 | 0.2082 | 0.1005 | | 0.0821 | 126.96 | 1587 | 0.7791 | 0.78 | 0.3312 | 1.4405 | 0.78 | 0.7488 | 0.2087 | 0.0998 | | 0.0821 | 128.0 | 1600 | 0.7789 | 0.78 | 0.3312 | 1.4386 | 0.78 | 0.7488 | 0.2047 | 0.0995 | | 0.0821 | 128.96 | 1612 | 0.7829 | 0.78 | 0.3330 | 1.4423 | 0.78 | 0.7488 | 0.1920 | 0.1005 | | 0.0821 | 130.0 | 1625 | 0.7797 | 0.78 | 0.3317 | 1.4400 | 0.78 | 0.7488 | 0.2013 | 0.1006 | | 0.0821 | 130.96 | 1637 | 0.7849 | 0.78 | 0.3336 | 1.4446 | 0.78 | 0.7491 | 0.2064 | 0.1006 | | 0.0821 | 132.0 | 1650 | 0.7817 | 0.78 | 0.3322 | 1.4396 | 0.78 | 0.7488 | 0.2060 | 0.1003 | | 0.0821 | 132.96 | 1662 | 0.7823 | 0.78 | 0.3329 | 1.4407 | 0.78 | 0.7488 | 0.1990 | 0.0999 | | 0.0821 | 134.0 | 1675 | 0.7869 | 0.78 | 0.3354 | 1.4482 | 0.78 | 0.7488 | 0.1999 | 0.1009 | | 0.0821 | 134.96 | 1687 | 0.7859 | 0.78 | 0.3349 | 1.4429 | 0.78 | 0.7488 | 0.1934 | 0.1013 | | 0.0821 | 136.0 | 1700 | 0.7867 | 0.78 | 0.3352 | 1.4437 | 0.78 | 0.7488 | 0.2114 | 0.1006 | | 0.0821 | 136.96 | 1712 | 0.7867 | 0.78 | 0.3350 | 1.4403 | 0.78 | 0.7488 | 0.2070 | 0.1011 | | 0.0821 | 138.0 | 1725 | 0.7851 | 0.78 | 0.3341 | 1.4439 | 0.78 | 0.7488 | 0.1906 | 0.1009 | | 0.0821 | 138.96 | 1737 | 0.7892 | 0.78 | 0.3360 | 1.4495 | 0.78 | 0.7488 | 0.2009 | 0.1020 | | 0.0821 | 140.0 | 1750 | 0.7893 | 0.78 | 0.3366 | 1.4434 | 0.78 | 0.7488 | 0.1976 | 0.1013 | | 0.0821 | 140.96 | 1762 | 0.7848 | 0.78 | 0.3344 | 1.4383 | 0.78 | 0.7488 | 0.1995 | 0.1001 | | 0.0821 | 142.0 | 1775 | 0.7911 | 0.78 | 0.3372 | 1.4487 | 0.78 | 0.7488 | 0.1995 | 0.1020 | | 0.0821 | 142.96 | 1787 | 0.7890 | 0.78 | 0.3362 | 1.4416 | 0.78 | 0.7488 | 0.2075 | 0.1010 | | 0.0821 | 144.0 | 1800 | 0.7915 | 0.78 | 0.3372 | 1.4476 | 0.78 | 0.7488 | 0.1842 | 0.1019 | | 0.0821 | 144.96 | 1812 | 0.7876 | 0.78 | 0.3351 | 1.4999 | 0.78 | 0.7488 | 0.1904 | 0.0995 | | 0.0821 | 146.0 | 1825 | 0.7933 | 0.78 | 0.3378 | 1.4469 | 0.78 | 0.7488 | 0.1973 | 0.1023 | | 0.0821 | 146.96 | 1837 | 0.7932 | 0.78 | 0.3383 | 1.4441 | 0.78 | 0.7488 | 0.2070 | 0.1016 | | 0.0821 | 148.0 | 1850 | 0.7907 | 0.78 | 0.3369 | 1.4439 | 0.78 | 0.7488 | 0.1932 | 0.1014 | | 0.0821 | 148.96 | 1862 | 0.7939 | 0.78 | 0.3386 | 1.4462 | 0.78 | 0.7488 | 0.1906 | 0.1015 | | 0.0821 | 150.0 | 1875 | 0.7943 | 0.78 | 0.3386 | 1.4449 | 0.78 | 0.7488 | 0.1965 | 0.1016 | | 0.0821 | 150.96 | 1887 | 0.7955 | 0.78 | 0.3393 | 1.5025 | 0.78 | 0.7488 | 0.2112 | 0.1015 | | 0.0821 | 152.0 | 1900 | 0.7936 | 0.78 | 0.3386 | 1.4407 | 0.78 | 0.7488 | 0.2112 | 0.1012 | | 0.0821 | 152.96 | 1912 | 0.7966 | 0.78 | 0.3400 | 1.5033 | 0.78 | 0.7488 | 0.1963 | 0.1012 | | 0.0821 | 154.0 | 1925 | 0.7981 | 0.78 | 0.3405 | 1.4495 | 0.78 | 0.7488 | 0.1895 | 0.1020 | | 0.0821 | 154.96 | 1937 | 0.7972 | 0.78 | 0.3401 | 1.4417 | 0.78 | 0.7488 | 0.1953 | 0.1018 | | 0.0821 | 156.0 | 1950 | 0.7922 | 0.78 | 0.3381 | 1.4395 | 0.78 | 0.7488 | 0.2056 | 0.0999 | | 0.0821 | 156.96 | 1962 | 0.8013 | 0.775 | 0.3425 | 1.4473 | 0.775 | 0.7451 | 0.1869 | 0.1028 | | 0.0821 | 158.0 | 1975 | 0.7977 | 0.78 | 0.3403 | 1.4446 | 0.78 | 0.7488 | 0.1872 | 0.1014 | | 0.0821 | 158.96 | 1987 | 0.7990 | 0.78 | 0.3412 | 1.4413 | 0.78 | 0.7488 | 0.1939 | 0.1017 | | 0.0668 | 160.0 | 2000 | 0.8048 | 0.775 | 0.3435 | 1.4532 | 0.775 | 0.7451 | 0.1966 | 0.1049 | | 0.0668 | 160.96 | 2012 | 0.8064 | 0.77 | 0.3448 | 1.4529 | 0.7700 | 0.7358 | 0.1953 | 0.1044 | | 0.0668 | 162.0 | 2025 | 0.7989 | 0.78 | 0.3412 | 1.4423 | 0.78 | 0.7488 | 0.2038 | 0.1022 | | 0.0668 | 162.96 | 2037 | 0.8001 | 0.78 | 0.3414 | 1.4440 | 0.78 | 0.7488 | 0.1972 | 0.1015 | | 0.0668 | 164.0 | 2050 | 0.8068 | 0.775 | 0.3448 | 1.4523 | 0.775 | 0.7396 | 0.2031 | 0.1036 | | 0.0668 | 164.96 | 2062 | 0.8046 | 0.785 | 0.3438 | 1.4475 | 0.785 | 0.7536 | 0.2070 | 0.1037 | | 0.0668 | 166.0 | 2075 | 0.8016 | 0.78 | 0.3426 | 1.4451 | 0.78 | 0.7488 | 0.1975 | 0.1012 | | 0.0668 | 166.96 | 2087 | 0.8053 | 0.78 | 0.3442 | 1.4485 | 0.78 | 0.7477 | 0.2112 | 0.1022 | | 0.0668 | 168.0 | 2100 | 0.8040 | 0.78 | 0.3433 | 1.4459 | 0.78 | 0.7422 | 0.2014 | 0.1031 | | 0.0668 | 168.96 | 2112 | 0.8048 | 0.785 | 0.3437 | 1.4479 | 0.785 | 0.7515 | 0.2046 | 0.1033 | | 0.0668 | 170.0 | 2125 | 0.8054 | 0.775 | 0.3447 | 1.5060 | 0.775 | 0.7450 | 0.1896 | 0.1017 | | 0.0668 | 170.96 | 2137 | 0.8067 | 0.775 | 0.3451 | 1.5079 | 0.775 | 0.7450 | 0.1898 | 0.1018 | | 0.0668 | 172.0 | 2150 | 0.8060 | 0.78 | 0.3447 | 1.4508 | 0.78 | 0.7488 | 0.1842 | 0.1022 | | 0.0668 | 172.96 | 2162 | 0.8127 | 0.77 | 0.3484 | 1.4513 | 0.7700 | 0.7358 | 0.2006 | 0.1042 | | 0.0668 | 174.0 | 2175 | 0.8080 | 0.77 | 0.3457 | 1.4453 | 0.7700 | 0.7349 | 0.2198 | 0.1034 | | 0.0668 | 174.96 | 2187 | 0.8095 | 0.775 | 0.3460 | 1.4471 | 0.775 | 0.7384 | 0.2029 | 0.1027 | | 0.0668 | 176.0 | 2200 | 0.8112 | 0.775 | 0.3467 | 1.4559 | 0.775 | 0.7395 | 0.1995 | 0.1036 | | 0.0668 | 176.96 | 2212 | 0.8089 | 0.77 | 0.3460 | 1.4485 | 0.7700 | 0.7357 | 0.2050 | 0.1019 | | 0.0668 | 178.0 | 2225 | 0.8093 | 0.77 | 0.3461 | 1.4459 | 0.7700 | 0.7357 | 0.1989 | 0.1021 | | 0.0668 | 178.96 | 2237 | 0.8118 | 0.775 | 0.3473 | 1.4499 | 0.775 | 0.7384 | 0.2085 | 0.1029 | | 0.0668 | 180.0 | 2250 | 0.8112 | 0.775 | 0.3472 | 1.4471 | 0.775 | 0.7384 | 0.2070 | 0.1027 | | 0.0668 | 180.96 | 2262 | 0.8124 | 0.77 | 0.3478 | 1.4484 | 0.7700 | 0.7357 | 0.1983 | 0.1029 | | 0.0668 | 182.0 | 2275 | 0.8140 | 0.77 | 0.3484 | 1.4489 | 0.7700 | 0.7357 | 0.1987 | 0.1038 | | 0.0668 | 182.96 | 2287 | 0.8137 | 0.77 | 0.3483 | 1.4491 | 0.7700 | 0.7357 | 0.2036 | 0.1030 | | 0.0668 | 184.0 | 2300 | 0.8133 | 0.77 | 0.3481 | 1.4468 | 0.7700 | 0.7357 | 0.2012 | 0.1024 | | 0.0668 | 184.96 | 2312 | 0.8152 | 0.77 | 0.3489 | 1.4525 | 0.7700 | 0.7357 | 0.1996 | 0.1029 | | 0.0668 | 186.0 | 2325 | 0.8149 | 0.77 | 0.3490 | 1.4511 | 0.7700 | 0.7357 | 0.1917 | 0.1027 | | 0.0668 | 186.96 | 2337 | 0.8151 | 0.77 | 0.3490 | 1.4489 | 0.7700 | 0.7357 | 0.1956 | 0.1028 | | 0.0668 | 188.0 | 2350 | 0.8175 | 0.77 | 0.3500 | 1.5084 | 0.7700 | 0.7357 | 0.2011 | 0.1038 | | 0.0668 | 188.96 | 2362 | 0.8181 | 0.765 | 0.3499 | 1.4506 | 0.765 | 0.7323 | 0.1975 | 0.1056 | | 0.0668 | 190.0 | 2375 | 0.8180 | 0.765 | 0.3504 | 1.4499 | 0.765 | 0.7323 | 0.2162 | 0.1050 | | 0.0668 | 190.96 | 2387 | 0.8168 | 0.77 | 0.3498 | 1.4510 | 0.7700 | 0.7357 | 0.2014 | 0.1039 | | 0.0668 | 192.0 | 2400 | 0.8183 | 0.77 | 0.3505 | 1.4483 | 0.7700 | 0.7379 | 0.2114 | 0.1032 | | 0.0668 | 192.96 | 2412 | 0.8193 | 0.775 | 0.3507 | 1.4508 | 0.775 | 0.7384 | 0.2025 | 0.1042 | | 0.0668 | 194.0 | 2425 | 0.8181 | 0.77 | 0.3503 | 1.4565 | 0.7700 | 0.7357 | 0.2090 | 0.1027 | | 0.0668 | 194.96 | 2437 | 0.8192 | 0.77 | 0.3507 | 1.4513 | 0.7700 | 0.7357 | 0.1953 | 0.1032 | | 0.0668 | 196.0 | 2450 | 0.8214 | 0.77 | 0.3520 | 1.4519 | 0.7700 | 0.7349 | 0.2112 | 0.1045 | | 0.0668 | 196.96 | 2462 | 0.8231 | 0.765 | 0.3531 | 1.4517 | 0.765 | 0.7323 | 0.2042 | 0.1049 | | 0.0668 | 198.0 | 2475 | 0.8219 | 0.77 | 0.3521 | 1.4512 | 0.7700 | 0.7349 | 0.2152 | 0.1044 | | 0.0668 | 198.96 | 2487 | 0.8223 | 0.77 | 0.3523 | 1.4507 | 0.7700 | 0.7349 | 0.1888 | 0.1050 | | 0.0571 | 200.0 | 2500 | 0.8235 | 0.77 | 0.3529 | 1.4533 | 0.7700 | 0.7349 | 0.2029 | 0.1050 | | 0.0571 | 200.96 | 2512 | 0.8227 | 0.77 | 0.3525 | 1.4718 | 0.7700 | 0.7357 | 0.2170 | 0.1033 | | 0.0571 | 202.0 | 2525 | 0.8226 | 0.77 | 0.3525 | 1.4505 | 0.7700 | 0.7349 | 0.1954 | 0.1041 | | 0.0571 | 202.96 | 2537 | 0.8231 | 0.765 | 0.3530 | 1.4506 | 0.765 | 0.7321 | 0.1962 | 0.1046 | | 0.0571 | 204.0 | 2550 | 0.8255 | 0.77 | 0.3535 | 1.4520 | 0.7700 | 0.7380 | 0.2078 | 0.1060 | | 0.0571 | 204.96 | 2562 | 0.8276 | 0.77 | 0.3550 | 1.4594 | 0.7700 | 0.7349 | 0.2013 | 0.1046 | | 0.0571 | 206.0 | 2575 | 0.8257 | 0.77 | 0.3542 | 1.4532 | 0.7700 | 0.7349 | 0.1987 | 0.1040 | | 0.0571 | 206.96 | 2587 | 0.8248 | 0.775 | 0.3536 | 1.4499 | 0.775 | 0.7406 | 0.1903 | 0.1043 | | 0.0571 | 208.0 | 2600 | 0.8250 | 0.77 | 0.3534 | 1.4537 | 0.7700 | 0.7349 | 0.2070 | 0.1040 | | 0.0571 | 208.96 | 2612 | 0.8277 | 0.77 | 0.3548 | 1.4521 | 0.7700 | 0.7380 | 0.1867 | 0.1058 | | 0.0571 | 210.0 | 2625 | 0.8271 | 0.77 | 0.3545 | 1.4543 | 0.7700 | 0.7349 | 0.2213 | 0.1036 | | 0.0571 | 210.96 | 2637 | 0.8284 | 0.775 | 0.3552 | 1.4516 | 0.775 | 0.7406 | 0.1992 | 0.1053 | | 0.0571 | 212.0 | 2650 | 0.8278 | 0.77 | 0.3545 | 1.4533 | 0.7700 | 0.7360 | 0.1938 | 0.1056 | | 0.0571 | 212.96 | 2662 | 0.8289 | 0.77 | 0.3552 | 1.4533 | 0.7700 | 0.7380 | 0.2017 | 0.1057 | | 0.0571 | 214.0 | 2675 | 0.8290 | 0.775 | 0.3556 | 1.4530 | 0.775 | 0.7406 | 0.2005 | 0.1052 | | 0.0571 | 214.96 | 2687 | 0.8282 | 0.77 | 0.3551 | 1.4517 | 0.7700 | 0.7379 | 0.1985 | 0.1037 | | 0.0571 | 216.0 | 2700 | 0.8294 | 0.77 | 0.3555 | 1.4588 | 0.7700 | 0.7349 | 0.1941 | 0.1045 | | 0.0571 | 216.96 | 2712 | 0.8305 | 0.775 | 0.3562 | 1.4516 | 0.775 | 0.7406 | 0.1977 | 0.1057 | | 0.0571 | 218.0 | 2725 | 0.8310 | 0.77 | 0.3565 | 1.4539 | 0.7700 | 0.7380 | 0.1926 | 0.1054 | | 0.0571 | 218.96 | 2737 | 0.8304 | 0.775 | 0.3560 | 1.4516 | 0.775 | 0.7406 | 0.1986 | 0.1054 | | 0.0571 | 220.0 | 2750 | 0.8320 | 0.775 | 0.3568 | 1.4545 | 0.775 | 0.7406 | 0.1953 | 0.1054 | | 0.0571 | 220.96 | 2762 | 0.8316 | 0.775 | 0.3569 | 1.4523 | 0.775 | 0.7406 | 0.1945 | 0.1045 | | 0.0571 | 222.0 | 2775 | 0.8330 | 0.77 | 0.3573 | 1.4547 | 0.7700 | 0.7380 | 0.1892 | 0.1067 | | 0.0571 | 222.96 | 2787 | 0.8309 | 0.77 | 0.3563 | 1.4548 | 0.7700 | 0.7379 | 0.2060 | 0.1033 | | 0.0571 | 224.0 | 2800 | 0.8323 | 0.775 | 0.3572 | 1.4515 | 0.775 | 0.7406 | 0.1910 | 0.1050 | | 0.0571 | 224.96 | 2812 | 0.8329 | 0.775 | 0.3569 | 1.4530 | 0.775 | 0.7406 | 0.1931 | 0.1055 | | 0.0571 | 226.0 | 2825 | 0.8319 | 0.78 | 0.3567 | 1.4513 | 0.78 | 0.7444 | 0.2038 | 0.1043 | | 0.0571 | 226.96 | 2837 | 0.8354 | 0.77 | 0.3586 | 1.4556 | 0.7700 | 0.7380 | 0.1969 | 0.1068 | | 0.0571 | 228.0 | 2850 | 0.8340 | 0.78 | 0.3575 | 1.4550 | 0.78 | 0.7444 | 0.2043 | 0.1062 | | 0.0571 | 228.96 | 2862 | 0.8355 | 0.775 | 0.3584 | 1.4546 | 0.775 | 0.7406 | 0.2048 | 0.1055 | | 0.0571 | 230.0 | 2875 | 0.8350 | 0.78 | 0.3579 | 1.4538 | 0.78 | 0.7444 | 0.2069 | 0.1064 | | 0.0571 | 230.96 | 2887 | 0.8358 | 0.77 | 0.3584 | 1.4550 | 0.7700 | 0.7380 | 0.1899 | 0.1061 | | 0.0571 | 232.0 | 2900 | 0.8366 | 0.77 | 0.3587 | 1.4564 | 0.7700 | 0.7380 | 0.1921 | 0.1070 | | 0.0571 | 232.96 | 2912 | 0.8364 | 0.775 | 0.3587 | 1.4557 | 0.775 | 0.7418 | 0.1970 | 0.1065 | | 0.0571 | 234.0 | 2925 | 0.8359 | 0.775 | 0.3585 | 1.4543 | 0.775 | 0.7406 | 0.1912 | 0.1061 | | 0.0571 | 234.96 | 2937 | 0.8360 | 0.775 | 0.3587 | 1.4540 | 0.775 | 0.7406 | 0.2017 | 0.1049 | | 0.0571 | 236.0 | 2950 | 0.8362 | 0.78 | 0.3587 | 1.4527 | 0.78 | 0.7444 | 0.1985 | 0.1060 | | 0.0571 | 236.96 | 2962 | 0.8375 | 0.78 | 0.3593 | 1.4554 | 0.78 | 0.7444 | 0.2035 | 0.1061 | | 0.0571 | 238.0 | 2975 | 0.8378 | 0.775 | 0.3593 | 1.4544 | 0.775 | 0.7418 | 0.1971 | 0.1068 | | 0.0571 | 238.96 | 2987 | 0.8369 | 0.78 | 0.3588 | 1.4557 | 0.78 | 0.7444 | 0.2178 | 0.1057 | | 0.0512 | 240.0 | 3000 | 0.8388 | 0.77 | 0.3600 | 1.4558 | 0.7700 | 0.7380 | 0.1939 | 0.1067 | | 0.0512 | 240.96 | 3012 | 0.8375 | 0.78 | 0.3593 | 1.4540 | 0.78 | 0.7444 | 0.2071 | 0.1058 | | 0.0512 | 242.0 | 3025 | 0.8393 | 0.775 | 0.3602 | 1.4546 | 0.775 | 0.7406 | 0.1990 | 0.1066 | | 0.0512 | 242.96 | 3037 | 0.8391 | 0.775 | 0.3601 | 1.4551 | 0.775 | 0.7406 | 0.2025 | 0.1063 | | 0.0512 | 244.0 | 3050 | 0.8414 | 0.77 | 0.3610 | 1.4575 | 0.7700 | 0.7380 | 0.1924 | 0.1072 | | 0.0512 | 244.96 | 3062 | 0.8385 | 0.78 | 0.3597 | 1.4531 | 0.78 | 0.7444 | 0.2062 | 0.1059 | | 0.0512 | 246.0 | 3075 | 0.8394 | 0.78 | 0.3603 | 1.4583 | 0.78 | 0.7444 | 0.1962 | 0.1057 | | 0.0512 | 246.96 | 3087 | 0.8401 | 0.775 | 0.3604 | 1.4535 | 0.775 | 0.7406 | 0.1880 | 0.1060 | | 0.0512 | 248.0 | 3100 | 0.8400 | 0.78 | 0.3605 | 1.4550 | 0.78 | 0.7444 | 0.2156 | 0.1058 | | 0.0512 | 248.96 | 3112 | 0.8404 | 0.78 | 0.3606 | 1.4554 | 0.78 | 0.7444 | 0.1977 | 0.1061 | | 0.0512 | 250.0 | 3125 | 0.8406 | 0.78 | 0.3607 | 1.4542 | 0.78 | 0.7444 | 0.2055 | 0.1062 | | 0.0512 | 250.96 | 3137 | 0.8408 | 0.78 | 0.3608 | 1.4545 | 0.78 | 0.7444 | 0.2036 | 0.1062 | | 0.0512 | 252.0 | 3150 | 0.8414 | 0.78 | 0.3611 | 1.4560 | 0.78 | 0.7444 | 0.2054 | 0.1063 | | 0.0512 | 252.96 | 3162 | 0.8424 | 0.775 | 0.3614 | 1.4580 | 0.775 | 0.7418 | 0.2037 | 0.1072 | | 0.0512 | 254.0 | 3175 | 0.8423 | 0.775 | 0.3616 | 1.4558 | 0.775 | 0.7406 | 0.2057 | 0.1064 | | 0.0512 | 254.96 | 3187 | 0.8422 | 0.775 | 0.3613 | 1.4562 | 0.775 | 0.7418 | 0.2070 | 0.1066 | | 0.0512 | 256.0 | 3200 | 0.8419 | 0.78 | 0.3612 | 1.4562 | 0.78 | 0.7444 | 0.2196 | 0.1063 | | 0.0512 | 256.96 | 3212 | 0.8434 | 0.775 | 0.3620 | 1.4565 | 0.775 | 0.7406 | 0.2033 | 0.1065 | | 0.0512 | 258.0 | 3225 | 0.8431 | 0.775 | 0.3619 | 1.4557 | 0.775 | 0.7418 | 0.2072 | 0.1064 | | 0.0512 | 258.96 | 3237 | 0.8435 | 0.77 | 0.3620 | 1.4567 | 0.7700 | 0.7380 | 0.1985 | 0.1066 | | 0.0512 | 260.0 | 3250 | 0.8433 | 0.78 | 0.3619 | 1.4567 | 0.78 | 0.7444 | 0.2179 | 0.1065 | | 0.0512 | 260.96 | 3262 | 0.8430 | 0.78 | 0.3619 | 1.4558 | 0.78 | 0.7444 | 0.2120 | 0.1060 | | 0.0512 | 262.0 | 3275 | 0.8432 | 0.78 | 0.3619 | 1.4552 | 0.78 | 0.7444 | 0.2058 | 0.1060 | | 0.0512 | 262.96 | 3287 | 0.8444 | 0.775 | 0.3623 | 1.4572 | 0.775 | 0.7418 | 0.2035 | 0.1068 | | 0.0512 | 264.0 | 3300 | 0.8442 | 0.775 | 0.3622 | 1.4574 | 0.775 | 0.7418 | 0.2054 | 0.1067 | | 0.0512 | 264.96 | 3312 | 0.8441 | 0.78 | 0.3623 | 1.4554 | 0.78 | 0.7444 | 0.2051 | 0.1062 | | 0.0512 | 266.0 | 3325 | 0.8446 | 0.775 | 0.3624 | 1.4561 | 0.775 | 0.7418 | 0.1975 | 0.1066 | | 0.0512 | 266.96 | 3337 | 0.8447 | 0.775 | 0.3624 | 1.4570 | 0.775 | 0.7418 | 0.2053 | 0.1065 | | 0.0512 | 268.0 | 3350 | 0.8448 | 0.78 | 0.3624 | 1.4573 | 0.78 | 0.7444 | 0.2085 | 0.1065 | | 0.0512 | 268.96 | 3362 | 0.8443 | 0.78 | 0.3624 | 1.4558 | 0.78 | 0.7444 | 0.2119 | 0.1065 | | 0.0512 | 270.0 | 3375 | 0.8453 | 0.775 | 0.3628 | 1.4571 | 0.775 | 0.7418 | 0.2035 | 0.1067 | | 0.0512 | 270.96 | 3387 | 0.8444 | 0.78 | 0.3623 | 1.4561 | 0.78 | 0.7444 | 0.2076 | 0.1063 | | 0.0512 | 272.0 | 3400 | 0.8455 | 0.775 | 0.3629 | 1.4569 | 0.775 | 0.7418 | 0.2034 | 0.1066 | | 0.0512 | 272.96 | 3412 | 0.8453 | 0.78 | 0.3628 | 1.4574 | 0.78 | 0.7444 | 0.2021 | 0.1065 | | 0.0512 | 274.0 | 3425 | 0.8450 | 0.78 | 0.3626 | 1.4560 | 0.78 | 0.7444 | 0.2058 | 0.1064 | | 0.0512 | 274.96 | 3437 | 0.8456 | 0.775 | 0.3629 | 1.4569 | 0.775 | 0.7418 | 0.2035 | 0.1066 | | 0.0512 | 276.0 | 3450 | 0.8454 | 0.775 | 0.3628 | 1.4565 | 0.775 | 0.7418 | 0.2033 | 0.1065 | | 0.0512 | 276.96 | 3462 | 0.8454 | 0.78 | 0.3628 | 1.4575 | 0.78 | 0.7444 | 0.2137 | 0.1063 | | 0.0512 | 278.0 | 3475 | 0.8457 | 0.78 | 0.3630 | 1.4567 | 0.78 | 0.7444 | 0.2092 | 0.1065 | | 0.0512 | 278.96 | 3487 | 0.8462 | 0.775 | 0.3632 | 1.4567 | 0.775 | 0.7418 | 0.1994 | 0.1067 | | 0.0481 | 280.0 | 3500 | 0.8456 | 0.78 | 0.3630 | 1.4572 | 0.78 | 0.7444 | 0.2192 | 0.1064 | | 0.0481 | 280.96 | 3512 | 0.8462 | 0.775 | 0.3632 | 1.4571 | 0.775 | 0.7418 | 0.2034 | 0.1066 | | 0.0481 | 282.0 | 3525 | 0.8457 | 0.775 | 0.3630 | 1.4563 | 0.775 | 0.7418 | 0.2042 | 0.1065 | | 0.0481 | 282.96 | 3537 | 0.8460 | 0.775 | 0.3631 | 1.4570 | 0.775 | 0.7418 | 0.2106 | 0.1066 | | 0.0481 | 284.0 | 3550 | 0.8462 | 0.775 | 0.3632 | 1.4570 | 0.775 | 0.7418 | 0.2106 | 0.1067 | | 0.0481 | 284.96 | 3562 | 0.8460 | 0.775 | 0.3631 | 1.4567 | 0.775 | 0.7418 | 0.2042 | 0.1065 | | 0.0481 | 286.0 | 3575 | 0.8461 | 0.775 | 0.3632 | 1.4568 | 0.775 | 0.7418 | 0.2043 | 0.1066 | | 0.0481 | 286.96 | 3587 | 0.8461 | 0.775 | 0.3632 | 1.4570 | 0.775 | 0.7418 | 0.2043 | 0.1066 | | 0.0481 | 288.0 | 3600 | 0.8461 | 0.775 | 0.3632 | 1.4570 | 0.775 | 0.7418 | 0.2043 | 0.1066 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/vit-base_tobacco_lr1e-5_wr_0.05_wd_0.1

# vit-base_tobacco_lr1e-5_wr_0.05_wd_0.1 This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9592 - Accuracy: 0.775 - Brier Loss: 0.3981 - Nll: 1.5416 - F1 Micro: 0.775 - F1 Macro: 0.7418 - Ece: 0.2227 - Aurc: 0.1082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.7440 | 0.815 | 0.3076 | 1.1842 | 0.815 | 0.7942 | 0.2216 | 0.0733 | | No log | 2.0 | 25 | 0.7436 | 0.82 | 0.3075 | 1.1869 | 0.82 | 0.8049 | 0.2132 | 0.0741 | | No log | 2.96 | 37 | 0.7454 | 0.81 | 0.3085 | 1.1880 | 0.81 | 0.7914 | 0.2312 | 0.0755 | | No log | 4.0 | 50 | 0.7439 | 0.815 | 0.3077 | 1.1846 | 0.815 | 0.7926 | 0.2369 | 0.0760 | | No log | 4.96 | 62 | 0.7370 | 0.82 | 0.3040 | 1.1982 | 0.82 | 0.8028 | 0.2374 | 0.0745 | | No log | 6.0 | 75 | 0.7507 | 0.82 | 0.3112 | 1.1980 | 0.82 | 0.8005 | 0.2513 | 0.0809 | | No log | 6.96 | 87 | 0.7370 | 0.805 | 0.3060 | 1.1778 | 0.805 | 0.7841 | 0.2522 | 0.0746 | | No log | 8.0 | 100 | 0.7437 | 0.81 | 0.3076 | 1.1846 | 0.81 | 0.7877 | 0.2301 | 0.0804 | | No log | 8.96 | 112 | 0.7311 | 0.81 | 0.3031 | 1.1975 | 0.81 | 0.7920 | 0.2084 | 0.0753 | | No log | 10.0 | 125 | 0.7305 | 0.8 | 0.3020 | 1.1785 | 0.8000 | 0.7792 | 0.2131 | 0.0777 | | No log | 10.96 | 137 | 0.7478 | 0.805 | 0.3119 | 1.3979 | 0.805 | 0.7860 | 0.2133 | 0.0827 | | No log | 12.0 | 150 | 0.7469 | 0.805 | 0.3082 | 1.3337 | 0.805 | 0.7844 | 0.2213 | 0.0843 | | No log | 12.96 | 162 | 0.7545 | 0.805 | 0.3114 | 1.4280 | 0.805 | 0.7893 | 0.2092 | 0.0935 | | No log | 14.0 | 175 | 0.7283 | 0.795 | 0.3012 | 1.1856 | 0.795 | 0.7739 | 0.2182 | 0.0806 | | No log | 14.96 | 187 | 0.7219 | 0.815 | 0.2972 | 1.2792 | 0.815 | 0.8043 | 0.2024 | 0.0734 | | No log | 16.0 | 200 | 0.7284 | 0.805 | 0.3001 | 1.2528 | 0.805 | 0.7899 | 0.2068 | 0.0858 | | No log | 16.96 | 212 | 0.7191 | 0.805 | 0.2981 | 1.3067 | 0.805 | 0.7919 | 0.2062 | 0.0809 | | No log | 18.0 | 225 | 0.7221 | 0.8 | 0.3011 | 1.1747 | 0.8000 | 0.7792 | 0.2091 | 0.0803 | | No log | 18.96 | 237 | 0.7253 | 0.81 | 0.2995 | 1.3143 | 0.81 | 0.7955 | 0.2136 | 0.0889 | | No log | 20.0 | 250 | 0.7186 | 0.8 | 0.2981 | 1.1839 | 0.8000 | 0.7819 | 0.1899 | 0.0812 | | No log | 20.96 | 262 | 0.7247 | 0.805 | 0.3012 | 1.2501 | 0.805 | 0.7925 | 0.2214 | 0.0891 | | No log | 22.0 | 275 | 0.7317 | 0.805 | 0.3058 | 1.3767 | 0.805 | 0.7853 | 0.2141 | 0.0893 | | No log | 22.96 | 287 | 0.7250 | 0.81 | 0.3031 | 1.3683 | 0.81 | 0.7907 | 0.1886 | 0.0838 | | No log | 24.0 | 300 | 0.7137 | 0.805 | 0.2983 | 1.3088 | 0.805 | 0.7851 | 0.1799 | 0.0782 | | No log | 24.96 | 312 | 0.7334 | 0.81 | 0.3070 | 1.4296 | 0.81 | 0.7909 | 0.1903 | 0.0898 | | No log | 26.0 | 325 | 0.7284 | 0.81 | 0.3035 | 1.2467 | 0.81 | 0.7984 | 0.2152 | 0.0916 | | No log | 26.96 | 337 | 0.7242 | 0.805 | 0.3020 | 1.3077 | 0.805 | 0.7862 | 0.2071 | 0.0891 | | No log | 28.0 | 350 | 0.7285 | 0.81 | 0.3028 | 1.3756 | 0.81 | 0.7910 | 0.2158 | 0.0915 | | No log | 28.96 | 362 | 0.7253 | 0.8 | 0.3016 | 1.3714 | 0.8000 | 0.7716 | 0.2057 | 0.0894 | | No log | 30.0 | 375 | 0.7321 | 0.8 | 0.3068 | 1.3688 | 0.8000 | 0.7736 | 0.1943 | 0.0885 | | No log | 30.96 | 387 | 0.7294 | 0.8 | 0.3047 | 1.3713 | 0.8000 | 0.7746 | 0.2138 | 0.0900 | | No log | 32.0 | 400 | 0.7296 | 0.81 | 0.3054 | 1.3749 | 0.81 | 0.7921 | 0.2074 | 0.0910 | | No log | 32.96 | 412 | 0.7311 | 0.805 | 0.3061 | 1.3704 | 0.805 | 0.7811 | 0.1984 | 0.0920 | | No log | 34.0 | 425 | 0.7291 | 0.805 | 0.3049 | 1.3686 | 0.805 | 0.7811 | 0.2126 | 0.0916 | | No log | 34.96 | 437 | 0.7301 | 0.795 | 0.3048 | 1.3712 | 0.795 | 0.7654 | 0.1917 | 0.0904 | | No log | 36.0 | 450 | 0.7318 | 0.81 | 0.3072 | 1.3695 | 0.81 | 0.7844 | 0.1976 | 0.0900 | | No log | 36.96 | 462 | 0.7403 | 0.795 | 0.3102 | 1.3712 | 0.795 | 0.7656 | 0.2039 | 0.0934 | | No log | 38.0 | 475 | 0.7376 | 0.795 | 0.3095 | 1.3653 | 0.795 | 0.7654 | 0.1982 | 0.0920 | | No log | 38.96 | 487 | 0.7326 | 0.805 | 0.3049 | 1.3815 | 0.805 | 0.7744 | 0.1820 | 0.0948 | | 0.1331 | 40.0 | 500 | 0.7268 | 0.8 | 0.3038 | 1.3702 | 0.8000 | 0.7704 | 0.2051 | 0.0899 | | 0.1331 | 40.96 | 512 | 0.7371 | 0.8 | 0.3074 | 1.3824 | 0.8000 | 0.7684 | 0.1946 | 0.0939 | | 0.1331 | 42.0 | 525 | 0.7374 | 0.81 | 0.3107 | 1.3600 | 0.81 | 0.7844 | 0.2109 | 0.0910 | | 0.1331 | 42.96 | 537 | 0.7366 | 0.8 | 0.3071 | 1.4434 | 0.8000 | 0.7776 | 0.2042 | 0.0935 | | 0.1331 | 44.0 | 550 | 0.7362 | 0.805 | 0.3083 | 1.3721 | 0.805 | 0.7829 | 0.1782 | 0.0929 | | 0.1331 | 44.96 | 562 | 0.7389 | 0.8 | 0.3110 | 1.3695 | 0.8000 | 0.7704 | 0.1966 | 0.0917 | | 0.1331 | 46.0 | 575 | 0.7426 | 0.79 | 0.3108 | 1.5068 | 0.79 | 0.7644 | 0.1938 | 0.0968 | | 0.1331 | 46.96 | 587 | 0.7395 | 0.8 | 0.3096 | 1.3760 | 0.8000 | 0.7704 | 0.1951 | 0.0927 | | 0.1331 | 48.0 | 600 | 0.7540 | 0.805 | 0.3185 | 1.4936 | 0.805 | 0.7821 | 0.1958 | 0.0979 | | 0.1331 | 48.96 | 612 | 0.7413 | 0.805 | 0.3116 | 1.4368 | 0.805 | 0.7829 | 0.1835 | 0.0955 | | 0.1331 | 50.0 | 625 | 0.7543 | 0.805 | 0.3167 | 1.4402 | 0.805 | 0.7831 | 0.2143 | 0.0974 | | 0.1331 | 50.96 | 637 | 0.7378 | 0.805 | 0.3087 | 1.3850 | 0.805 | 0.7829 | 0.1886 | 0.0935 | | 0.1331 | 52.0 | 650 | 0.7545 | 0.795 | 0.3175 | 1.3873 | 0.795 | 0.7656 | 0.2007 | 0.0957 | | 0.1331 | 52.96 | 662 | 0.7464 | 0.8 | 0.3140 | 1.3734 | 0.8000 | 0.7707 | 0.1872 | 0.0938 | | 0.1331 | 54.0 | 675 | 0.7439 | 0.8 | 0.3120 | 1.3765 | 0.8000 | 0.7704 | 0.2036 | 0.0942 | | 0.1331 | 54.96 | 687 | 0.7506 | 0.8 | 0.3150 | 1.3788 | 0.8000 | 0.7707 | 0.1788 | 0.0959 | | 0.1331 | 56.0 | 700 | 0.7511 | 0.805 | 0.3158 | 1.4378 | 0.805 | 0.7829 | 0.2054 | 0.0955 | | 0.1331 | 56.96 | 712 | 0.7587 | 0.805 | 0.3196 | 1.4494 | 0.805 | 0.7831 | 0.1844 | 0.0972 | | 0.1331 | 58.0 | 725 | 0.7505 | 0.8 | 0.3154 | 1.3759 | 0.8000 | 0.7704 | 0.1913 | 0.0953 | | 0.1331 | 58.96 | 737 | 0.7553 | 0.79 | 0.3167 | 1.4457 | 0.79 | 0.7549 | 0.1977 | 0.0959 | | 0.1331 | 60.0 | 750 | 0.7543 | 0.8 | 0.3175 | 1.3807 | 0.8000 | 0.7707 | 0.1963 | 0.0953 | | 0.1331 | 60.96 | 762 | 0.7592 | 0.795 | 0.3200 | 1.3759 | 0.795 | 0.7681 | 0.1986 | 0.0961 | | 0.1331 | 62.0 | 775 | 0.7557 | 0.795 | 0.3185 | 1.3785 | 0.795 | 0.7634 | 0.1971 | 0.0948 | | 0.1331 | 62.96 | 787 | 0.7591 | 0.79 | 0.3200 | 1.4466 | 0.79 | 0.7613 | 0.2033 | 0.0963 | | 0.1331 | 64.0 | 800 | 0.7624 | 0.795 | 0.3210 | 1.4423 | 0.795 | 0.7621 | 0.2030 | 0.0962 | | 0.1331 | 64.96 | 812 | 0.7674 | 0.79 | 0.3240 | 1.4454 | 0.79 | 0.7596 | 0.1973 | 0.0969 | | 0.1331 | 66.0 | 825 | 0.7645 | 0.79 | 0.3224 | 1.4497 | 0.79 | 0.7611 | 0.1999 | 0.0964 | | 0.1331 | 66.96 | 837 | 0.7652 | 0.795 | 0.3234 | 1.4418 | 0.795 | 0.7668 | 0.1819 | 0.0968 | | 0.1331 | 68.0 | 850 | 0.7695 | 0.795 | 0.3250 | 1.4969 | 0.795 | 0.7606 | 0.1914 | 0.0979 | | 0.1331 | 68.96 | 862 | 0.7708 | 0.785 | 0.3258 | 1.4482 | 0.785 | 0.7516 | 0.1954 | 0.0976 | | 0.1331 | 70.0 | 875 | 0.7691 | 0.795 | 0.3249 | 1.4960 | 0.795 | 0.7673 | 0.1895 | 0.0976 | | 0.1331 | 70.96 | 887 | 0.7741 | 0.785 | 0.3272 | 1.5043 | 0.785 | 0.7519 | 0.1898 | 0.0982 | | 0.1331 | 72.0 | 900 | 0.7788 | 0.79 | 0.3293 | 1.5094 | 0.79 | 0.7611 | 0.1738 | 0.0995 | | 0.1331 | 72.96 | 912 | 0.7837 | 0.785 | 0.3329 | 1.5306 | 0.785 | 0.7577 | 0.2002 | 0.1004 | | 0.1331 | 74.0 | 925 | 0.7755 | 0.785 | 0.3280 | 1.4985 | 0.785 | 0.7514 | 0.1906 | 0.0981 | | 0.1331 | 74.96 | 937 | 0.7797 | 0.785 | 0.3308 | 1.4611 | 0.785 | 0.7580 | 0.1925 | 0.0994 | | 0.1331 | 76.0 | 950 | 0.7744 | 0.785 | 0.3273 | 1.4441 | 0.785 | 0.7519 | 0.1929 | 0.0976 | | 0.1331 | 76.96 | 962 | 0.7766 | 0.785 | 0.3295 | 1.4420 | 0.785 | 0.7516 | 0.1899 | 0.0972 | | 0.1331 | 78.0 | 975 | 0.7888 | 0.785 | 0.3339 | 1.4991 | 0.785 | 0.7573 | 0.1879 | 0.0998 | | 0.1331 | 78.96 | 987 | 0.7765 | 0.795 | 0.3292 | 1.4915 | 0.795 | 0.7663 | 0.1750 | 0.0948 | | 0.071 | 80.0 | 1000 | 0.7821 | 0.785 | 0.3303 | 1.4990 | 0.785 | 0.7519 | 0.1940 | 0.0986 | | 0.071 | 80.96 | 1012 | 0.7860 | 0.79 | 0.3330 | 1.4977 | 0.79 | 0.7644 | 0.1698 | 0.0976 | | 0.071 | 82.0 | 1025 | 0.7882 | 0.78 | 0.3342 | 1.5243 | 0.78 | 0.7482 | 0.1930 | 0.1006 | | 0.071 | 82.96 | 1037 | 0.7879 | 0.78 | 0.3333 | 1.5037 | 0.78 | 0.7491 | 0.2055 | 0.0995 | | 0.071 | 84.0 | 1050 | 0.7842 | 0.78 | 0.3326 | 1.4959 | 0.78 | 0.7488 | 0.1945 | 0.0985 | | 0.071 | 84.96 | 1062 | 0.7866 | 0.78 | 0.3338 | 1.4961 | 0.78 | 0.7488 | 0.1877 | 0.0982 | | 0.071 | 86.0 | 1075 | 0.7931 | 0.785 | 0.3369 | 1.5006 | 0.785 | 0.7573 | 0.1898 | 0.1003 | | 0.071 | 86.96 | 1087 | 0.7937 | 0.78 | 0.3360 | 1.5043 | 0.78 | 0.7488 | 0.1828 | 0.0999 | | 0.071 | 88.0 | 1100 | 0.7948 | 0.78 | 0.3374 | 1.5034 | 0.78 | 0.7488 | 0.1893 | 0.0999 | | 0.071 | 88.96 | 1112 | 0.7962 | 0.78 | 0.3372 | 1.5078 | 0.78 | 0.7494 | 0.1943 | 0.1011 | | 0.071 | 90.0 | 1125 | 0.7956 | 0.785 | 0.3377 | 1.5039 | 0.785 | 0.7516 | 0.1918 | 0.0999 | | 0.071 | 90.96 | 1137 | 0.7996 | 0.78 | 0.3382 | 1.5060 | 0.78 | 0.7491 | 0.1982 | 0.1013 | | 0.071 | 92.0 | 1150 | 0.7980 | 0.78 | 0.3381 | 1.5023 | 0.78 | 0.7488 | 0.1902 | 0.1004 | | 0.071 | 92.96 | 1162 | 0.8015 | 0.78 | 0.3396 | 1.5029 | 0.78 | 0.7488 | 0.1978 | 0.1007 | | 0.071 | 94.0 | 1175 | 0.8044 | 0.78 | 0.3411 | 1.5047 | 0.78 | 0.7488 | 0.1929 | 0.1012 | | 0.071 | 94.96 | 1187 | 0.7977 | 0.78 | 0.3392 | 1.4989 | 0.78 | 0.7488 | 0.1866 | 0.0989 | | 0.071 | 96.0 | 1200 | 0.8071 | 0.78 | 0.3425 | 1.5021 | 0.78 | 0.7488 | 0.1941 | 0.1018 | | 0.071 | 96.96 | 1212 | 0.8033 | 0.78 | 0.3406 | 1.4967 | 0.78 | 0.7488 | 0.1913 | 0.1000 | | 0.071 | 98.0 | 1225 | 0.8148 | 0.775 | 0.3466 | 1.4555 | 0.775 | 0.7462 | 0.1828 | 0.1036 | | 0.071 | 98.96 | 1237 | 0.8062 | 0.78 | 0.3417 | 1.5007 | 0.78 | 0.7488 | 0.1949 | 0.1004 | | 0.071 | 100.0 | 1250 | 0.8123 | 0.77 | 0.3456 | 1.5069 | 0.7700 | 0.7424 | 0.1964 | 0.1020 | | 0.071 | 100.96 | 1262 | 0.8117 | 0.78 | 0.3452 | 1.5048 | 0.78 | 0.7488 | 0.2081 | 0.1020 | | 0.071 | 102.0 | 1275 | 0.8125 | 0.77 | 0.3454 | 1.5066 | 0.7700 | 0.7424 | 0.2040 | 0.1022 | | 0.071 | 102.96 | 1287 | 0.8134 | 0.775 | 0.3458 | 1.5048 | 0.775 | 0.7450 | 0.1977 | 0.1013 | | 0.071 | 104.0 | 1300 | 0.8152 | 0.78 | 0.3461 | 1.5027 | 0.78 | 0.7488 | 0.2044 | 0.1014 | | 0.071 | 104.96 | 1312 | 0.8185 | 0.78 | 0.3478 | 1.5057 | 0.78 | 0.7488 | 0.1900 | 0.1022 | | 0.071 | 106.0 | 1325 | 0.8191 | 0.78 | 0.3480 | 1.5053 | 0.78 | 0.7488 | 0.2084 | 0.1026 | | 0.071 | 106.96 | 1337 | 0.8207 | 0.77 | 0.3497 | 1.5095 | 0.7700 | 0.7424 | 0.1984 | 0.1025 | | 0.071 | 108.0 | 1350 | 0.8221 | 0.77 | 0.3487 | 1.5095 | 0.7700 | 0.7424 | 0.1871 | 0.1031 | | 0.071 | 108.96 | 1362 | 0.8229 | 0.765 | 0.3501 | 1.4607 | 0.765 | 0.7331 | 0.1920 | 0.1028 | | 0.071 | 110.0 | 1375 | 0.8232 | 0.78 | 0.3498 | 1.5044 | 0.78 | 0.7488 | 0.1995 | 0.1023 | | 0.071 | 110.96 | 1387 | 0.8279 | 0.785 | 0.3513 | 1.5060 | 0.785 | 0.7526 | 0.2073 | 0.1033 | | 0.071 | 112.0 | 1400 | 0.8246 | 0.775 | 0.3505 | 1.5038 | 0.775 | 0.7450 | 0.1927 | 0.1018 | | 0.071 | 112.96 | 1412 | 0.8308 | 0.765 | 0.3537 | 1.5095 | 0.765 | 0.7331 | 0.1931 | 0.1035 | | 0.071 | 114.0 | 1425 | 0.8277 | 0.775 | 0.3513 | 1.5058 | 0.775 | 0.7395 | 0.1977 | 0.1022 | | 0.071 | 114.96 | 1437 | 0.8302 | 0.76 | 0.3531 | 1.4583 | 0.76 | 0.7296 | 0.2112 | 0.1028 | | 0.071 | 116.0 | 1450 | 0.8328 | 0.765 | 0.3535 | 1.5125 | 0.765 | 0.7331 | 0.2008 | 0.1037 | | 0.071 | 116.96 | 1462 | 0.8309 | 0.76 | 0.3533 | 1.4542 | 0.76 | 0.7296 | 0.2037 | 0.1029 | | 0.071 | 118.0 | 1475 | 0.8378 | 0.765 | 0.3558 | 1.5162 | 0.765 | 0.7323 | 0.2040 | 0.1055 | | 0.071 | 118.96 | 1487 | 0.8341 | 0.76 | 0.3547 | 1.5076 | 0.76 | 0.7296 | 0.1942 | 0.1032 | | 0.0462 | 120.0 | 1500 | 0.8367 | 0.76 | 0.3557 | 1.5134 | 0.76 | 0.7296 | 0.1987 | 0.1034 | | 0.0462 | 120.96 | 1512 | 0.8369 | 0.76 | 0.3553 | 1.5081 | 0.76 | 0.7296 | 0.2121 | 0.1036 | | 0.0462 | 122.0 | 1525 | 0.8385 | 0.77 | 0.3560 | 1.5076 | 0.7700 | 0.7357 | 0.1944 | 0.1034 | | 0.0462 | 122.96 | 1537 | 0.8415 | 0.76 | 0.3577 | 1.5127 | 0.76 | 0.7296 | 0.2080 | 0.1040 | | 0.0462 | 124.0 | 1550 | 0.8418 | 0.765 | 0.3571 | 1.5123 | 0.765 | 0.7333 | 0.1905 | 0.1043 | | 0.0462 | 124.96 | 1562 | 0.8431 | 0.76 | 0.3581 | 1.5124 | 0.76 | 0.7296 | 0.2029 | 0.1043 | | 0.0462 | 126.0 | 1575 | 0.8461 | 0.765 | 0.3595 | 1.5115 | 0.765 | 0.7331 | 0.1861 | 0.1044 | | 0.0462 | 126.96 | 1587 | 0.8446 | 0.76 | 0.3586 | 1.5117 | 0.76 | 0.7296 | 0.1962 | 0.1043 | | 0.0462 | 128.0 | 1600 | 0.8448 | 0.765 | 0.3585 | 1.5106 | 0.765 | 0.7333 | 0.1899 | 0.1048 | | 0.0462 | 128.96 | 1612 | 0.8503 | 0.765 | 0.3611 | 1.5156 | 0.765 | 0.7323 | 0.1865 | 0.1050 | | 0.0462 | 130.0 | 1625 | 0.8473 | 0.765 | 0.3597 | 1.5082 | 0.765 | 0.7333 | 0.1992 | 0.1040 | | 0.0462 | 130.96 | 1637 | 0.8530 | 0.76 | 0.3617 | 1.5178 | 0.76 | 0.7296 | 0.2008 | 0.1053 | | 0.0462 | 132.0 | 1650 | 0.8499 | 0.765 | 0.3608 | 1.5105 | 0.765 | 0.7321 | 0.1910 | 0.1035 | | 0.0462 | 132.96 | 1662 | 0.8529 | 0.765 | 0.3612 | 1.5095 | 0.765 | 0.7333 | 0.1943 | 0.1043 | | 0.0462 | 134.0 | 1675 | 0.8547 | 0.765 | 0.3635 | 1.5095 | 0.765 | 0.7321 | 0.2002 | 0.1032 | | 0.0462 | 134.96 | 1687 | 0.8572 | 0.765 | 0.3638 | 1.5159 | 0.765 | 0.7333 | 0.1979 | 0.1056 | | 0.0462 | 136.0 | 1700 | 0.8582 | 0.765 | 0.3642 | 1.5165 | 0.765 | 0.7333 | 0.2026 | 0.1057 | | 0.0462 | 136.96 | 1712 | 0.8581 | 0.76 | 0.3639 | 1.5118 | 0.76 | 0.7296 | 0.1965 | 0.1052 | | 0.0462 | 138.0 | 1725 | 0.8570 | 0.77 | 0.3629 | 1.5094 | 0.7700 | 0.7358 | 0.1870 | 0.1029 | | 0.0462 | 138.96 | 1737 | 0.8611 | 0.76 | 0.3650 | 1.5129 | 0.76 | 0.7296 | 0.1919 | 0.1040 | | 0.0462 | 140.0 | 1750 | 0.8618 | 0.76 | 0.3659 | 1.5131 | 0.76 | 0.7296 | 0.1981 | 0.1042 | | 0.0462 | 140.96 | 1762 | 0.8605 | 0.765 | 0.3652 | 1.5115 | 0.765 | 0.7333 | 0.1875 | 0.1048 | | 0.0462 | 142.0 | 1775 | 0.8647 | 0.76 | 0.3666 | 1.5157 | 0.76 | 0.7296 | 0.2002 | 0.1052 | | 0.0462 | 142.96 | 1787 | 0.8618 | 0.76 | 0.3654 | 1.5116 | 0.76 | 0.7296 | 0.2006 | 0.1045 | | 0.0462 | 144.0 | 1800 | 0.8672 | 0.765 | 0.3672 | 1.5160 | 0.765 | 0.7333 | 0.1979 | 0.1053 | | 0.0462 | 144.96 | 1812 | 0.8625 | 0.77 | 0.3648 | 1.5080 | 0.7700 | 0.7358 | 0.1975 | 0.1026 | | 0.0462 | 146.0 | 1825 | 0.8695 | 0.765 | 0.3679 | 1.5169 | 0.765 | 0.7323 | 0.1973 | 0.1051 | | 0.0462 | 146.96 | 1837 | 0.8696 | 0.76 | 0.3685 | 1.5132 | 0.76 | 0.7296 | 0.1936 | 0.1037 | | 0.0462 | 148.0 | 1850 | 0.8678 | 0.765 | 0.3671 | 1.5110 | 0.765 | 0.7333 | 0.2008 | 0.1040 | | 0.0462 | 148.96 | 1862 | 0.8713 | 0.765 | 0.3690 | 1.5152 | 0.765 | 0.7333 | 0.1983 | 0.1050 | | 0.0462 | 150.0 | 1875 | 0.8716 | 0.765 | 0.3687 | 1.5163 | 0.765 | 0.7323 | 0.2029 | 0.1051 | | 0.0462 | 150.96 | 1887 | 0.8724 | 0.77 | 0.3691 | 1.5113 | 0.7700 | 0.7358 | 0.1997 | 0.1037 | | 0.0462 | 152.0 | 1900 | 0.8729 | 0.765 | 0.3695 | 1.5134 | 0.765 | 0.7333 | 0.1966 | 0.1050 | | 0.0462 | 152.96 | 1912 | 0.8760 | 0.765 | 0.3706 | 1.5131 | 0.765 | 0.7333 | 0.2046 | 0.1040 | | 0.0462 | 154.0 | 1925 | 0.8761 | 0.765 | 0.3707 | 1.5138 | 0.765 | 0.7333 | 0.1896 | 0.1037 | | 0.0462 | 154.96 | 1937 | 0.8778 | 0.765 | 0.3711 | 1.5138 | 0.765 | 0.7333 | 0.2012 | 0.1046 | | 0.0462 | 156.0 | 1950 | 0.8768 | 0.765 | 0.3712 | 1.5125 | 0.765 | 0.7333 | 0.1891 | 0.1041 | | 0.0462 | 156.96 | 1962 | 0.8816 | 0.77 | 0.3732 | 1.5205 | 0.7700 | 0.7360 | 0.1993 | 0.1067 | | 0.0462 | 158.0 | 1975 | 0.8793 | 0.765 | 0.3718 | 1.5157 | 0.765 | 0.7333 | 0.2025 | 0.1049 | | 0.0462 | 158.96 | 1987 | 0.8788 | 0.77 | 0.3713 | 1.5126 | 0.7700 | 0.7358 | 0.2044 | 0.1039 | | 0.0335 | 160.0 | 2000 | 0.8851 | 0.77 | 0.3739 | 1.5193 | 0.7700 | 0.7360 | 0.2042 | 0.1069 | | 0.0335 | 160.96 | 2012 | 0.8872 | 0.77 | 0.3748 | 1.5200 | 0.7700 | 0.7360 | 0.2009 | 0.1057 | | 0.0335 | 162.0 | 2025 | 0.8827 | 0.765 | 0.3731 | 1.5144 | 0.765 | 0.7333 | 0.1897 | 0.1050 | | 0.0335 | 162.96 | 2037 | 0.8821 | 0.765 | 0.3724 | 1.5129 | 0.765 | 0.7333 | 0.1971 | 0.1042 | | 0.0335 | 164.0 | 2050 | 0.8919 | 0.77 | 0.3770 | 1.5229 | 0.7700 | 0.7360 | 0.2119 | 0.1061 | | 0.0335 | 164.96 | 2062 | 0.8907 | 0.765 | 0.3764 | 1.5240 | 0.765 | 0.7323 | 0.2125 | 0.1069 | | 0.0335 | 166.0 | 2075 | 0.8857 | 0.765 | 0.3743 | 1.5127 | 0.765 | 0.7333 | 0.1906 | 0.1044 | | 0.0335 | 166.96 | 2087 | 0.8928 | 0.77 | 0.3771 | 1.5253 | 0.7700 | 0.7360 | 0.2062 | 0.1062 | | 0.0335 | 168.0 | 2100 | 0.8895 | 0.77 | 0.3750 | 1.5179 | 0.7700 | 0.7360 | 0.2062 | 0.1054 | | 0.0335 | 168.96 | 2112 | 0.8904 | 0.77 | 0.3754 | 1.5178 | 0.7700 | 0.7360 | 0.2048 | 0.1055 | | 0.0335 | 170.0 | 2125 | 0.8919 | 0.765 | 0.3766 | 1.5137 | 0.765 | 0.7333 | 0.2170 | 0.1044 | | 0.0335 | 170.96 | 2137 | 0.8949 | 0.77 | 0.3779 | 1.5203 | 0.7700 | 0.7360 | 0.2042 | 0.1069 | | 0.0335 | 172.0 | 2150 | 0.8949 | 0.77 | 0.3779 | 1.5204 | 0.7700 | 0.7360 | 0.2078 | 0.1069 | | 0.0335 | 172.96 | 2162 | 0.8986 | 0.765 | 0.3794 | 1.5241 | 0.765 | 0.7310 | 0.2079 | 0.1072 | | 0.0335 | 174.0 | 2175 | 0.8978 | 0.76 | 0.3787 | 1.5201 | 0.76 | 0.7272 | 0.2108 | 0.1056 | | 0.0335 | 174.96 | 2187 | 0.8990 | 0.77 | 0.3786 | 1.5198 | 0.7700 | 0.7360 | 0.2032 | 0.1053 | | 0.0335 | 176.0 | 2200 | 0.9003 | 0.77 | 0.3794 | 1.5206 | 0.7700 | 0.7360 | 0.1996 | 0.1060 | | 0.0335 | 176.96 | 2212 | 0.9000 | 0.77 | 0.3797 | 1.5196 | 0.7700 | 0.7360 | 0.2116 | 0.1063 | | 0.0335 | 178.0 | 2225 | 0.9000 | 0.765 | 0.3794 | 1.5178 | 0.765 | 0.7333 | 0.1875 | 0.1055 | | 0.0335 | 178.96 | 2237 | 0.9034 | 0.77 | 0.3804 | 1.5218 | 0.7700 | 0.7360 | 0.1964 | 0.1068 | | 0.0335 | 180.0 | 2250 | 0.9020 | 0.77 | 0.3802 | 1.5198 | 0.7700 | 0.7360 | 0.2058 | 0.1063 | | 0.0335 | 180.96 | 2262 | 0.9037 | 0.77 | 0.3808 | 1.5192 | 0.7700 | 0.7360 | 0.1976 | 0.1063 | | 0.0335 | 182.0 | 2275 | 0.9059 | 0.77 | 0.3812 | 1.5227 | 0.7700 | 0.7360 | 0.1962 | 0.1067 | | 0.0335 | 182.96 | 2287 | 0.9063 | 0.77 | 0.3818 | 1.5206 | 0.7700 | 0.7360 | 0.2000 | 0.1065 | | 0.0335 | 184.0 | 2300 | 0.9058 | 0.77 | 0.3814 | 1.5196 | 0.7700 | 0.7360 | 0.1926 | 0.1061 | | 0.0335 | 184.96 | 2312 | 0.9082 | 0.77 | 0.3821 | 1.5211 | 0.7700 | 0.7360 | 0.2001 | 0.1067 | | 0.0335 | 186.0 | 2325 | 0.9083 | 0.77 | 0.3824 | 1.5204 | 0.7700 | 0.7360 | 0.2062 | 0.1057 | | 0.0335 | 186.96 | 2337 | 0.9090 | 0.77 | 0.3824 | 1.5220 | 0.7700 | 0.7360 | 0.2027 | 0.1063 | | 0.0335 | 188.0 | 2350 | 0.9106 | 0.77 | 0.3828 | 1.5213 | 0.7700 | 0.7360 | 0.1968 | 0.1068 | | 0.0335 | 188.96 | 2362 | 0.9116 | 0.77 | 0.3829 | 1.5238 | 0.7700 | 0.7360 | 0.2029 | 0.1071 | | 0.0335 | 190.0 | 2375 | 0.9120 | 0.77 | 0.3835 | 1.5225 | 0.7700 | 0.7360 | 0.1953 | 0.1064 | | 0.0335 | 190.96 | 2387 | 0.9123 | 0.77 | 0.3835 | 1.5227 | 0.7700 | 0.7360 | 0.2080 | 0.1069 | | 0.0335 | 192.0 | 2400 | 0.9131 | 0.775 | 0.3838 | 1.5222 | 0.775 | 0.7418 | 0.2039 | 0.1061 | | 0.0335 | 192.96 | 2412 | 0.9144 | 0.765 | 0.3841 | 1.5200 | 0.765 | 0.7333 | 0.2163 | 0.1060 | | 0.0335 | 194.0 | 2425 | 0.9138 | 0.77 | 0.3839 | 1.5200 | 0.7700 | 0.7360 | 0.2092 | 0.1057 | | 0.0335 | 194.96 | 2437 | 0.9164 | 0.775 | 0.3850 | 1.5249 | 0.775 | 0.7418 | 0.2188 | 0.1065 | | 0.0335 | 196.0 | 2450 | 0.9185 | 0.77 | 0.3861 | 1.5257 | 0.7700 | 0.7360 | 0.2087 | 0.1067 | | 0.0335 | 196.96 | 2462 | 0.9207 | 0.77 | 0.3868 | 1.5286 | 0.7700 | 0.7360 | 0.2063 | 0.1074 | | 0.0335 | 198.0 | 2475 | 0.9191 | 0.77 | 0.3858 | 1.5254 | 0.7700 | 0.7360 | 0.2129 | 0.1068 | | 0.0335 | 198.96 | 2487 | 0.9195 | 0.77 | 0.3861 | 1.5240 | 0.7700 | 0.7360 | 0.2059 | 0.1066 | | 0.0264 | 200.0 | 2500 | 0.9205 | 0.77 | 0.3864 | 1.5246 | 0.7700 | 0.7360 | 0.2081 | 0.1069 | | 0.0264 | 200.96 | 2512 | 0.9214 | 0.77 | 0.3865 | 1.5235 | 0.7700 | 0.7360 | 0.2018 | 0.1066 | | 0.0264 | 202.0 | 2525 | 0.9216 | 0.775 | 0.3867 | 1.5253 | 0.775 | 0.7418 | 0.2156 | 0.1068 | | 0.0264 | 202.96 | 2537 | 0.9218 | 0.775 | 0.3870 | 1.5225 | 0.775 | 0.7418 | 0.2108 | 0.1064 | | 0.0264 | 204.0 | 2550 | 0.9241 | 0.775 | 0.3871 | 1.4893 | 0.775 | 0.7418 | 0.2087 | 0.1071 | | 0.0264 | 204.96 | 2562 | 0.9270 | 0.77 | 0.3889 | 1.5244 | 0.7700 | 0.7360 | 0.2024 | 0.1071 | | 0.0264 | 206.0 | 2575 | 0.9260 | 0.775 | 0.3885 | 1.5262 | 0.775 | 0.7418 | 0.2116 | 0.1069 | | 0.0264 | 206.96 | 2587 | 0.9259 | 0.775 | 0.3883 | 1.5269 | 0.775 | 0.7418 | 0.2089 | 0.1065 | | 0.0264 | 208.0 | 2600 | 0.9254 | 0.77 | 0.3875 | 1.5247 | 0.7700 | 0.7360 | 0.2060 | 0.1069 | | 0.0264 | 208.96 | 2612 | 0.9285 | 0.775 | 0.3889 | 1.5281 | 0.775 | 0.7418 | 0.2115 | 0.1074 | | 0.0264 | 210.0 | 2625 | 0.9277 | 0.775 | 0.3886 | 1.5254 | 0.775 | 0.7418 | 0.2114 | 0.1069 | | 0.0264 | 210.96 | 2637 | 0.9304 | 0.775 | 0.3897 | 1.5278 | 0.775 | 0.7418 | 0.2095 | 0.1071 | | 0.0264 | 212.0 | 2650 | 0.9288 | 0.77 | 0.3886 | 1.5270 | 0.7700 | 0.7360 | 0.2068 | 0.1070 | | 0.0264 | 212.96 | 2662 | 0.9310 | 0.775 | 0.3896 | 1.5316 | 0.775 | 0.7418 | 0.2135 | 0.1071 | | 0.0264 | 214.0 | 2675 | 0.9311 | 0.775 | 0.3899 | 1.5263 | 0.775 | 0.7418 | 0.2187 | 0.1070 | | 0.0264 | 214.96 | 2687 | 0.9315 | 0.775 | 0.3899 | 1.5256 | 0.775 | 0.7418 | 0.2123 | 0.1068 | | 0.0264 | 216.0 | 2700 | 0.9315 | 0.77 | 0.3896 | 1.5258 | 0.7700 | 0.7360 | 0.2070 | 0.1071 | | 0.0264 | 216.96 | 2712 | 0.9334 | 0.775 | 0.3905 | 1.5291 | 0.775 | 0.7418 | 0.2088 | 0.1071 | | 0.0264 | 218.0 | 2725 | 0.9342 | 0.775 | 0.3908 | 1.5283 | 0.775 | 0.7418 | 0.2146 | 0.1072 | | 0.0264 | 218.96 | 2737 | 0.9337 | 0.775 | 0.3903 | 1.5282 | 0.775 | 0.7418 | 0.2110 | 0.1070 | | 0.0264 | 220.0 | 2750 | 0.9357 | 0.775 | 0.3913 | 1.5284 | 0.775 | 0.7418 | 0.2149 | 0.1073 | | 0.0264 | 220.96 | 2762 | 0.9367 | 0.775 | 0.3918 | 1.5299 | 0.775 | 0.7418 | 0.2088 | 0.1072 | | 0.0264 | 222.0 | 2775 | 0.9371 | 0.775 | 0.3916 | 1.5294 | 0.775 | 0.7418 | 0.2141 | 0.1075 | | 0.0264 | 222.96 | 2787 | 0.9359 | 0.775 | 0.3910 | 1.5271 | 0.775 | 0.7418 | 0.2126 | 0.1067 | | 0.0264 | 224.0 | 2800 | 0.9374 | 0.775 | 0.3918 | 1.5298 | 0.775 | 0.7418 | 0.2084 | 0.1072 | | 0.0264 | 224.96 | 2812 | 0.9378 | 0.775 | 0.3914 | 1.5296 | 0.775 | 0.7418 | 0.2073 | 0.1072 | | 0.0264 | 226.0 | 2825 | 0.9377 | 0.775 | 0.3916 | 1.5274 | 0.775 | 0.7418 | 0.2075 | 0.1066 | | 0.0264 | 226.96 | 2837 | 0.9412 | 0.775 | 0.3932 | 1.5310 | 0.775 | 0.7418 | 0.2096 | 0.1077 | | 0.0264 | 228.0 | 2850 | 0.9402 | 0.775 | 0.3923 | 1.5329 | 0.775 | 0.7418 | 0.2161 | 0.1076 | | 0.0264 | 228.96 | 2862 | 0.9420 | 0.775 | 0.3932 | 1.5301 | 0.775 | 0.7418 | 0.2078 | 0.1074 | | 0.0264 | 230.0 | 2875 | 0.9412 | 0.775 | 0.3925 | 1.5315 | 0.775 | 0.7418 | 0.2078 | 0.1076 | | 0.0264 | 230.96 | 2887 | 0.9422 | 0.775 | 0.3930 | 1.5340 | 0.775 | 0.7418 | 0.2179 | 0.1077 | | 0.0264 | 232.0 | 2900 | 0.9431 | 0.775 | 0.3933 | 1.5336 | 0.775 | 0.7418 | 0.2158 | 0.1081 | | 0.0264 | 232.96 | 2912 | 0.9428 | 0.775 | 0.3931 | 1.5304 | 0.775 | 0.7418 | 0.2086 | 0.1075 | | 0.0264 | 234.0 | 2925 | 0.9434 | 0.775 | 0.3935 | 1.5325 | 0.775 | 0.7418 | 0.2152 | 0.1074 | | 0.0264 | 234.96 | 2937 | 0.9431 | 0.775 | 0.3933 | 1.5286 | 0.775 | 0.7418 | 0.2081 | 0.1070 | | 0.0264 | 236.0 | 2950 | 0.9438 | 0.775 | 0.3935 | 1.5307 | 0.775 | 0.7418 | 0.2077 | 0.1073 | | 0.0264 | 236.96 | 2962 | 0.9452 | 0.775 | 0.3940 | 1.5329 | 0.775 | 0.7418 | 0.2217 | 0.1074 | | 0.0264 | 238.0 | 2975 | 0.9453 | 0.775 | 0.3939 | 1.5328 | 0.775 | 0.7418 | 0.2129 | 0.1076 | | 0.0264 | 238.96 | 2987 | 0.9451 | 0.775 | 0.3937 | 1.5308 | 0.775 | 0.7418 | 0.2133 | 0.1073 | | 0.0223 | 240.0 | 3000 | 0.9470 | 0.775 | 0.3947 | 1.5333 | 0.775 | 0.7418 | 0.2220 | 0.1077 | | 0.0223 | 240.96 | 3012 | 0.9461 | 0.775 | 0.3942 | 1.5329 | 0.775 | 0.7418 | 0.2127 | 0.1072 | | 0.0223 | 242.0 | 3025 | 0.9477 | 0.775 | 0.3949 | 1.5310 | 0.775 | 0.7418 | 0.2133 | 0.1074 | | 0.0223 | 242.96 | 3037 | 0.9480 | 0.775 | 0.3949 | 1.5331 | 0.775 | 0.7418 | 0.2165 | 0.1073 | | 0.0223 | 244.0 | 3050 | 0.9499 | 0.775 | 0.3955 | 1.5384 | 0.775 | 0.7418 | 0.2226 | 0.1080 | | 0.0223 | 244.96 | 3062 | 0.9476 | 0.775 | 0.3946 | 1.5322 | 0.775 | 0.7418 | 0.2128 | 0.1069 | | 0.0223 | 246.0 | 3075 | 0.9490 | 0.775 | 0.3953 | 1.5298 | 0.775 | 0.7418 | 0.2137 | 0.1071 | | 0.0223 | 246.96 | 3087 | 0.9496 | 0.775 | 0.3953 | 1.5315 | 0.775 | 0.7418 | 0.2133 | 0.1071 | | 0.0223 | 248.0 | 3100 | 0.9500 | 0.775 | 0.3955 | 1.5335 | 0.775 | 0.7418 | 0.2131 | 0.1072 | | 0.0223 | 248.96 | 3112 | 0.9503 | 0.775 | 0.3956 | 1.5323 | 0.775 | 0.7418 | 0.2164 | 0.1072 | | 0.0223 | 250.0 | 3125 | 0.9505 | 0.775 | 0.3955 | 1.5338 | 0.775 | 0.7418 | 0.2128 | 0.1071 | | 0.0223 | 250.96 | 3137 | 0.9510 | 0.775 | 0.3957 | 1.5372 | 0.775 | 0.7418 | 0.2266 | 0.1072 | | 0.0223 | 252.0 | 3150 | 0.9517 | 0.775 | 0.3960 | 1.5363 | 0.775 | 0.7418 | 0.2222 | 0.1073 | | 0.0223 | 252.96 | 3162 | 0.9526 | 0.775 | 0.3961 | 1.5372 | 0.775 | 0.7418 | 0.2227 | 0.1080 | | 0.0223 | 254.0 | 3175 | 0.9527 | 0.77 | 0.3963 | 1.5340 | 0.7700 | 0.7368 | 0.2174 | 0.1081 | | 0.0223 | 254.96 | 3187 | 0.9527 | 0.775 | 0.3962 | 1.5389 | 0.775 | 0.7418 | 0.2222 | 0.1074 | | 0.0223 | 256.0 | 3200 | 0.9528 | 0.775 | 0.3962 | 1.5347 | 0.775 | 0.7418 | 0.2258 | 0.1073 | | 0.0223 | 256.96 | 3212 | 0.9545 | 0.775 | 0.3969 | 1.5401 | 0.775 | 0.7418 | 0.2226 | 0.1083 | | 0.0223 | 258.0 | 3225 | 0.9540 | 0.775 | 0.3966 | 1.5369 | 0.775 | 0.7418 | 0.2224 | 0.1074 | | 0.0223 | 258.96 | 3237 | 0.9547 | 0.775 | 0.3969 | 1.5370 | 0.775 | 0.7418 | 0.2228 | 0.1082 | | 0.0223 | 260.0 | 3250 | 0.9549 | 0.775 | 0.3969 | 1.5381 | 0.775 | 0.7418 | 0.2226 | 0.1075 | | 0.0223 | 260.96 | 3262 | 0.9545 | 0.775 | 0.3968 | 1.5345 | 0.775 | 0.7418 | 0.2134 | 0.1072 | | 0.0223 | 262.0 | 3275 | 0.9550 | 0.775 | 0.3970 | 1.5362 | 0.775 | 0.7418 | 0.2145 | 0.1079 | | 0.0223 | 262.96 | 3287 | 0.9558 | 0.775 | 0.3971 | 1.5392 | 0.775 | 0.7418 | 0.2227 | 0.1076 | | 0.0223 | 264.0 | 3300 | 0.9557 | 0.775 | 0.3970 | 1.5383 | 0.775 | 0.7418 | 0.2226 | 0.1074 | | 0.0223 | 264.96 | 3312 | 0.9561 | 0.775 | 0.3973 | 1.5393 | 0.775 | 0.7418 | 0.2224 | 0.1080 | | 0.0223 | 266.0 | 3325 | 0.9563 | 0.775 | 0.3972 | 1.5387 | 0.775 | 0.7418 | 0.2224 | 0.1073 | | 0.0223 | 266.96 | 3337 | 0.9568 | 0.775 | 0.3974 | 1.5407 | 0.775 | 0.7418 | 0.2225 | 0.1082 | | 0.0223 | 268.0 | 3350 | 0.9567 | 0.775 | 0.3973 | 1.5373 | 0.775 | 0.7418 | 0.2259 | 0.1080 | | 0.0223 | 268.96 | 3362 | 0.9566 | 0.775 | 0.3973 | 1.5371 | 0.775 | 0.7418 | 0.2225 | 0.1080 | | 0.0223 | 270.0 | 3375 | 0.9574 | 0.775 | 0.3976 | 1.5403 | 0.775 | 0.7418 | 0.2227 | 0.1075 | | 0.0223 | 270.96 | 3387 | 0.9568 | 0.775 | 0.3974 | 1.5363 | 0.775 | 0.7418 | 0.2225 | 0.1072 | | 0.0223 | 272.0 | 3400 | 0.9580 | 0.775 | 0.3978 | 1.5465 | 0.775 | 0.7418 | 0.2241 | 0.1081 | | 0.0223 | 272.96 | 3412 | 0.9577 | 0.775 | 0.3977 | 1.5383 | 0.775 | 0.7418 | 0.2228 | 0.1074 | | 0.0223 | 274.0 | 3425 | 0.9577 | 0.775 | 0.3976 | 1.5409 | 0.775 | 0.7418 | 0.2225 | 0.1080 | | 0.0223 | 274.96 | 3437 | 0.9582 | 0.775 | 0.3978 | 1.5409 | 0.775 | 0.7418 | 0.2226 | 0.1075 | | 0.0223 | 276.0 | 3450 | 0.9581 | 0.775 | 0.3978 | 1.5412 | 0.775 | 0.7418 | 0.2225 | 0.1082 | | 0.0223 | 276.96 | 3462 | 0.9582 | 0.775 | 0.3978 | 1.5367 | 0.775 | 0.7418 | 0.2220 | 0.1073 | | 0.0223 | 278.0 | 3475 | 0.9587 | 0.775 | 0.3980 | 1.5422 | 0.775 | 0.7418 | 0.2244 | 0.1082 | | 0.0223 | 278.96 | 3487 | 0.9588 | 0.775 | 0.3980 | 1.5478 | 0.775 | 0.7418 | 0.2242 | 0.1082 | | 0.0202 | 280.0 | 3500 | 0.9586 | 0.775 | 0.3980 | 1.5381 | 0.775 | 0.7418 | 0.2219 | 0.1081 | | 0.0202 | 280.96 | 3512 | 0.9592 | 0.775 | 0.3981 | 1.5474 | 0.775 | 0.7418 | 0.2243 | 0.1082 | | 0.0202 | 282.0 | 3525 | 0.9588 | 0.775 | 0.3980 | 1.5396 | 0.775 | 0.7418 | 0.2227 | 0.1080 | | 0.0202 | 282.96 | 3537 | 0.9589 | 0.775 | 0.3980 | 1.5401 | 0.775 | 0.7418 | 0.2218 | 0.1074 | | 0.0202 | 284.0 | 3550 | 0.9593 | 0.775 | 0.3982 | 1.5441 | 0.775 | 0.7418 | 0.2243 | 0.1083 | | 0.0202 | 284.96 | 3562 | 0.9591 | 0.775 | 0.3981 | 1.5412 | 0.775 | 0.7418 | 0.2227 | 0.1082 | | 0.0202 | 286.0 | 3575 | 0.9592 | 0.775 | 0.3981 | 1.5417 | 0.775 | 0.7418 | 0.2227 | 0.1082 | | 0.0202 | 286.96 | 3587 | 0.9592 | 0.775 | 0.3981 | 1.5416 | 0.775 | 0.7418 | 0.2227 | 0.1082 | | 0.0202 | 288.0 | 3600 | 0.9592 | 0.775 | 0.3981 | 1.5416 | 0.775 | 0.7418 | 0.2227 | 0.1082 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/vit-tiny_tobacco3482_dualsimkd_

# vit-tiny_tobacco3482_dualsimkd_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1401 - Accuracy: 0.385 - Brier Loss: 0.8709 - Nll: 8.8462 - F1 Micro: 0.3850 - F1 Macro: 0.1979 - Ece: 0.3606 - Aurc: 0.3874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 100 | 0.5117 | 0.04 | 0.9009 | 19.1664 | 0.04 | 0.0077 | 0.1344 | 0.9445 | | No log | 2.0 | 200 | 0.3168 | 0.05 | 0.8997 | 15.0313 | 0.0500 | 0.0095 | 0.1344 | 0.8364 | | No log | 3.0 | 300 | 0.2703 | 0.18 | 0.8978 | 9.6860 | 0.18 | 0.0305 | 0.2180 | 0.7731 | | No log | 4.0 | 400 | 0.2266 | 0.18 | 0.8952 | 12.0957 | 0.18 | 0.0305 | 0.2223 | 0.7993 | | 1.1219 | 5.0 | 500 | 0.1687 | 0.18 | 0.8951 | 12.7136 | 0.18 | 0.0305 | 0.2215 | 0.7713 | | 1.1219 | 6.0 | 600 | 0.1331 | 0.165 | 0.8956 | 12.6737 | 0.165 | 0.0284 | 0.2044 | 0.7829 | | 1.1219 | 7.0 | 700 | 0.1139 | 0.18 | 0.8960 | 12.6380 | 0.18 | 0.0305 | 0.2283 | 0.7875 | | 1.1219 | 8.0 | 800 | 0.1143 | 0.18 | 0.8963 | 12.6385 | 0.18 | 0.0306 | 0.2183 | 0.7703 | | 1.1219 | 9.0 | 900 | 0.1246 | 0.18 | 0.8966 | 12.5389 | 0.18 | 0.0305 | 0.2223 | 0.7726 | | 0.0694 | 10.0 | 1000 | 0.1262 | 0.18 | 0.8961 | 12.6316 | 0.18 | 0.0305 | 0.2271 | 0.7894 | | 0.0694 | 11.0 | 1100 | 0.1186 | 0.155 | 0.8961 | 12.6309 | 0.155 | 0.0268 | 0.2169 | 0.6418 | | 0.0694 | 12.0 | 1200 | 0.1290 | 0.18 | 0.8960 | 12.6360 | 0.18 | 0.0305 | 0.2272 | 0.8014 | | 0.0694 | 13.0 | 1300 | 0.1202 | 0.18 | 0.8959 | 12.6644 | 0.18 | 0.0305 | 0.2274 | 0.7910 | | 0.0694 | 14.0 | 1400 | 0.1341 | 0.18 | 0.8960 | 12.6667 | 0.18 | 0.0305 | 0.2273 | 0.7916 | | 0.0505 | 15.0 | 1500 | 0.1234 | 0.18 | 0.8961 | 12.6653 | 0.18 | 0.0305 | 0.2261 | 0.7819 | | 0.0505 | 16.0 | 1600 | 0.1375 | 0.18 | 0.8960 | 12.6951 | 0.18 | 0.0305 | 0.2283 | 0.7929 | | 0.0505 | 17.0 | 1700 | 0.1249 | 0.18 | 0.8959 | 12.7041 | 0.18 | 0.0305 | 0.2262 | 0.7820 | | 0.0505 | 18.0 | 1800 | 0.1263 | 0.18 | 0.8964 | 12.6096 | 0.18 | 0.0305 | 0.2228 | 0.7900 | | 0.0505 | 19.0 | 1900 | 0.1243 | 0.18 | 0.8961 | 12.6667 | 0.18 | 0.0305 | 0.2229 | 0.7896 | | 0.0483 | 20.0 | 2000 | 0.1246 | 0.18 | 0.8960 | 12.6285 | 0.18 | 0.0305 | 0.2172 | 0.7913 | | 0.0483 | 21.0 | 2100 | 0.1218 | 0.18 | 0.8961 | 12.6375 | 0.18 | 0.0305 | 0.2250 | 0.8003 | | 0.0483 | 22.0 | 2200 | 0.1228 | 0.18 | 0.8964 | 12.5765 | 0.18 | 0.0305 | 0.2258 | 0.7938 | | 0.0483 | 23.0 | 2300 | 0.1270 | 0.18 | 0.8963 | 12.6332 | 0.18 | 0.0305 | 0.2239 | 0.8055 | | 0.0483 | 24.0 | 2400 | 0.1303 | 0.18 | 0.8963 | 12.5914 | 0.18 | 0.0305 | 0.2270 | 0.8006 | | 0.0484 | 25.0 | 2500 | 0.1234 | 0.18 | 0.8960 | 12.6429 | 0.18 | 0.0305 | 0.2208 | 0.7990 | | 0.0484 | 26.0 | 2600 | 0.1313 | 0.18 | 0.8965 | 12.5721 | 0.18 | 0.0305 | 0.2205 | 0.8069 | | 0.0484 | 27.0 | 2700 | 0.1314 | 0.18 | 0.8963 | 12.5982 | 0.18 | 0.0305 | 0.2247 | 0.8110 | | 0.0484 | 28.0 | 2800 | 0.1326 | 0.18 | 0.8962 | 12.6539 | 0.18 | 0.0305 | 0.2143 | 0.8083 | | 0.0484 | 29.0 | 2900 | 0.1337 | 0.18 | 0.8964 | 12.5814 | 0.18 | 0.0305 | 0.2225 | 0.8106 | | 0.0473 | 30.0 | 3000 | 0.1369 | 0.18 | 0.8962 | 12.6021 | 0.18 | 0.0305 | 0.2258 | 0.8095 | | 0.0473 | 31.0 | 3100 | 0.1295 | 0.18 | 0.8958 | 12.6587 | 0.18 | 0.0305 | 0.2273 | 0.8104 | | 0.0473 | 32.0 | 3200 | 0.1343 | 0.18 | 0.8959 | 12.6740 | 0.18 | 0.0305 | 0.2220 | 0.8119 | | 0.0473 | 33.0 | 3300 | 0.1359 | 0.18 | 0.8960 | 12.6790 | 0.18 | 0.0305 | 0.2273 | 0.8134 | | 0.0473 | 34.0 | 3400 | 0.1367 | 0.18 | 0.8961 | 12.6336 | 0.18 | 0.0305 | 0.2228 | 0.8159 | | 0.0476 | 35.0 | 3500 | 0.1378 | 0.18 | 0.8963 | 12.6119 | 0.18 | 0.0305 | 0.2270 | 0.8172 | | 0.0476 | 36.0 | 3600 | 0.1286 | 0.18 | 0.8961 | 12.6340 | 0.18 | 0.0305 | 0.2218 | 0.8148 | | 0.0476 | 37.0 | 3700 | 0.1333 | 0.18 | 0.8960 | 12.6328 | 0.18 | 0.0305 | 0.2207 | 0.8164 | | 0.0476 | 38.0 | 3800 | 0.1328 | 0.18 | 0.8963 | 12.6294 | 0.18 | 0.0305 | 0.2196 | 0.8180 | | 0.0476 | 39.0 | 3900 | 0.1344 | 0.18 | 0.8961 | 12.6417 | 0.18 | 0.0305 | 0.2207 | 0.8209 | | 0.0474 | 40.0 | 4000 | 0.1362 | 0.18 | 0.8959 | 12.6775 | 0.18 | 0.0305 | 0.2187 | 0.8198 | | 0.0474 | 41.0 | 4100 | 0.1340 | 0.18 | 0.8961 | 12.6746 | 0.18 | 0.0305 | 0.2249 | 0.8215 | | 0.0474 | 42.0 | 4200 | 0.1308 | 0.18 | 0.8958 | 12.6621 | 0.18 | 0.0305 | 0.2208 | 0.8215 | | 0.0474 | 43.0 | 4300 | 0.1372 | 0.18 | 0.8960 | 12.6133 | 0.18 | 0.0305 | 0.2249 | 0.8204 | | 0.0474 | 44.0 | 4400 | 0.1436 | 0.18 | 0.8963 | 12.6014 | 0.18 | 0.0305 | 0.2280 | 0.8201 | | 0.0472 | 45.0 | 4500 | 0.1374 | 0.18 | 0.8960 | 12.6316 | 0.18 | 0.0305 | 0.2228 | 0.8193 | | 0.0472 | 46.0 | 4600 | 0.1261 | 0.18 | 0.8957 | 12.6840 | 0.18 | 0.0305 | 0.2251 | 0.8220 | | 0.0472 | 47.0 | 4700 | 0.1340 | 0.18 | 0.8956 | 12.6704 | 0.18 | 0.0305 | 0.2251 | 0.8221 | | 0.0472 | 48.0 | 4800 | 0.1320 | 0.18 | 0.8959 | 12.6111 | 0.18 | 0.0305 | 0.2227 | 0.8203 | | 0.0472 | 49.0 | 4900 | 0.1336 | 0.18 | 0.8956 | 12.6838 | 0.18 | 0.0305 | 0.2294 | 0.8209 | | 0.0474 | 50.0 | 5000 | 0.1342 | 0.18 | 0.8959 | 12.3426 | 0.18 | 0.0305 | 0.2292 | 0.8218 | | 0.0474 | 51.0 | 5100 | 0.1362 | 0.18 | 0.8957 | 12.3611 | 0.18 | 0.0305 | 0.2261 | 0.8224 | | 0.0474 | 52.0 | 5200 | 0.1368 | 0.18 | 0.8958 | 11.5617 | 0.18 | 0.0305 | 0.2205 | 0.8222 | | 0.0474 | 53.0 | 5300 | 0.1391 | 0.18 | 0.8955 | 11.5519 | 0.18 | 0.0305 | 0.2312 | 0.8225 | | 0.0474 | 54.0 | 5400 | 0.1366 | 0.18 | 0.8947 | 12.2068 | 0.18 | 0.0305 | 0.2231 | 0.8231 | | 0.047 | 55.0 | 5500 | 0.1355 | 0.19 | 0.8943 | 11.5922 | 0.19 | 0.0641 | 0.2299 | 0.8248 | | 0.047 | 56.0 | 5600 | 0.1386 | 0.17 | 0.8930 | 11.8204 | 0.17 | 0.0705 | 0.2240 | 0.5968 | | 0.047 | 57.0 | 5700 | 0.1364 | 0.33 | 0.8936 | 11.0092 | 0.33 | 0.1878 | 0.3195 | 0.4381 | | 0.047 | 58.0 | 5800 | 0.1368 | 0.27 | 0.8923 | 11.0463 | 0.27 | 0.1541 | 0.2874 | 0.5187 | | 0.047 | 59.0 | 5900 | 0.1328 | 0.325 | 0.8915 | 10.5269 | 0.325 | 0.1702 | 0.3247 | 0.4469 | | 0.0469 | 60.0 | 6000 | 0.1402 | 0.235 | 0.8945 | 9.2940 | 0.235 | 0.1141 | 0.2558 | 0.6612 | | 0.0469 | 61.0 | 6100 | 0.1387 | 0.345 | 0.8913 | 9.2678 | 0.345 | 0.1657 | 0.3422 | 0.4100 | | 0.0469 | 62.0 | 6200 | 0.1386 | 0.31 | 0.8891 | 10.1100 | 0.31 | 0.1637 | 0.3134 | 0.4609 | | 0.0469 | 63.0 | 6300 | 0.1379 | 0.34 | 0.8892 | 9.1965 | 0.34 | 0.1582 | 0.3388 | 0.4344 | | 0.0469 | 64.0 | 6400 | 0.1375 | 0.335 | 0.8876 | 9.2252 | 0.335 | 0.1624 | 0.3356 | 0.4239 | | 0.0469 | 65.0 | 6500 | 0.1357 | 0.345 | 0.8868 | 9.1887 | 0.345 | 0.1659 | 0.3361 | 0.4061 | | 0.0469 | 66.0 | 6600 | 0.1394 | 0.345 | 0.8850 | 9.1819 | 0.345 | 0.1641 | 0.3398 | 0.4265 | | 0.0469 | 67.0 | 6700 | 0.1410 | 0.34 | 0.8850 | 9.1158 | 0.34 | 0.1590 | 0.3328 | 0.4302 | | 0.0469 | 68.0 | 6800 | 0.1387 | 0.295 | 0.8814 | 9.2693 | 0.295 | 0.1374 | 0.3039 | 0.4572 | | 0.0469 | 69.0 | 6900 | 0.1385 | 0.335 | 0.8814 | 9.1526 | 0.335 | 0.1668 | 0.3324 | 0.4205 | | 0.0463 | 70.0 | 7000 | 0.1392 | 0.34 | 0.8814 | 9.1159 | 0.34 | 0.1546 | 0.3405 | 0.4263 | | 0.0463 | 71.0 | 7100 | 0.1418 | 0.35 | 0.8820 | 9.1363 | 0.35 | 0.1692 | 0.3436 | 0.4019 | | 0.0463 | 72.0 | 7200 | 0.1379 | 0.35 | 0.8791 | 9.0483 | 0.35 | 0.1726 | 0.3402 | 0.4226 | | 0.0463 | 73.0 | 7300 | 0.1405 | 0.33 | 0.8760 | 9.3563 | 0.33 | 0.1731 | 0.3207 | 0.4307 | | 0.0463 | 74.0 | 7400 | 0.1401 | 0.31 | 0.8769 | 9.4413 | 0.31 | 0.1676 | 0.3099 | 0.4383 | | 0.0458 | 75.0 | 7500 | 0.1393 | 0.38 | 0.8778 | 9.0788 | 0.38 | 0.1985 | 0.3518 | 0.3976 | | 0.0458 | 76.0 | 7600 | 0.1384 | 0.39 | 0.8779 | 9.0233 | 0.39 | 0.2027 | 0.3673 | 0.4144 | | 0.0458 | 77.0 | 7700 | 0.1403 | 0.365 | 0.8818 | 9.1567 | 0.3650 | 0.1953 | 0.3518 | 0.4181 | | 0.0458 | 78.0 | 7800 | 0.1400 | 0.27 | 0.8725 | 11.0592 | 0.27 | 0.1627 | 0.2896 | 0.4809 | | 0.0458 | 79.0 | 7900 | 0.1402 | 0.375 | 0.8739 | 9.1158 | 0.375 | 0.1961 | 0.3540 | 0.3929 | | 0.0455 | 80.0 | 8000 | 0.1401 | 0.315 | 0.8722 | 9.9114 | 0.315 | 0.1771 | 0.3220 | 0.4443 | | 0.0455 | 81.0 | 8100 | 0.1378 | 0.39 | 0.8761 | 9.0128 | 0.39 | 0.2048 | 0.3642 | 0.4020 | | 0.0455 | 82.0 | 8200 | 0.1401 | 0.38 | 0.8729 | 9.1624 | 0.38 | 0.2006 | 0.3612 | 0.3924 | | 0.0455 | 83.0 | 8300 | 0.1391 | 0.38 | 0.8742 | 8.8982 | 0.38 | 0.2048 | 0.3561 | 0.3991 | | 0.0455 | 84.0 | 8400 | 0.1381 | 0.375 | 0.8734 | 9.0598 | 0.375 | 0.1901 | 0.3567 | 0.4010 | | 0.0453 | 85.0 | 8500 | 0.1398 | 0.39 | 0.8718 | 9.1407 | 0.39 | 0.2057 | 0.3693 | 0.3892 | | 0.0453 | 86.0 | 8600 | 0.1389 | 0.37 | 0.8721 | 9.3494 | 0.37 | 0.2006 | 0.3505 | 0.3914 | | 0.0453 | 87.0 | 8700 | 0.1390 | 0.395 | 0.8743 | 8.7444 | 0.395 | 0.2113 | 0.3724 | 0.3854 | | 0.0453 | 88.0 | 8800 | 0.1404 | 0.395 | 0.8739 | 8.7654 | 0.395 | 0.2134 | 0.3657 | 0.3925 | | 0.0453 | 89.0 | 8900 | 0.1409 | 0.385 | 0.8726 | 8.7763 | 0.3850 | 0.2032 | 0.3643 | 0.3963 | | 0.0451 | 90.0 | 9000 | 0.1403 | 0.39 | 0.8717 | 8.8363 | 0.39 | 0.2055 | 0.3668 | 0.3926 | | 0.0451 | 91.0 | 9100 | 0.1388 | 0.39 | 0.8719 | 9.2985 | 0.39 | 0.2099 | 0.3662 | 0.3847 | | 0.0451 | 92.0 | 9200 | 0.1397 | 0.385 | 0.8702 | 9.4449 | 0.3850 | 0.2050 | 0.3535 | 0.3877 | | 0.0451 | 93.0 | 9300 | 0.1403 | 0.385 | 0.8709 | 8.9790 | 0.3850 | 0.1989 | 0.3473 | 0.3887 | | 0.0451 | 94.0 | 9400 | 0.1400 | 0.39 | 0.8705 | 9.1647 | 0.39 | 0.2053 | 0.3569 | 0.3865 | | 0.045 | 95.0 | 9500 | 0.1404 | 0.395 | 0.8712 | 9.1707 | 0.395 | 0.2087 | 0.3688 | 0.3815 | | 0.045 | 96.0 | 9600 | 0.1404 | 0.385 | 0.8711 | 8.6711 | 0.3850 | 0.1980 | 0.3566 | 0.3867 | | 0.045 | 97.0 | 9700 | 0.1399 | 0.39 | 0.8706 | 9.1288 | 0.39 | 0.2035 | 0.3610 | 0.3845 | | 0.045 | 98.0 | 9800 | 0.1400 | 0.385 | 0.8708 | 9.1302 | 0.3850 | 0.1982 | 0.3538 | 0.3870 | | 0.045 | 99.0 | 9900 | 0.1398 | 0.39 | 0.8712 | 8.8257 | 0.39 | 0.2002 | 0.3660 | 0.3825 | | 0.0449 | 100.0 | 10000 | 0.1401 | 0.385 | 0.8709 | 8.8462 | 0.3850 | 0.1979 | 0.3606 | 0.3874 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.5

# 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4277 - Accuracy: 0.835 - Brier Loss: 0.2653 - Nll: 1.5700 - F1 Micro: 0.835 - F1 Macro: 0.8164 - Ece: 0.1805 - Aurc: 0.0632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.6826 | 0.23 | 0.8622 | 4.7953 | 0.23 | 0.1892 | 0.2929 | 0.7651 | | No log | 2.0 | 50 | 1.0374 | 0.53 | 0.6004 | 2.7646 | 0.53 | 0.4280 | 0.2624 | 0.2619 | | No log | 3.0 | 75 | 0.8158 | 0.665 | 0.4678 | 2.4034 | 0.665 | 0.5565 | 0.2488 | 0.1416 | | No log | 4.0 | 100 | 0.6879 | 0.72 | 0.3838 | 1.5355 | 0.72 | 0.6873 | 0.2297 | 0.1064 | | No log | 5.0 | 125 | 0.6511 | 0.775 | 0.3538 | 1.5183 | 0.775 | 0.7285 | 0.2235 | 0.0915 | | No log | 6.0 | 150 | 0.7310 | 0.755 | 0.3579 | 1.3899 | 0.755 | 0.7257 | 0.2190 | 0.0926 | | No log | 7.0 | 175 | 0.5698 | 0.795 | 0.3246 | 1.3920 | 0.795 | 0.7691 | 0.2251 | 0.0956 | | No log | 8.0 | 200 | 0.5675 | 0.805 | 0.3064 | 1.4278 | 0.805 | 0.7733 | 0.2093 | 0.0655 | | No log | 9.0 | 225 | 0.5986 | 0.8 | 0.3356 | 1.5317 | 0.8000 | 0.7890 | 0.2249 | 0.0913 | | No log | 10.0 | 250 | 0.6158 | 0.755 | 0.3475 | 1.5027 | 0.755 | 0.7340 | 0.2152 | 0.0782 | | No log | 11.0 | 275 | 0.5353 | 0.815 | 0.3037 | 1.6003 | 0.815 | 0.8143 | 0.2305 | 0.0749 | | No log | 12.0 | 300 | 0.5460 | 0.825 | 0.3008 | 1.7407 | 0.825 | 0.8070 | 0.2378 | 0.0856 | | No log | 13.0 | 325 | 0.4905 | 0.83 | 0.2787 | 1.1328 | 0.83 | 0.8099 | 0.2344 | 0.0481 | | No log | 14.0 | 350 | 0.4913 | 0.795 | 0.2881 | 1.2261 | 0.795 | 0.7521 | 0.2121 | 0.0661 | | No log | 15.0 | 375 | 0.4773 | 0.835 | 0.2753 | 1.2716 | 0.835 | 0.8140 | 0.2125 | 0.0636 | | No log | 16.0 | 400 | 0.4848 | 0.84 | 0.2751 | 1.5983 | 0.8400 | 0.8139 | 0.2195 | 0.0707 | | No log | 17.0 | 425 | 0.4994 | 0.805 | 0.2886 | 1.5637 | 0.805 | 0.7689 | 0.2049 | 0.0617 | | No log | 18.0 | 450 | 0.4610 | 0.835 | 0.2871 | 1.3906 | 0.835 | 0.8122 | 0.2175 | 0.0675 | | No log | 19.0 | 475 | 0.4594 | 0.84 | 0.2669 | 1.2217 | 0.8400 | 0.8214 | 0.2022 | 0.0516 | | 0.4534 | 20.0 | 500 | 0.4793 | 0.815 | 0.2874 | 1.4445 | 0.815 | 0.7965 | 0.2024 | 0.0641 | | 0.4534 | 21.0 | 525 | 0.5185 | 0.785 | 0.3215 | 1.8358 | 0.785 | 0.7743 | 0.2250 | 0.0850 | | 0.4534 | 22.0 | 550 | 0.4339 | 0.83 | 0.2635 | 1.2137 | 0.83 | 0.8200 | 0.1944 | 0.0610 | | 0.4534 | 23.0 | 575 | 0.4640 | 0.825 | 0.2770 | 1.4137 | 0.825 | 0.8086 | 0.1800 | 0.0674 | | 0.4534 | 24.0 | 600 | 0.4528 | 0.825 | 0.2692 | 1.3148 | 0.825 | 0.8077 | 0.1912 | 0.0678 | | 0.4534 | 25.0 | 625 | 0.4361 | 0.84 | 0.2600 | 1.4205 | 0.8400 | 0.8278 | 0.2066 | 0.0534 | | 0.4534 | 26.0 | 650 | 0.4239 | 0.835 | 0.2590 | 1.2112 | 0.835 | 0.8224 | 0.1850 | 0.0544 | | 0.4534 | 27.0 | 675 | 0.4294 | 0.82 | 0.2636 | 1.2671 | 0.82 | 0.8023 | 0.1866 | 0.0619 | | 0.4534 | 28.0 | 700 | 0.4327 | 0.84 | 0.2633 | 1.3084 | 0.8400 | 0.8283 | 0.1954 | 0.0628 | | 0.4534 | 29.0 | 725 | 0.4309 | 0.825 | 0.2640 | 1.4275 | 0.825 | 0.8022 | 0.2117 | 0.0667 | | 0.4534 | 30.0 | 750 | 0.4299 | 0.83 | 0.2636 | 1.3161 | 0.83 | 0.8103 | 0.2110 | 0.0620 | | 0.4534 | 31.0 | 775 | 0.4345 | 0.835 | 0.2634 | 1.4605 | 0.835 | 0.8269 | 0.1998 | 0.0562 | | 0.4534 | 32.0 | 800 | 0.4404 | 0.83 | 0.2743 | 1.3965 | 0.83 | 0.8077 | 0.2198 | 0.0669 | | 0.4534 | 33.0 | 825 | 0.4254 | 0.83 | 0.2614 | 1.3734 | 0.83 | 0.8133 | 0.1990 | 0.0567 | | 0.4534 | 34.0 | 850 | 0.4271 | 0.835 | 0.2632 | 1.3963 | 0.835 | 0.8164 | 0.1932 | 0.0649 | | 0.4534 | 35.0 | 875 | 0.4284 | 0.835 | 0.2636 | 1.3713 | 0.835 | 0.8164 | 0.2127 | 0.0634 | | 0.4534 | 36.0 | 900 | 0.4262 | 0.835 | 0.2628 | 1.4403 | 0.835 | 0.8164 | 0.1926 | 0.0649 | | 0.4534 | 37.0 | 925 | 0.4253 | 0.835 | 0.2621 | 1.3813 | 0.835 | 0.8164 | 0.2015 | 0.0628 | | 0.4534 | 38.0 | 950 | 0.4262 | 0.835 | 0.2626 | 1.4528 | 0.835 | 0.8164 | 0.1971 | 0.0628 | | 0.4534 | 39.0 | 975 | 0.4271 | 0.835 | 0.2629 | 1.4410 | 0.835 | 0.8164 | 0.1933 | 0.0627 | | 0.0663 | 40.0 | 1000 | 0.4283 | 0.835 | 0.2639 | 1.4647 | 0.835 | 0.8164 | 0.1996 | 0.0631 | | 0.0663 | 41.0 | 1025 | 0.4272 | 0.835 | 0.2639 | 1.4417 | 0.835 | 0.8164 | 0.2088 | 0.0630 | | 0.0663 | 42.0 | 1050 | 0.4276 | 0.835 | 0.2640 | 1.3976 | 0.835 | 0.8164 | 0.1992 | 0.0634 | | 0.0663 | 43.0 | 1075 | 0.4270 | 0.835 | 0.2633 | 1.4392 | 0.835 | 0.8164 | 0.1892 | 0.0628 | | 0.0663 | 44.0 | 1100 | 0.4264 | 0.835 | 0.2635 | 1.4429 | 0.835 | 0.8164 | 0.1885 | 0.0631 | | 0.0663 | 45.0 | 1125 | 0.4269 | 0.835 | 0.2637 | 1.4461 | 0.835 | 0.8164 | 0.1974 | 0.0629 | | 0.0663 | 46.0 | 1150 | 0.4268 | 0.835 | 0.2636 | 1.4415 | 0.835 | 0.8164 | 0.1866 | 0.0625 | | 0.0663 | 47.0 | 1175 | 0.4269 | 0.835 | 0.2641 | 1.4646 | 0.835 | 0.8164 | 0.1812 | 0.0636 | | 0.0663 | 48.0 | 1200 | 0.4271 | 0.835 | 0.2639 | 1.3990 | 0.835 | 0.8164 | 0.1865 | 0.0631 | | 0.0663 | 49.0 | 1225 | 0.4267 | 0.835 | 0.2639 | 1.4474 | 0.835 | 0.8164 | 0.1946 | 0.0629 | | 0.0663 | 50.0 | 1250 | 0.4273 | 0.835 | 0.2642 | 1.4492 | 0.835 | 0.8164 | 0.1802 | 0.0631 | | 0.0663 | 51.0 | 1275 | 0.4272 | 0.835 | 0.2644 | 1.4475 | 0.835 | 0.8164 | 0.1942 | 0.0630 | | 0.0663 | 52.0 | 1300 | 0.4283 | 0.835 | 0.2648 | 1.5157 | 0.835 | 0.8164 | 0.1963 | 0.0635 | | 0.0663 | 53.0 | 1325 | 0.4271 | 0.835 | 0.2643 | 1.5046 | 0.835 | 0.8164 | 0.1955 | 0.0633 | | 0.0663 | 54.0 | 1350 | 0.4271 | 0.835 | 0.2642 | 1.4629 | 0.835 | 0.8164 | 0.1790 | 0.0617 | | 0.0663 | 55.0 | 1375 | 0.4278 | 0.835 | 0.2649 | 1.5752 | 0.835 | 0.8164 | 0.2007 | 0.0635 | | 0.0663 | 56.0 | 1400 | 0.4280 | 0.835 | 0.2648 | 1.5165 | 0.835 | 0.8164 | 0.1706 | 0.0631 | | 0.0663 | 57.0 | 1425 | 0.4275 | 0.835 | 0.2644 | 1.5134 | 0.835 | 0.8164 | 0.1864 | 0.0629 | | 0.0663 | 58.0 | 1450 | 0.4270 | 0.835 | 0.2643 | 1.5088 | 0.835 | 0.8164 | 0.1883 | 0.0630 | | 0.0663 | 59.0 | 1475 | 0.4273 | 0.835 | 0.2644 | 1.5111 | 0.835 | 0.8164 | 0.1951 | 0.0630 | | 0.0615 | 60.0 | 1500 | 0.4281 | 0.835 | 0.2651 | 1.5727 | 0.835 | 0.8164 | 0.2084 | 0.0630 | | 0.0615 | 61.0 | 1525 | 0.4271 | 0.835 | 0.2647 | 1.5198 | 0.835 | 0.8164 | 0.1957 | 0.0631 | | 0.0615 | 62.0 | 1550 | 0.4276 | 0.835 | 0.2649 | 1.5139 | 0.835 | 0.8164 | 0.1969 | 0.0630 | | 0.0615 | 63.0 | 1575 | 0.4269 | 0.835 | 0.2646 | 1.4579 | 0.835 | 0.8164 | 0.1802 | 0.0629 | | 0.0615 | 64.0 | 1600 | 0.4275 | 0.835 | 0.2648 | 1.5144 | 0.835 | 0.8164 | 0.2006 | 0.0632 | | 0.0615 | 65.0 | 1625 | 0.4276 | 0.835 | 0.2649 | 1.5129 | 0.835 | 0.8164 | 0.1846 | 0.0632 | | 0.0615 | 66.0 | 1650 | 0.4272 | 0.835 | 0.2647 | 1.5165 | 0.835 | 0.8164 | 0.1796 | 0.0629 | | 0.0615 | 67.0 | 1675 | 0.4273 | 0.835 | 0.2647 | 1.5141 | 0.835 | 0.8164 | 0.1882 | 0.0631 | | 0.0615 | 68.0 | 1700 | 0.4276 | 0.835 | 0.2649 | 1.5146 | 0.835 | 0.8164 | 0.1799 | 0.0631 | | 0.0615 | 69.0 | 1725 | 0.4275 | 0.835 | 0.2649 | 1.5215 | 0.835 | 0.8164 | 0.1799 | 0.0631 | | 0.0615 | 70.0 | 1750 | 0.4275 | 0.835 | 0.2647 | 1.5124 | 0.835 | 0.8164 | 0.1884 | 0.0632 | | 0.0615 | 71.0 | 1775 | 0.4278 | 0.835 | 0.2652 | 1.5245 | 0.835 | 0.8164 | 0.1800 | 0.0631 | | 0.0615 | 72.0 | 1800 | 0.4277 | 0.835 | 0.2650 | 1.5169 | 0.835 | 0.8164 | 0.1802 | 0.0631 | | 0.0615 | 73.0 | 1825 | 0.4277 | 0.835 | 0.2651 | 1.5282 | 0.835 | 0.8164 | 0.1804 | 0.0633 | | 0.0615 | 74.0 | 1850 | 0.4273 | 0.835 | 0.2650 | 1.5156 | 0.835 | 0.8164 | 0.1804 | 0.0632 | | 0.0615 | 75.0 | 1875 | 0.4278 | 0.835 | 0.2653 | 1.5706 | 0.835 | 0.8164 | 0.1804 | 0.0632 | | 0.0615 | 76.0 | 1900 | 0.4275 | 0.835 | 0.2651 | 1.5337 | 0.835 | 0.8164 | 0.1807 | 0.0633 | | 0.0615 | 77.0 | 1925 | 0.4276 | 0.835 | 0.2652 | 1.5357 | 0.835 | 0.8164 | 0.1804 | 0.0633 | | 0.0615 | 78.0 | 1950 | 0.4275 | 0.835 | 0.2651 | 1.5701 | 0.835 | 0.8164 | 0.1805 | 0.0633 | | 0.0615 | 79.0 | 1975 | 0.4277 | 0.835 | 0.2651 | 1.5161 | 0.835 | 0.8164 | 0.1807 | 0.0633 | | 0.0614 | 80.0 | 2000 | 0.4278 | 0.835 | 0.2653 | 1.5709 | 0.835 | 0.8164 | 0.1808 | 0.0632 | | 0.0614 | 81.0 | 2025 | 0.4278 | 0.835 | 0.2653 | 1.5703 | 0.835 | 0.8164 | 0.1804 | 0.0632 | | 0.0614 | 82.0 | 2050 | 0.4278 | 0.835 | 0.2653 | 1.5700 | 0.835 | 0.8164 | 0.1806 | 0.0633 | | 0.0614 | 83.0 | 2075 | 0.4277 | 0.835 | 0.2652 | 1.5700 | 0.835 | 0.8164 | 0.1803 | 0.0631 | | 0.0614 | 84.0 | 2100 | 0.4276 | 0.835 | 0.2652 | 1.5694 | 0.835 | 0.8164 | 0.1804 | 0.0632 | | 0.0614 | 85.0 | 2125 | 0.4275 | 0.835 | 0.2652 | 1.5702 | 0.835 | 0.8164 | 0.1807 | 0.0633 | | 0.0614 | 86.0 | 2150 | 0.4276 | 0.835 | 0.2652 | 1.5699 | 0.835 | 0.8164 | 0.1805 | 0.0633 | | 0.0614 | 87.0 | 2175 | 0.4277 | 0.835 | 0.2653 | 1.5703 | 0.835 | 0.8164 | 0.1805 | 0.0633 | | 0.0614 | 88.0 | 2200 | 0.4277 | 0.835 | 0.2652 | 1.5702 | 0.835 | 0.8164 | 0.1882 | 0.0632 | | 0.0614 | 89.0 | 2225 | 0.4277 | 0.835 | 0.2653 | 1.5702 | 0.835 | 0.8164 | 0.1806 | 0.0633 | | 0.0614 | 90.0 | 2250 | 0.4276 | 0.835 | 0.2653 | 1.5696 | 0.835 | 0.8164 | 0.1806 | 0.0633 | | 0.0614 | 91.0 | 2275 | 0.4277 | 0.835 | 0.2653 | 1.5698 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 92.0 | 2300 | 0.4276 | 0.835 | 0.2652 | 1.5699 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 93.0 | 2325 | 0.4277 | 0.835 | 0.2653 | 1.5700 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 94.0 | 2350 | 0.4276 | 0.835 | 0.2653 | 1.5698 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 95.0 | 2375 | 0.4277 | 0.835 | 0.2653 | 1.5699 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 96.0 | 2400 | 0.4276 | 0.835 | 0.2653 | 1.5700 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 97.0 | 2425 | 0.4277 | 0.835 | 0.2653 | 1.5699 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 98.0 | 2450 | 0.4276 | 0.835 | 0.2653 | 1.5699 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 99.0 | 2475 | 0.4277 | 0.835 | 0.2653 | 1.5700 | 0.835 | 0.8164 | 0.1805 | 0.0632 | | 0.0614 | 100.0 | 2500 | 0.4277 | 0.835 | 0.2653 | 1.5700 | 0.835 | 0.8164 | 0.1805 | 0.0632 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.7

# 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4925 - Accuracy: 0.845 - Brier Loss: 0.2526 - Nll: 1.5547 - F1 Micro: 0.845 - F1 Macro: 0.8258 - Ece: 0.1785 - Aurc: 0.0736 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.8463 | 0.245 | 0.8631 | 4.7256 | 0.245 | 0.2002 | 0.2955 | 0.7640 | | No log | 2.0 | 50 | 1.1593 | 0.535 | 0.5972 | 2.7208 | 0.535 | 0.4319 | 0.2539 | 0.2591 | | No log | 3.0 | 75 | 0.9039 | 0.67 | 0.4555 | 2.3747 | 0.67 | 0.5677 | 0.2448 | 0.1349 | | No log | 4.0 | 100 | 0.7631 | 0.73 | 0.3757 | 1.5518 | 0.7300 | 0.7026 | 0.1947 | 0.0987 | | No log | 5.0 | 125 | 0.7412 | 0.775 | 0.3497 | 1.4677 | 0.775 | 0.7456 | 0.2239 | 0.0892 | | No log | 6.0 | 150 | 0.9198 | 0.72 | 0.3977 | 1.7618 | 0.72 | 0.6958 | 0.2190 | 0.1118 | | No log | 7.0 | 175 | 0.6117 | 0.81 | 0.2969 | 1.2112 | 0.81 | 0.7726 | 0.2244 | 0.0661 | | No log | 8.0 | 200 | 0.6296 | 0.78 | 0.3090 | 1.3439 | 0.78 | 0.7443 | 0.1959 | 0.0771 | | No log | 9.0 | 225 | 0.6850 | 0.785 | 0.3187 | 1.6325 | 0.785 | 0.7651 | 0.2194 | 0.0986 | | No log | 10.0 | 250 | 0.6304 | 0.79 | 0.3111 | 1.3598 | 0.79 | 0.7821 | 0.2106 | 0.0838 | | No log | 11.0 | 275 | 0.6668 | 0.775 | 0.3242 | 1.9754 | 0.775 | 0.6942 | 0.2005 | 0.0947 | | No log | 12.0 | 300 | 0.6795 | 0.775 | 0.3263 | 1.6182 | 0.775 | 0.7692 | 0.2155 | 0.0875 | | No log | 13.0 | 325 | 0.5156 | 0.85 | 0.2454 | 0.9647 | 0.85 | 0.8378 | 0.2033 | 0.0515 | | No log | 14.0 | 350 | 0.5341 | 0.845 | 0.2644 | 1.0410 | 0.845 | 0.8402 | 0.2050 | 0.0503 | | No log | 15.0 | 375 | 0.4678 | 0.865 | 0.2245 | 0.9232 | 0.865 | 0.8564 | 0.1836 | 0.0363 | | No log | 16.0 | 400 | 0.5620 | 0.82 | 0.2819 | 1.1475 | 0.82 | 0.7980 | 0.2050 | 0.0710 | | No log | 17.0 | 425 | 0.5253 | 0.83 | 0.2642 | 0.8809 | 0.83 | 0.8145 | 0.1811 | 0.0723 | | No log | 18.0 | 450 | 0.6295 | 0.815 | 0.2997 | 1.8144 | 0.815 | 0.8062 | 0.2120 | 0.0636 | | No log | 19.0 | 475 | 0.5748 | 0.83 | 0.2774 | 1.7900 | 0.83 | 0.8200 | 0.1920 | 0.0506 | | 0.466 | 20.0 | 500 | 0.4704 | 0.84 | 0.2275 | 0.8869 | 0.8400 | 0.8135 | 0.1882 | 0.0472 | | 0.466 | 21.0 | 525 | 0.5693 | 0.82 | 0.2820 | 1.3315 | 0.82 | 0.8013 | 0.2011 | 0.0821 | | 0.466 | 22.0 | 550 | 0.5251 | 0.81 | 0.2677 | 1.2663 | 0.81 | 0.7890 | 0.2037 | 0.0745 | | 0.466 | 23.0 | 575 | 0.5158 | 0.83 | 0.2638 | 1.2621 | 0.83 | 0.8070 | 0.1927 | 0.0614 | | 0.466 | 24.0 | 600 | 0.5056 | 0.835 | 0.2590 | 1.5337 | 0.835 | 0.8080 | 0.1887 | 0.0617 | | 0.466 | 25.0 | 625 | 0.4897 | 0.85 | 0.2476 | 1.4341 | 0.85 | 0.8361 | 0.1870 | 0.0627 | | 0.466 | 26.0 | 650 | 0.4994 | 0.85 | 0.2556 | 1.5846 | 0.85 | 0.8302 | 0.1965 | 0.0718 | | 0.466 | 27.0 | 675 | 0.4720 | 0.845 | 0.2406 | 1.3093 | 0.845 | 0.8234 | 0.1873 | 0.0704 | | 0.466 | 28.0 | 700 | 0.4858 | 0.84 | 0.2486 | 1.4459 | 0.8400 | 0.8192 | 0.1676 | 0.0730 | | 0.466 | 29.0 | 725 | 0.4908 | 0.84 | 0.2510 | 1.4941 | 0.8400 | 0.8159 | 0.1754 | 0.0717 | | 0.466 | 30.0 | 750 | 0.4805 | 0.855 | 0.2442 | 1.3279 | 0.855 | 0.8334 | 0.1827 | 0.0667 | | 0.466 | 31.0 | 775 | 0.4783 | 0.845 | 0.2428 | 1.4150 | 0.845 | 0.8264 | 0.1759 | 0.0660 | | 0.466 | 32.0 | 800 | 0.4822 | 0.855 | 0.2449 | 1.4848 | 0.855 | 0.8322 | 0.1928 | 0.0702 | | 0.466 | 33.0 | 825 | 0.4845 | 0.84 | 0.2462 | 1.4925 | 0.8400 | 0.8227 | 0.1837 | 0.0692 | | 0.466 | 34.0 | 850 | 0.4843 | 0.85 | 0.2466 | 1.4881 | 0.85 | 0.8295 | 0.1752 | 0.0683 | | 0.466 | 35.0 | 875 | 0.4837 | 0.85 | 0.2464 | 1.4939 | 0.85 | 0.8295 | 0.1842 | 0.0718 | | 0.466 | 36.0 | 900 | 0.4843 | 0.85 | 0.2467 | 1.4910 | 0.85 | 0.8295 | 0.1950 | 0.0705 | | 0.466 | 37.0 | 925 | 0.4862 | 0.85 | 0.2479 | 1.4938 | 0.85 | 0.8295 | 0.1871 | 0.0713 | | 0.466 | 38.0 | 950 | 0.4854 | 0.85 | 0.2478 | 1.4945 | 0.85 | 0.8295 | 0.1859 | 0.0719 | | 0.466 | 39.0 | 975 | 0.4850 | 0.85 | 0.2471 | 1.4891 | 0.85 | 0.8295 | 0.1855 | 0.0724 | | 0.0749 | 40.0 | 1000 | 0.4869 | 0.85 | 0.2484 | 1.4967 | 0.85 | 0.8295 | 0.1969 | 0.0718 | | 0.0749 | 41.0 | 1025 | 0.4857 | 0.85 | 0.2482 | 1.5544 | 0.85 | 0.8295 | 0.1904 | 0.0726 | | 0.0749 | 42.0 | 1050 | 0.4872 | 0.85 | 0.2487 | 1.5559 | 0.85 | 0.8295 | 0.1877 | 0.0732 | | 0.0749 | 43.0 | 1075 | 0.4873 | 0.85 | 0.2488 | 1.5534 | 0.85 | 0.8295 | 0.1871 | 0.0723 | | 0.0749 | 44.0 | 1100 | 0.4870 | 0.85 | 0.2489 | 1.5542 | 0.85 | 0.8295 | 0.1787 | 0.0730 | | 0.0749 | 45.0 | 1125 | 0.4874 | 0.85 | 0.2490 | 1.5544 | 0.85 | 0.8295 | 0.1867 | 0.0724 | | 0.0749 | 46.0 | 1150 | 0.4868 | 0.85 | 0.2486 | 1.5531 | 0.85 | 0.8295 | 0.1954 | 0.0723 | | 0.0749 | 47.0 | 1175 | 0.4879 | 0.85 | 0.2493 | 1.5546 | 0.85 | 0.8295 | 0.1842 | 0.0727 | | 0.0749 | 48.0 | 1200 | 0.4882 | 0.85 | 0.2495 | 1.5537 | 0.85 | 0.8295 | 0.1864 | 0.0730 | | 0.0749 | 49.0 | 1225 | 0.4875 | 0.85 | 0.2492 | 1.5537 | 0.85 | 0.8295 | 0.1884 | 0.0727 | | 0.0749 | 50.0 | 1250 | 0.4880 | 0.85 | 0.2494 | 1.5528 | 0.85 | 0.8295 | 0.1877 | 0.0726 | | 0.0749 | 51.0 | 1275 | 0.4888 | 0.85 | 0.2499 | 1.5539 | 0.85 | 0.8295 | 0.1754 | 0.0725 | | 0.0749 | 52.0 | 1300 | 0.4894 | 0.85 | 0.2501 | 1.5540 | 0.85 | 0.8295 | 0.1883 | 0.0736 | | 0.0749 | 53.0 | 1325 | 0.4889 | 0.85 | 0.2501 | 1.5533 | 0.85 | 0.8295 | 0.1708 | 0.0727 | | 0.0749 | 54.0 | 1350 | 0.4891 | 0.85 | 0.2500 | 1.5531 | 0.85 | 0.8295 | 0.1785 | 0.0729 | | 0.0749 | 55.0 | 1375 | 0.4904 | 0.85 | 0.2509 | 1.5541 | 0.85 | 0.8295 | 0.1744 | 0.0730 | | 0.0749 | 56.0 | 1400 | 0.4903 | 0.85 | 0.2507 | 1.5541 | 0.85 | 0.8295 | 0.1897 | 0.0730 | | 0.0749 | 57.0 | 1425 | 0.4894 | 0.85 | 0.2503 | 1.5536 | 0.85 | 0.8295 | 0.1792 | 0.0730 | | 0.0749 | 58.0 | 1450 | 0.4889 | 0.85 | 0.2501 | 1.5531 | 0.85 | 0.8295 | 0.1892 | 0.0730 | | 0.0749 | 59.0 | 1475 | 0.4907 | 0.85 | 0.2511 | 1.5542 | 0.85 | 0.8295 | 0.1767 | 0.0733 | | 0.0712 | 60.0 | 1500 | 0.4897 | 0.85 | 0.2506 | 1.5540 | 0.85 | 0.8295 | 0.1813 | 0.0732 | | 0.0712 | 61.0 | 1525 | 0.4906 | 0.85 | 0.2512 | 1.5545 | 0.85 | 0.8295 | 0.1853 | 0.0733 | | 0.0712 | 62.0 | 1550 | 0.4905 | 0.85 | 0.2512 | 1.5541 | 0.85 | 0.8295 | 0.1723 | 0.0733 | | 0.0712 | 63.0 | 1575 | 0.4904 | 0.85 | 0.2512 | 1.5543 | 0.85 | 0.8295 | 0.1817 | 0.0732 | | 0.0712 | 64.0 | 1600 | 0.4915 | 0.85 | 0.2515 | 1.5544 | 0.85 | 0.8295 | 0.1942 | 0.0736 | | 0.0712 | 65.0 | 1625 | 0.4898 | 0.85 | 0.2506 | 1.5534 | 0.85 | 0.8295 | 0.1712 | 0.0735 | | 0.0712 | 66.0 | 1650 | 0.4911 | 0.85 | 0.2516 | 1.5548 | 0.85 | 0.8295 | 0.1824 | 0.0733 | | 0.0712 | 67.0 | 1675 | 0.4908 | 0.85 | 0.2513 | 1.5546 | 0.85 | 0.8295 | 0.1896 | 0.0734 | | 0.0712 | 68.0 | 1700 | 0.4911 | 0.85 | 0.2516 | 1.5548 | 0.85 | 0.8295 | 0.1744 | 0.0734 | | 0.0712 | 69.0 | 1725 | 0.4912 | 0.85 | 0.2516 | 1.5541 | 0.85 | 0.8295 | 0.1726 | 0.0733 | | 0.0712 | 70.0 | 1750 | 0.4910 | 0.85 | 0.2514 | 1.5543 | 0.85 | 0.8295 | 0.1827 | 0.0736 | | 0.0712 | 71.0 | 1775 | 0.4918 | 0.85 | 0.2520 | 1.5546 | 0.85 | 0.8295 | 0.1909 | 0.0736 | | 0.0712 | 72.0 | 1800 | 0.4916 | 0.85 | 0.2519 | 1.5545 | 0.85 | 0.8295 | 0.1830 | 0.0734 | | 0.0712 | 73.0 | 1825 | 0.4913 | 0.85 | 0.2517 | 1.5540 | 0.85 | 0.8295 | 0.1835 | 0.0733 | | 0.0712 | 74.0 | 1850 | 0.4918 | 0.85 | 0.2521 | 1.5544 | 0.85 | 0.8295 | 0.1831 | 0.0736 | | 0.0712 | 75.0 | 1875 | 0.4919 | 0.85 | 0.2521 | 1.5548 | 0.85 | 0.8295 | 0.1829 | 0.0734 | | 0.0712 | 76.0 | 1900 | 0.4916 | 0.85 | 0.2520 | 1.5547 | 0.85 | 0.8295 | 0.1831 | 0.0733 | | 0.0712 | 77.0 | 1925 | 0.4919 | 0.85 | 0.2521 | 1.5542 | 0.85 | 0.8295 | 0.1732 | 0.0735 | | 0.0712 | 78.0 | 1950 | 0.4920 | 0.85 | 0.2521 | 1.5541 | 0.85 | 0.8295 | 0.1831 | 0.0734 | | 0.0712 | 79.0 | 1975 | 0.4920 | 0.85 | 0.2522 | 1.5544 | 0.85 | 0.8295 | 0.1833 | 0.0734 | | 0.0712 | 80.0 | 2000 | 0.4922 | 0.845 | 0.2523 | 1.5549 | 0.845 | 0.8258 | 0.1859 | 0.0735 | | 0.0712 | 81.0 | 2025 | 0.4920 | 0.85 | 0.2522 | 1.5542 | 0.85 | 0.8295 | 0.1830 | 0.0732 | | 0.0712 | 82.0 | 2050 | 0.4920 | 0.845 | 0.2522 | 1.5549 | 0.845 | 0.8258 | 0.1783 | 0.0734 | | 0.0712 | 83.0 | 2075 | 0.4922 | 0.85 | 0.2524 | 1.5546 | 0.85 | 0.8295 | 0.1832 | 0.0734 | | 0.0712 | 84.0 | 2100 | 0.4920 | 0.845 | 0.2522 | 1.5543 | 0.845 | 0.8258 | 0.1784 | 0.0735 | | 0.0712 | 85.0 | 2125 | 0.4921 | 0.845 | 0.2523 | 1.5547 | 0.845 | 0.8258 | 0.1785 | 0.0735 | | 0.0712 | 86.0 | 2150 | 0.4921 | 0.85 | 0.2523 | 1.5545 | 0.85 | 0.8295 | 0.1836 | 0.0733 | | 0.0712 | 87.0 | 2175 | 0.4924 | 0.85 | 0.2524 | 1.5547 | 0.85 | 0.8295 | 0.1836 | 0.0734 | | 0.0712 | 88.0 | 2200 | 0.4925 | 0.845 | 0.2524 | 1.5548 | 0.845 | 0.8258 | 0.1785 | 0.0735 | | 0.0712 | 89.0 | 2225 | 0.4924 | 0.85 | 0.2525 | 1.5548 | 0.85 | 0.8295 | 0.1835 | 0.0734 | | 0.0712 | 90.0 | 2250 | 0.4921 | 0.845 | 0.2523 | 1.5545 | 0.845 | 0.8258 | 0.1688 | 0.0735 | | 0.0712 | 91.0 | 2275 | 0.4925 | 0.845 | 0.2525 | 1.5546 | 0.845 | 0.8258 | 0.1785 | 0.0735 | | 0.0712 | 92.0 | 2300 | 0.4924 | 0.845 | 0.2524 | 1.5546 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0712 | 93.0 | 2325 | 0.4925 | 0.845 | 0.2526 | 1.5548 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0712 | 94.0 | 2350 | 0.4924 | 0.845 | 0.2525 | 1.5547 | 0.845 | 0.8258 | 0.1786 | 0.0736 | | 0.0712 | 95.0 | 2375 | 0.4926 | 0.845 | 0.2526 | 1.5547 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0712 | 96.0 | 2400 | 0.4925 | 0.845 | 0.2526 | 1.5548 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0712 | 97.0 | 2425 | 0.4925 | 0.845 | 0.2526 | 1.5547 | 0.845 | 0.8258 | 0.1785 | 0.0735 | | 0.0712 | 98.0 | 2450 | 0.4926 | 0.845 | 0.2526 | 1.5548 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0712 | 99.0 | 2475 | 0.4925 | 0.845 | 0.2526 | 1.5548 | 0.845 | 0.8258 | 0.1785 | 0.0736 | | 0.0711 | 100.0 | 2500 | 0.4925 | 0.845 | 0.2526 | 1.5547 | 0.845 | 0.8258 | 0.1785 | 0.0736 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.9

# 6_e_200-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5536 - Accuracy: 0.82 - Brier Loss: 0.2571 - Nll: 1.4560 - F1 Micro: 0.82 - F1 Macro: 0.7994 - Ece: 0.1404 - Aurc: 0.0578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 2.0125 | 0.23 | 0.8650 | 4.4951 | 0.23 | 0.1799 | 0.2806 | 0.7660 | | No log | 2.0 | 50 | 1.2756 | 0.555 | 0.5948 | 2.6781 | 0.555 | 0.4537 | 0.2800 | 0.2519 | | No log | 3.0 | 75 | 0.9515 | 0.685 | 0.4392 | 1.9416 | 0.685 | 0.5937 | 0.2067 | 0.1288 | | No log | 4.0 | 100 | 0.7861 | 0.72 | 0.3622 | 1.5125 | 0.72 | 0.6675 | 0.2050 | 0.0961 | | No log | 5.0 | 125 | 0.7551 | 0.77 | 0.3362 | 1.5478 | 0.7700 | 0.7318 | 0.2043 | 0.0838 | | No log | 6.0 | 150 | 0.8056 | 0.77 | 0.3525 | 1.4305 | 0.7700 | 0.7589 | 0.1943 | 0.0891 | | No log | 7.0 | 175 | 0.7942 | 0.775 | 0.3310 | 1.8237 | 0.775 | 0.7454 | 0.1812 | 0.0924 | | No log | 8.0 | 200 | 0.7735 | 0.77 | 0.3384 | 1.5161 | 0.7700 | 0.7530 | 0.1987 | 0.0931 | | No log | 9.0 | 225 | 0.6992 | 0.79 | 0.3025 | 1.5664 | 0.79 | 0.7777 | 0.1631 | 0.0774 | | No log | 10.0 | 250 | 0.6753 | 0.8 | 0.2955 | 1.5189 | 0.8000 | 0.7900 | 0.1654 | 0.0633 | | No log | 11.0 | 275 | 0.7701 | 0.805 | 0.3018 | 1.4787 | 0.805 | 0.7932 | 0.1581 | 0.0881 | | No log | 12.0 | 300 | 0.7164 | 0.79 | 0.3292 | 1.3527 | 0.79 | 0.7892 | 0.1946 | 0.0871 | | No log | 13.0 | 325 | 0.6376 | 0.8 | 0.2901 | 1.4953 | 0.8000 | 0.7824 | 0.1770 | 0.0659 | | No log | 14.0 | 350 | 0.7319 | 0.77 | 0.3247 | 1.6062 | 0.7700 | 0.7424 | 0.1803 | 0.0816 | | No log | 15.0 | 375 | 0.5749 | 0.805 | 0.2738 | 0.8483 | 0.805 | 0.8010 | 0.1569 | 0.0647 | | No log | 16.0 | 400 | 0.6879 | 0.775 | 0.3085 | 1.3379 | 0.775 | 0.7759 | 0.1909 | 0.0730 | | No log | 17.0 | 425 | 0.5094 | 0.85 | 0.2241 | 1.4391 | 0.85 | 0.8360 | 0.1589 | 0.0441 | | No log | 18.0 | 450 | 0.6826 | 0.8 | 0.3015 | 1.6933 | 0.8000 | 0.7969 | 0.1651 | 0.0792 | | No log | 19.0 | 475 | 0.5677 | 0.825 | 0.2622 | 1.5426 | 0.825 | 0.8051 | 0.1600 | 0.0515 | | 0.4493 | 20.0 | 500 | 0.5156 | 0.85 | 0.2312 | 1.5882 | 0.85 | 0.8471 | 0.1466 | 0.0427 | | 0.4493 | 21.0 | 525 | 0.5743 | 0.83 | 0.2600 | 1.5702 | 0.83 | 0.8187 | 0.1604 | 0.0540 | | 0.4493 | 22.0 | 550 | 0.5872 | 0.825 | 0.2712 | 1.6270 | 0.825 | 0.8056 | 0.1687 | 0.0572 | | 0.4493 | 23.0 | 575 | 0.5770 | 0.81 | 0.2701 | 1.5089 | 0.81 | 0.7969 | 0.1559 | 0.0655 | | 0.4493 | 24.0 | 600 | 0.5621 | 0.82 | 0.2590 | 1.3500 | 0.82 | 0.8052 | 0.1621 | 0.0587 | | 0.4493 | 25.0 | 625 | 0.5480 | 0.805 | 0.2518 | 1.2519 | 0.805 | 0.7884 | 0.1483 | 0.0619 | | 0.4493 | 26.0 | 650 | 0.5555 | 0.81 | 0.2575 | 1.3183 | 0.81 | 0.7926 | 0.1585 | 0.0598 | | 0.4493 | 27.0 | 675 | 0.5449 | 0.82 | 0.2524 | 1.4400 | 0.82 | 0.8059 | 0.1713 | 0.0579 | | 0.4493 | 28.0 | 700 | 0.5483 | 0.81 | 0.2545 | 1.4400 | 0.81 | 0.7894 | 0.1450 | 0.0580 | | 0.4493 | 29.0 | 725 | 0.5448 | 0.81 | 0.2524 | 1.3070 | 0.81 | 0.7931 | 0.1447 | 0.0595 | | 0.4493 | 30.0 | 750 | 0.5476 | 0.815 | 0.2538 | 1.3101 | 0.815 | 0.7982 | 0.1536 | 0.0582 | | 0.4493 | 31.0 | 775 | 0.5433 | 0.82 | 0.2529 | 1.3812 | 0.82 | 0.8011 | 0.1637 | 0.0575 | | 0.4493 | 32.0 | 800 | 0.5469 | 0.805 | 0.2528 | 1.2973 | 0.805 | 0.7905 | 0.1668 | 0.0600 | | 0.4493 | 33.0 | 825 | 0.5443 | 0.815 | 0.2525 | 1.3020 | 0.815 | 0.7933 | 0.1768 | 0.0579 | | 0.4493 | 34.0 | 850 | 0.5442 | 0.82 | 0.2521 | 1.3234 | 0.82 | 0.8011 | 0.1555 | 0.0580 | | 0.4493 | 35.0 | 875 | 0.5434 | 0.82 | 0.2531 | 1.4362 | 0.82 | 0.8011 | 0.1430 | 0.0564 | | 0.4493 | 36.0 | 900 | 0.5469 | 0.815 | 0.2534 | 1.3075 | 0.815 | 0.7933 | 0.1590 | 0.0578 | | 0.4493 | 37.0 | 925 | 0.5468 | 0.815 | 0.2546 | 1.3204 | 0.815 | 0.7933 | 0.1623 | 0.0567 | | 0.4493 | 38.0 | 950 | 0.5473 | 0.815 | 0.2540 | 1.3722 | 0.815 | 0.7933 | 0.1514 | 0.0582 | | 0.4493 | 39.0 | 975 | 0.5453 | 0.82 | 0.2532 | 1.3874 | 0.82 | 0.8011 | 0.1751 | 0.0568 | | 0.0581 | 40.0 | 1000 | 0.5475 | 0.815 | 0.2543 | 1.3116 | 0.815 | 0.7933 | 0.1654 | 0.0573 | | 0.0581 | 41.0 | 1025 | 0.5452 | 0.815 | 0.2533 | 1.4421 | 0.815 | 0.7933 | 0.1459 | 0.0579 | | 0.0581 | 42.0 | 1050 | 0.5467 | 0.815 | 0.2538 | 1.3730 | 0.815 | 0.7933 | 0.1642 | 0.0576 | | 0.0581 | 43.0 | 1075 | 0.5478 | 0.815 | 0.2544 | 1.3086 | 0.815 | 0.7933 | 0.1657 | 0.0581 | | 0.0581 | 44.0 | 1100 | 0.5482 | 0.815 | 0.2545 | 1.3744 | 0.815 | 0.7933 | 0.1629 | 0.0583 | | 0.0581 | 45.0 | 1125 | 0.5493 | 0.815 | 0.2550 | 1.3676 | 0.815 | 0.7933 | 0.1638 | 0.0594 | | 0.0581 | 46.0 | 1150 | 0.5478 | 0.82 | 0.2547 | 1.4645 | 0.82 | 0.8011 | 0.1631 | 0.0572 | | 0.0581 | 47.0 | 1175 | 0.5487 | 0.815 | 0.2547 | 1.3795 | 0.815 | 0.7933 | 0.1634 | 0.0577 | | 0.0581 | 48.0 | 1200 | 0.5471 | 0.825 | 0.2546 | 1.4421 | 0.825 | 0.8067 | 0.1436 | 0.0564 | | 0.0581 | 49.0 | 1225 | 0.5489 | 0.815 | 0.2547 | 1.3676 | 0.815 | 0.7933 | 0.1663 | 0.0578 | | 0.0581 | 50.0 | 1250 | 0.5482 | 0.82 | 0.2549 | 1.4346 | 0.82 | 0.7990 | 0.1481 | 0.0574 | | 0.0581 | 51.0 | 1275 | 0.5472 | 0.82 | 0.2540 | 1.5012 | 0.82 | 0.8011 | 0.1565 | 0.0569 | | 0.0581 | 52.0 | 1300 | 0.5489 | 0.825 | 0.2553 | 1.4351 | 0.825 | 0.8051 | 0.1608 | 0.0576 | | 0.0581 | 53.0 | 1325 | 0.5486 | 0.815 | 0.2549 | 1.3799 | 0.815 | 0.7933 | 0.1483 | 0.0573 | | 0.0581 | 54.0 | 1350 | 0.5498 | 0.815 | 0.2552 | 1.4434 | 0.815 | 0.7933 | 0.1542 | 0.0578 | | 0.0581 | 55.0 | 1375 | 0.5508 | 0.82 | 0.2559 | 1.4394 | 0.82 | 0.7994 | 0.1562 | 0.0576 | | 0.0581 | 56.0 | 1400 | 0.5492 | 0.825 | 0.2552 | 1.4368 | 0.825 | 0.8051 | 0.1483 | 0.0572 | | 0.0581 | 57.0 | 1425 | 0.5501 | 0.815 | 0.2552 | 1.3874 | 0.815 | 0.7933 | 0.1390 | 0.0579 | | 0.0581 | 58.0 | 1450 | 0.5497 | 0.82 | 0.2553 | 1.4365 | 0.82 | 0.7994 | 0.1437 | 0.0579 | | 0.0581 | 59.0 | 1475 | 0.5507 | 0.82 | 0.2557 | 1.4343 | 0.82 | 0.7994 | 0.1389 | 0.0584 | | 0.056 | 60.0 | 1500 | 0.5501 | 0.825 | 0.2555 | 1.4410 | 0.825 | 0.8051 | 0.1585 | 0.0583 | | 0.056 | 61.0 | 1525 | 0.5510 | 0.82 | 0.2559 | 1.4380 | 0.82 | 0.7994 | 0.1395 | 0.0578 | | 0.056 | 62.0 | 1550 | 0.5510 | 0.82 | 0.2558 | 1.4421 | 0.82 | 0.7994 | 0.1441 | 0.0573 | | 0.056 | 63.0 | 1575 | 0.5508 | 0.82 | 0.2559 | 1.4369 | 0.82 | 0.7994 | 0.1395 | 0.0575 | | 0.056 | 64.0 | 1600 | 0.5514 | 0.82 | 0.2560 | 1.4410 | 0.82 | 0.7994 | 0.1393 | 0.0579 | | 0.056 | 65.0 | 1625 | 0.5519 | 0.825 | 0.2563 | 1.4544 | 0.825 | 0.8051 | 0.1427 | 0.0575 | | 0.056 | 66.0 | 1650 | 0.5510 | 0.82 | 0.2560 | 1.4400 | 0.82 | 0.7994 | 0.1391 | 0.0576 | | 0.056 | 67.0 | 1675 | 0.5520 | 0.825 | 0.2563 | 1.4396 | 0.825 | 0.8051 | 0.1422 | 0.0580 | | 0.056 | 68.0 | 1700 | 0.5516 | 0.82 | 0.2561 | 1.4412 | 0.82 | 0.7994 | 0.1394 | 0.0580 | | 0.056 | 69.0 | 1725 | 0.5512 | 0.82 | 0.2560 | 1.4433 | 0.82 | 0.7994 | 0.1393 | 0.0577 | | 0.056 | 70.0 | 1750 | 0.5515 | 0.82 | 0.2561 | 1.4418 | 0.82 | 0.7994 | 0.1391 | 0.0576 | | 0.056 | 71.0 | 1775 | 0.5517 | 0.82 | 0.2562 | 1.4448 | 0.82 | 0.7994 | 0.1449 | 0.0581 | | 0.056 | 72.0 | 1800 | 0.5524 | 0.825 | 0.2566 | 1.4421 | 0.825 | 0.8051 | 0.1437 | 0.0579 | | 0.056 | 73.0 | 1825 | 0.5518 | 0.82 | 0.2562 | 1.4403 | 0.82 | 0.7994 | 0.1469 | 0.0576 | | 0.056 | 74.0 | 1850 | 0.5529 | 0.825 | 0.2568 | 1.4450 | 0.825 | 0.8051 | 0.1434 | 0.0580 | | 0.056 | 75.0 | 1875 | 0.5528 | 0.82 | 0.2566 | 1.4475 | 0.82 | 0.7994 | 0.1447 | 0.0585 | | 0.056 | 76.0 | 1900 | 0.5529 | 0.82 | 0.2568 | 1.4463 | 0.82 | 0.7994 | 0.1447 | 0.0578 | | 0.056 | 77.0 | 1925 | 0.5528 | 0.82 | 0.2567 | 1.4469 | 0.82 | 0.7994 | 0.1401 | 0.0577 | | 0.056 | 78.0 | 1950 | 0.5525 | 0.82 | 0.2565 | 1.4506 | 0.82 | 0.7994 | 0.1444 | 0.0576 | | 0.056 | 79.0 | 1975 | 0.5527 | 0.825 | 0.2567 | 1.4479 | 0.825 | 0.8051 | 0.1423 | 0.0576 | | 0.0559 | 80.0 | 2000 | 0.5530 | 0.825 | 0.2568 | 1.4429 | 0.825 | 0.8051 | 0.1423 | 0.0578 | | 0.0559 | 81.0 | 2025 | 0.5529 | 0.825 | 0.2567 | 1.4489 | 0.825 | 0.8051 | 0.1422 | 0.0581 | | 0.0559 | 82.0 | 2050 | 0.5529 | 0.82 | 0.2568 | 1.4550 | 0.82 | 0.7994 | 0.1401 | 0.0576 | | 0.0559 | 83.0 | 2075 | 0.5534 | 0.82 | 0.2570 | 1.4458 | 0.82 | 0.7994 | 0.1399 | 0.0580 | | 0.0559 | 84.0 | 2100 | 0.5530 | 0.82 | 0.2568 | 1.4497 | 0.82 | 0.7994 | 0.1399 | 0.0577 | | 0.0559 | 85.0 | 2125 | 0.5533 | 0.82 | 0.2570 | 1.4507 | 0.82 | 0.7994 | 0.1401 | 0.0577 | | 0.0559 | 86.0 | 2150 | 0.5531 | 0.825 | 0.2568 | 1.4515 | 0.825 | 0.8051 | 0.1428 | 0.0577 | | 0.0559 | 87.0 | 2175 | 0.5534 | 0.82 | 0.2569 | 1.4503 | 0.82 | 0.7994 | 0.1404 | 0.0577 | | 0.0559 | 88.0 | 2200 | 0.5534 | 0.82 | 0.2569 | 1.4532 | 0.82 | 0.7994 | 0.1399 | 0.0581 | | 0.0559 | 89.0 | 2225 | 0.5533 | 0.825 | 0.2569 | 1.4499 | 0.825 | 0.8051 | 0.1423 | 0.0578 | | 0.0559 | 90.0 | 2250 | 0.5534 | 0.82 | 0.2570 | 1.4517 | 0.82 | 0.7994 | 0.1404 | 0.0577 | | 0.0559 | 91.0 | 2275 | 0.5533 | 0.82 | 0.2569 | 1.4526 | 0.82 | 0.7994 | 0.1405 | 0.0579 | | 0.0559 | 92.0 | 2300 | 0.5534 | 0.825 | 0.2570 | 1.4533 | 0.825 | 0.8051 | 0.1424 | 0.0577 | | 0.0559 | 93.0 | 2325 | 0.5535 | 0.82 | 0.2570 | 1.4527 | 0.82 | 0.7994 | 0.1399 | 0.0580 | | 0.0559 | 94.0 | 2350 | 0.5536 | 0.82 | 0.2571 | 1.4533 | 0.82 | 0.7994 | 0.1404 | 0.0577 | | 0.0559 | 95.0 | 2375 | 0.5536 | 0.82 | 0.2571 | 1.4547 | 0.82 | 0.7994 | 0.1400 | 0.0579 | | 0.0559 | 96.0 | 2400 | 0.5535 | 0.82 | 0.2570 | 1.4567 | 0.82 | 0.7994 | 0.1400 | 0.0578 | | 0.0559 | 97.0 | 2425 | 0.5536 | 0.82 | 0.2571 | 1.4523 | 0.82 | 0.7994 | 0.1404 | 0.0579 | | 0.0559 | 98.0 | 2450 | 0.5536 | 0.82 | 0.2571 | 1.4570 | 0.82 | 0.7994 | 0.1404 | 0.0578 | | 0.0559 | 99.0 | 2475 | 0.5536 | 0.82 | 0.2571 | 1.4570 | 0.82 | 0.7994 | 0.1404 | 0.0578 | | 0.0559 | 100.0 | 2500 | 0.5536 | 0.82 | 0.2571 | 1.4560 | 0.82 | 0.7994 | 0.1404 | 0.0578 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4137 - Accuracy: 0.83 - Brier Loss: 0.2631 - Nll: 1.5189 - F1 Micro: 0.83 - F1 Macro: 0.8172 - Ece: 0.2007 - Aurc: 0.0591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.6265 | 0.23 | 0.8647 | 5.1432 | 0.23 | 0.1847 | 0.2751 | 0.7516 | | No log | 2.0 | 50 | 1.0240 | 0.505 | 0.6074 | 2.7425 | 0.505 | 0.3980 | 0.2977 | 0.2705 | | No log | 3.0 | 75 | 0.8130 | 0.655 | 0.4809 | 2.4939 | 0.655 | 0.5584 | 0.2576 | 0.1502 | | No log | 4.0 | 100 | 0.6703 | 0.735 | 0.3867 | 1.3509 | 0.735 | 0.6895 | 0.2334 | 0.1109 | | No log | 5.0 | 125 | 0.6313 | 0.755 | 0.3420 | 1.2521 | 0.755 | 0.7207 | 0.2081 | 0.0789 | | No log | 6.0 | 150 | 0.6598 | 0.76 | 0.3543 | 1.4171 | 0.76 | 0.7103 | 0.2275 | 0.0886 | | No log | 7.0 | 175 | 0.5669 | 0.77 | 0.3368 | 1.5060 | 0.7700 | 0.7351 | 0.2247 | 0.0941 | | No log | 8.0 | 200 | 0.5486 | 0.775 | 0.3004 | 1.1511 | 0.775 | 0.7413 | 0.2252 | 0.0640 | | No log | 9.0 | 225 | 0.5456 | 0.795 | 0.3141 | 1.4663 | 0.795 | 0.7762 | 0.2198 | 0.0889 | | No log | 10.0 | 250 | 0.4954 | 0.82 | 0.2819 | 1.4644 | 0.82 | 0.7981 | 0.2150 | 0.0649 | | No log | 11.0 | 275 | 0.4804 | 0.805 | 0.2866 | 1.3705 | 0.805 | 0.7927 | 0.2078 | 0.0658 | | No log | 12.0 | 300 | 0.5234 | 0.785 | 0.3152 | 1.5290 | 0.785 | 0.7681 | 0.2149 | 0.0637 | | No log | 13.0 | 325 | 0.4701 | 0.815 | 0.2839 | 1.4490 | 0.815 | 0.8010 | 0.2315 | 0.0586 | | No log | 14.0 | 350 | 0.4859 | 0.795 | 0.2807 | 1.1224 | 0.795 | 0.7957 | 0.2170 | 0.0512 | | No log | 15.0 | 375 | 0.5580 | 0.79 | 0.3272 | 1.7539 | 0.79 | 0.7735 | 0.2376 | 0.0708 | | No log | 16.0 | 400 | 0.4918 | 0.8 | 0.2961 | 1.5112 | 0.8000 | 0.7988 | 0.1850 | 0.0568 | | No log | 17.0 | 425 | 0.4442 | 0.8 | 0.2846 | 1.6182 | 0.8000 | 0.7767 | 0.2083 | 0.0712 | | No log | 18.0 | 450 | 0.4460 | 0.82 | 0.2760 | 1.6839 | 0.82 | 0.8027 | 0.2127 | 0.0523 | | No log | 19.0 | 475 | 0.4423 | 0.825 | 0.2676 | 1.3774 | 0.825 | 0.8176 | 0.1853 | 0.0557 | | 0.4472 | 20.0 | 500 | 0.4998 | 0.81 | 0.2910 | 1.7711 | 0.81 | 0.8152 | 0.2181 | 0.0635 | | 0.4472 | 21.0 | 525 | 0.4579 | 0.83 | 0.2871 | 1.7025 | 0.83 | 0.8135 | 0.1927 | 0.0696 | | 0.4472 | 22.0 | 550 | 0.4421 | 0.825 | 0.2683 | 1.6453 | 0.825 | 0.8215 | 0.1929 | 0.0613 | | 0.4472 | 23.0 | 575 | 0.4368 | 0.8 | 0.2821 | 1.7298 | 0.8000 | 0.7684 | 0.2060 | 0.0771 | | 0.4472 | 24.0 | 600 | 0.4310 | 0.83 | 0.2689 | 1.4699 | 0.83 | 0.8163 | 0.2067 | 0.0556 | | 0.4472 | 25.0 | 625 | 0.4394 | 0.83 | 0.2751 | 1.5955 | 0.83 | 0.8166 | 0.2138 | 0.0681 | | 0.4472 | 26.0 | 650 | 0.4395 | 0.815 | 0.2786 | 1.6788 | 0.815 | 0.8033 | 0.2034 | 0.0643 | | 0.4472 | 27.0 | 675 | 0.4118 | 0.84 | 0.2578 | 1.5641 | 0.8400 | 0.8293 | 0.2024 | 0.0554 | | 0.4472 | 28.0 | 700 | 0.4273 | 0.82 | 0.2707 | 1.7118 | 0.82 | 0.8090 | 0.2133 | 0.0674 | | 0.4472 | 29.0 | 725 | 0.4207 | 0.835 | 0.2648 | 1.6469 | 0.835 | 0.8206 | 0.1948 | 0.0652 | | 0.4472 | 30.0 | 750 | 0.4172 | 0.825 | 0.2620 | 1.5024 | 0.825 | 0.8114 | 0.1833 | 0.0601 | | 0.4472 | 31.0 | 775 | 0.4148 | 0.825 | 0.2610 | 1.4994 | 0.825 | 0.8070 | 0.2052 | 0.0593 | | 0.4472 | 32.0 | 800 | 0.4148 | 0.825 | 0.2627 | 1.6293 | 0.825 | 0.8088 | 0.2080 | 0.0618 | | 0.4472 | 33.0 | 825 | 0.4159 | 0.825 | 0.2625 | 1.5069 | 0.825 | 0.8135 | 0.2082 | 0.0604 | | 0.4472 | 34.0 | 850 | 0.4168 | 0.825 | 0.2638 | 1.5770 | 0.825 | 0.8137 | 0.1888 | 0.0588 | | 0.4472 | 35.0 | 875 | 0.4181 | 0.82 | 0.2640 | 1.5404 | 0.82 | 0.8043 | 0.2145 | 0.0582 | | 0.4472 | 36.0 | 900 | 0.4154 | 0.83 | 0.2618 | 1.5719 | 0.83 | 0.8165 | 0.1965 | 0.0586 | | 0.4472 | 37.0 | 925 | 0.4160 | 0.825 | 0.2632 | 1.5840 | 0.825 | 0.8137 | 0.2003 | 0.0604 | | 0.4472 | 38.0 | 950 | 0.4133 | 0.83 | 0.2616 | 1.5711 | 0.83 | 0.8163 | 0.2040 | 0.0596 | | 0.4472 | 39.0 | 975 | 0.4167 | 0.825 | 0.2635 | 1.5210 | 0.825 | 0.8138 | 0.1930 | 0.0590 | | 0.0652 | 40.0 | 1000 | 0.4162 | 0.83 | 0.2630 | 1.6312 | 0.83 | 0.8163 | 0.1973 | 0.0593 | | 0.0652 | 41.0 | 1025 | 0.4144 | 0.83 | 0.2626 | 1.5787 | 0.83 | 0.8163 | 0.2068 | 0.0603 | | 0.0652 | 42.0 | 1050 | 0.4150 | 0.83 | 0.2631 | 1.5789 | 0.83 | 0.8163 | 0.1970 | 0.0588 | | 0.0652 | 43.0 | 1075 | 0.4158 | 0.825 | 0.2635 | 1.5833 | 0.825 | 0.8138 | 0.1927 | 0.0597 | | 0.0652 | 44.0 | 1100 | 0.4132 | 0.83 | 0.2622 | 1.5130 | 0.83 | 0.8163 | 0.2030 | 0.0593 | | 0.0652 | 45.0 | 1125 | 0.4146 | 0.83 | 0.2630 | 1.6312 | 0.83 | 0.8165 | 0.2010 | 0.0587 | | 0.0652 | 46.0 | 1150 | 0.4138 | 0.825 | 0.2624 | 1.5301 | 0.825 | 0.8135 | 0.2065 | 0.0587 | | 0.0652 | 47.0 | 1175 | 0.4142 | 0.83 | 0.2627 | 1.6292 | 0.83 | 0.8163 | 0.1984 | 0.0591 | | 0.0652 | 48.0 | 1200 | 0.4146 | 0.825 | 0.2629 | 1.5735 | 0.825 | 0.8137 | 0.1998 | 0.0589 | | 0.0652 | 49.0 | 1225 | 0.4143 | 0.83 | 0.2630 | 1.5276 | 0.83 | 0.8163 | 0.2116 | 0.0599 | | 0.0652 | 50.0 | 1250 | 0.4140 | 0.83 | 0.2628 | 1.5705 | 0.83 | 0.8163 | 0.1966 | 0.0590 | | 0.0652 | 51.0 | 1275 | 0.4152 | 0.825 | 0.2637 | 1.5747 | 0.825 | 0.8138 | 0.1835 | 0.0593 | | 0.0652 | 52.0 | 1300 | 0.4145 | 0.825 | 0.2629 | 1.5796 | 0.825 | 0.8137 | 0.1926 | 0.0593 | | 0.0652 | 53.0 | 1325 | 0.4147 | 0.825 | 0.2631 | 1.6323 | 0.825 | 0.8138 | 0.1838 | 0.0588 | | 0.0652 | 54.0 | 1350 | 0.4141 | 0.83 | 0.2628 | 1.5763 | 0.83 | 0.8163 | 0.2035 | 0.0592 | | 0.0652 | 55.0 | 1375 | 0.4137 | 0.83 | 0.2630 | 1.5751 | 0.83 | 0.8163 | 0.2042 | 0.0590 | | 0.0652 | 56.0 | 1400 | 0.4145 | 0.83 | 0.2632 | 1.6307 | 0.83 | 0.8163 | 0.1981 | 0.0588 | | 0.0652 | 57.0 | 1425 | 0.4149 | 0.825 | 0.2634 | 1.5225 | 0.825 | 0.8137 | 0.2008 | 0.0589 | | 0.0652 | 58.0 | 1450 | 0.4146 | 0.83 | 0.2634 | 1.5725 | 0.83 | 0.8163 | 0.2121 | 0.0589 | | 0.0652 | 59.0 | 1475 | 0.4142 | 0.83 | 0.2632 | 1.5214 | 0.83 | 0.8163 | 0.2028 | 0.0590 | | 0.0614 | 60.0 | 1500 | 0.4145 | 0.83 | 0.2634 | 1.5237 | 0.83 | 0.8163 | 0.1981 | 0.0585 | | 0.0614 | 61.0 | 1525 | 0.4142 | 0.83 | 0.2630 | 1.5710 | 0.83 | 0.8163 | 0.2070 | 0.0591 | | 0.0614 | 62.0 | 1550 | 0.4139 | 0.825 | 0.2631 | 1.5733 | 0.825 | 0.8135 | 0.1986 | 0.0594 | | 0.0614 | 63.0 | 1575 | 0.4139 | 0.825 | 0.2630 | 1.5813 | 0.825 | 0.8136 | 0.1984 | 0.0593 | | 0.0614 | 64.0 | 1600 | 0.4138 | 0.83 | 0.2629 | 1.5729 | 0.83 | 0.8163 | 0.2035 | 0.0590 | | 0.0614 | 65.0 | 1625 | 0.4139 | 0.825 | 0.2629 | 1.5715 | 0.825 | 0.8136 | 0.2026 | 0.0593 | | 0.0614 | 66.0 | 1650 | 0.4136 | 0.825 | 0.2629 | 1.5768 | 0.825 | 0.8135 | 0.1988 | 0.0592 | | 0.0614 | 67.0 | 1675 | 0.4139 | 0.825 | 0.2629 | 1.5709 | 0.825 | 0.8135 | 0.1987 | 0.0593 | | 0.0614 | 68.0 | 1700 | 0.4143 | 0.825 | 0.2633 | 1.5744 | 0.825 | 0.8138 | 0.1896 | 0.0595 | | 0.0614 | 69.0 | 1725 | 0.4142 | 0.825 | 0.2632 | 1.5752 | 0.825 | 0.8138 | 0.1896 | 0.0593 | | 0.0614 | 70.0 | 1750 | 0.4142 | 0.825 | 0.2632 | 1.5769 | 0.825 | 0.8138 | 0.1879 | 0.0594 | | 0.0614 | 71.0 | 1775 | 0.4138 | 0.83 | 0.2630 | 1.5734 | 0.83 | 0.8163 | 0.2073 | 0.0588 | | 0.0614 | 72.0 | 1800 | 0.4140 | 0.825 | 0.2631 | 1.5734 | 0.825 | 0.8138 | 0.1977 | 0.0593 | | 0.0614 | 73.0 | 1825 | 0.4135 | 0.83 | 0.2629 | 1.5711 | 0.83 | 0.8163 | 0.2035 | 0.0589 | | 0.0614 | 74.0 | 1850 | 0.4140 | 0.83 | 0.2632 | 1.5717 | 0.83 | 0.8163 | 0.2038 | 0.0590 | | 0.0614 | 75.0 | 1875 | 0.4141 | 0.825 | 0.2633 | 1.5205 | 0.825 | 0.8138 | 0.1838 | 0.0593 | | 0.0614 | 76.0 | 1900 | 0.4138 | 0.825 | 0.2631 | 1.5218 | 0.825 | 0.8137 | 0.1838 | 0.0595 | | 0.0614 | 77.0 | 1925 | 0.4134 | 0.825 | 0.2628 | 1.5710 | 0.825 | 0.8135 | 0.1937 | 0.0591 | | 0.0614 | 78.0 | 1950 | 0.4135 | 0.83 | 0.2629 | 1.5688 | 0.83 | 0.8163 | 0.2067 | 0.0588 | | 0.0614 | 79.0 | 1975 | 0.4138 | 0.825 | 0.2631 | 1.5143 | 0.825 | 0.8137 | 0.1942 | 0.0592 | | 0.0613 | 80.0 | 2000 | 0.4134 | 0.825 | 0.2628 | 1.5152 | 0.825 | 0.8135 | 0.1939 | 0.0591 | | 0.0613 | 81.0 | 2025 | 0.4139 | 0.825 | 0.2632 | 1.5144 | 0.825 | 0.8136 | 0.1903 | 0.0593 | | 0.0613 | 82.0 | 2050 | 0.4139 | 0.83 | 0.2632 | 1.5242 | 0.83 | 0.8163 | 0.1894 | 0.0589 | | 0.0613 | 83.0 | 2075 | 0.4138 | 0.825 | 0.2631 | 1.5159 | 0.825 | 0.8136 | 0.2014 | 0.0594 | | 0.0613 | 84.0 | 2100 | 0.4137 | 0.825 | 0.2631 | 1.5707 | 0.825 | 0.8136 | 0.1954 | 0.0592 | | 0.0613 | 85.0 | 2125 | 0.4136 | 0.825 | 0.2630 | 1.5252 | 0.825 | 0.8136 | 0.1878 | 0.0592 | | 0.0613 | 86.0 | 2150 | 0.4138 | 0.83 | 0.2630 | 1.5186 | 0.83 | 0.8172 | 0.2024 | 0.0588 | | 0.0613 | 87.0 | 2175 | 0.4139 | 0.825 | 0.2632 | 1.5201 | 0.825 | 0.8138 | 0.1927 | 0.0592 | | 0.0613 | 88.0 | 2200 | 0.4138 | 0.83 | 0.2631 | 1.5285 | 0.83 | 0.8172 | 0.1897 | 0.0591 | | 0.0613 | 89.0 | 2225 | 0.4137 | 0.825 | 0.2631 | 1.5185 | 0.825 | 0.8136 | 0.1956 | 0.0593 | | 0.0613 | 90.0 | 2250 | 0.4137 | 0.83 | 0.2631 | 1.5212 | 0.83 | 0.8172 | 0.2007 | 0.0591 | | 0.0613 | 91.0 | 2275 | 0.4138 | 0.825 | 0.2631 | 1.5185 | 0.825 | 0.8138 | 0.1915 | 0.0593 | | 0.0613 | 92.0 | 2300 | 0.4136 | 0.83 | 0.2630 | 1.5174 | 0.83 | 0.8172 | 0.2067 | 0.0590 | | 0.0613 | 93.0 | 2325 | 0.4137 | 0.83 | 0.2631 | 1.5204 | 0.83 | 0.8172 | 0.1939 | 0.0591 | | 0.0613 | 94.0 | 2350 | 0.4137 | 0.83 | 0.2631 | 1.5255 | 0.83 | 0.8172 | 0.2007 | 0.0592 | | 0.0613 | 95.0 | 2375 | 0.4137 | 0.83 | 0.2631 | 1.5161 | 0.83 | 0.8172 | 0.1966 | 0.0591 | | 0.0613 | 96.0 | 2400 | 0.4136 | 0.83 | 0.2630 | 1.5180 | 0.83 | 0.8172 | 0.2007 | 0.0590 | | 0.0613 | 97.0 | 2425 | 0.4137 | 0.83 | 0.2631 | 1.5176 | 0.83 | 0.8172 | 0.1966 | 0.0591 | | 0.0613 | 98.0 | 2450 | 0.4137 | 0.83 | 0.2631 | 1.5194 | 0.83 | 0.8172 | 0.1966 | 0.0590 | | 0.0613 | 99.0 | 2475 | 0.4137 | 0.83 | 0.2631 | 1.5195 | 0.83 | 0.8172 | 0.2005 | 0.0591 | | 0.0613 | 100.0 | 2500 | 0.4137 | 0.83 | 0.2631 | 1.5189 | 0.83 | 0.8172 | 0.2007 | 0.0591 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.7

# 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4858 - Accuracy: 0.845 - Brier Loss: 0.2480 - Nll: 1.6086 - F1 Micro: 0.845 - F1 Macro: 0.8265 - Ece: 0.1885 - Aurc: 0.0556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.8068 | 0.235 | 0.8615 | 4.8212 | 0.235 | 0.1982 | 0.2851 | 0.7569 | | No log | 2.0 | 50 | 1.1592 | 0.525 | 0.6018 | 2.7552 | 0.525 | 0.4251 | 0.2868 | 0.2632 | | No log | 3.0 | 75 | 0.9152 | 0.66 | 0.4661 | 2.3855 | 0.66 | 0.5569 | 0.2425 | 0.1423 | | No log | 4.0 | 100 | 0.7630 | 0.72 | 0.3764 | 1.5145 | 0.72 | 0.6868 | 0.2192 | 0.1030 | | No log | 5.0 | 125 | 0.7141 | 0.77 | 0.3437 | 1.5182 | 0.7700 | 0.7351 | 0.2394 | 0.0885 | | No log | 6.0 | 150 | 0.7497 | 0.765 | 0.3420 | 1.4392 | 0.765 | 0.7401 | 0.2226 | 0.0850 | | No log | 7.0 | 175 | 0.6465 | 0.8 | 0.3252 | 1.4412 | 0.8000 | 0.7688 | 0.2262 | 0.0878 | | No log | 8.0 | 200 | 0.6100 | 0.79 | 0.3013 | 1.1306 | 0.79 | 0.7626 | 0.2135 | 0.0732 | | No log | 9.0 | 225 | 0.7123 | 0.78 | 0.3429 | 1.5207 | 0.78 | 0.7679 | 0.2302 | 0.1021 | | No log | 10.0 | 250 | 0.5881 | 0.83 | 0.2873 | 1.3827 | 0.83 | 0.8274 | 0.2288 | 0.0503 | | No log | 11.0 | 275 | 0.6079 | 0.805 | 0.2958 | 1.3646 | 0.805 | 0.7659 | 0.1826 | 0.0795 | | No log | 12.0 | 300 | 0.5697 | 0.815 | 0.2829 | 1.4925 | 0.815 | 0.7846 | 0.1783 | 0.0637 | | No log | 13.0 | 325 | 0.5641 | 0.815 | 0.2834 | 1.7183 | 0.815 | 0.7950 | 0.1949 | 0.0587 | | No log | 14.0 | 350 | 0.5287 | 0.815 | 0.2615 | 1.1254 | 0.815 | 0.7990 | 0.1903 | 0.0508 | | No log | 15.0 | 375 | 0.5220 | 0.82 | 0.2627 | 1.5375 | 0.82 | 0.8024 | 0.2039 | 0.0477 | | No log | 16.0 | 400 | 0.5982 | 0.8 | 0.3044 | 1.9197 | 0.8000 | 0.7762 | 0.2026 | 0.0664 | | No log | 17.0 | 425 | 0.5165 | 0.83 | 0.2653 | 1.6907 | 0.83 | 0.8120 | 0.2057 | 0.0505 | | No log | 18.0 | 450 | 0.5345 | 0.84 | 0.2695 | 1.8508 | 0.8400 | 0.8259 | 0.1987 | 0.0505 | | No log | 19.0 | 475 | 0.5325 | 0.83 | 0.2670 | 1.3625 | 0.83 | 0.8226 | 0.1937 | 0.0450 | | 0.4632 | 20.0 | 500 | 0.5102 | 0.835 | 0.2561 | 1.4043 | 0.835 | 0.8179 | 0.1957 | 0.0568 | | 0.4632 | 21.0 | 525 | 0.5171 | 0.83 | 0.2605 | 1.6651 | 0.83 | 0.8199 | 0.1834 | 0.0508 | | 0.4632 | 22.0 | 550 | 0.5294 | 0.825 | 0.2594 | 1.6140 | 0.825 | 0.7987 | 0.1745 | 0.0559 | | 0.4632 | 23.0 | 575 | 0.5234 | 0.835 | 0.2633 | 1.7530 | 0.835 | 0.8190 | 0.1937 | 0.0534 | | 0.4632 | 24.0 | 600 | 0.4999 | 0.845 | 0.2469 | 1.4458 | 0.845 | 0.8181 | 0.1650 | 0.0580 | | 0.4632 | 25.0 | 625 | 0.5025 | 0.825 | 0.2571 | 1.6308 | 0.825 | 0.8111 | 0.1712 | 0.0493 | | 0.4632 | 26.0 | 650 | 0.5058 | 0.83 | 0.2563 | 1.5906 | 0.83 | 0.8074 | 0.1828 | 0.0566 | | 0.4632 | 27.0 | 675 | 0.4873 | 0.835 | 0.2476 | 1.4946 | 0.835 | 0.8148 | 0.1727 | 0.0584 | | 0.4632 | 28.0 | 700 | 0.4894 | 0.835 | 0.2486 | 1.6165 | 0.835 | 0.8153 | 0.1538 | 0.0573 | | 0.4632 | 29.0 | 725 | 0.4834 | 0.835 | 0.2455 | 1.6185 | 0.835 | 0.8178 | 0.1869 | 0.0523 | | 0.4632 | 30.0 | 750 | 0.4842 | 0.835 | 0.2460 | 1.4898 | 0.835 | 0.8115 | 0.1683 | 0.0524 | | 0.4632 | 31.0 | 775 | 0.4869 | 0.835 | 0.2480 | 1.5350 | 0.835 | 0.8144 | 0.1796 | 0.0543 | | 0.4632 | 32.0 | 800 | 0.4865 | 0.835 | 0.2473 | 1.6052 | 0.835 | 0.8179 | 0.1792 | 0.0560 | | 0.4632 | 33.0 | 825 | 0.4856 | 0.83 | 0.2479 | 1.5508 | 0.83 | 0.8118 | 0.1773 | 0.0550 | | 0.4632 | 34.0 | 850 | 0.4861 | 0.835 | 0.2477 | 1.5486 | 0.835 | 0.8144 | 0.1779 | 0.0568 | | 0.4632 | 35.0 | 875 | 0.4873 | 0.835 | 0.2484 | 1.6076 | 0.835 | 0.8179 | 0.1803 | 0.0556 | | 0.4632 | 36.0 | 900 | 0.4867 | 0.84 | 0.2480 | 1.5438 | 0.8400 | 0.8205 | 0.1791 | 0.0561 | | 0.4632 | 37.0 | 925 | 0.4863 | 0.84 | 0.2480 | 1.6035 | 0.8400 | 0.8205 | 0.1768 | 0.0558 | | 0.4632 | 38.0 | 950 | 0.4864 | 0.835 | 0.2479 | 1.5469 | 0.835 | 0.8144 | 0.1876 | 0.0563 | | 0.4632 | 39.0 | 975 | 0.4866 | 0.835 | 0.2483 | 1.5996 | 0.835 | 0.8144 | 0.1759 | 0.0546 | | 0.0757 | 40.0 | 1000 | 0.4869 | 0.84 | 0.2480 | 1.5412 | 0.8400 | 0.8205 | 0.1726 | 0.0550 | | 0.0757 | 41.0 | 1025 | 0.4869 | 0.84 | 0.2482 | 1.6060 | 0.8400 | 0.8205 | 0.1848 | 0.0545 | | 0.0757 | 42.0 | 1050 | 0.4872 | 0.835 | 0.2485 | 1.6080 | 0.835 | 0.8179 | 0.1812 | 0.0558 | | 0.0757 | 43.0 | 1075 | 0.4863 | 0.84 | 0.2476 | 1.6046 | 0.8400 | 0.8205 | 0.1773 | 0.0538 | | 0.0757 | 44.0 | 1100 | 0.4866 | 0.84 | 0.2482 | 1.6117 | 0.8400 | 0.8205 | 0.1719 | 0.0557 | | 0.0757 | 45.0 | 1125 | 0.4858 | 0.835 | 0.2478 | 1.6046 | 0.835 | 0.8144 | 0.1728 | 0.0554 | | 0.0757 | 46.0 | 1150 | 0.4860 | 0.845 | 0.2478 | 1.6034 | 0.845 | 0.8265 | 0.2000 | 0.0551 | | 0.0757 | 47.0 | 1175 | 0.4863 | 0.845 | 0.2480 | 1.5422 | 0.845 | 0.8265 | 0.1918 | 0.0560 | | 0.0757 | 48.0 | 1200 | 0.4871 | 0.84 | 0.2486 | 1.5468 | 0.8400 | 0.8205 | 0.1795 | 0.0558 | | 0.0757 | 49.0 | 1225 | 0.4855 | 0.835 | 0.2475 | 1.5511 | 0.835 | 0.8144 | 0.1814 | 0.0555 | | 0.0757 | 50.0 | 1250 | 0.4854 | 0.84 | 0.2477 | 1.5488 | 0.8400 | 0.8205 | 0.1830 | 0.0549 | | 0.0757 | 51.0 | 1275 | 0.4854 | 0.835 | 0.2475 | 1.5505 | 0.835 | 0.8144 | 0.1879 | 0.0547 | | 0.0757 | 52.0 | 1300 | 0.4867 | 0.845 | 0.2483 | 1.6045 | 0.845 | 0.8265 | 0.1700 | 0.0560 | | 0.0757 | 53.0 | 1325 | 0.4849 | 0.835 | 0.2473 | 1.6070 | 0.835 | 0.8179 | 0.1736 | 0.0546 | | 0.0757 | 54.0 | 1350 | 0.4860 | 0.845 | 0.2479 | 1.5524 | 0.845 | 0.8265 | 0.1762 | 0.0549 | | 0.0757 | 55.0 | 1375 | 0.4863 | 0.84 | 0.2481 | 1.5535 | 0.8400 | 0.8205 | 0.1864 | 0.0560 | | 0.0757 | 56.0 | 1400 | 0.4871 | 0.84 | 0.2484 | 1.6074 | 0.8400 | 0.8205 | 0.1821 | 0.0562 | | 0.0757 | 57.0 | 1425 | 0.4868 | 0.845 | 0.2483 | 1.5544 | 0.845 | 0.8265 | 0.1832 | 0.0557 | | 0.0757 | 58.0 | 1450 | 0.4848 | 0.845 | 0.2474 | 1.5455 | 0.845 | 0.8265 | 0.1770 | 0.0551 | | 0.0757 | 59.0 | 1475 | 0.4861 | 0.845 | 0.2481 | 1.6056 | 0.845 | 0.8265 | 0.1824 | 0.0552 | | 0.0718 | 60.0 | 1500 | 0.4860 | 0.845 | 0.2481 | 1.5485 | 0.845 | 0.8265 | 0.1874 | 0.0555 | | 0.0718 | 61.0 | 1525 | 0.4859 | 0.845 | 0.2480 | 1.5471 | 0.845 | 0.8265 | 0.1833 | 0.0545 | | 0.0718 | 62.0 | 1550 | 0.4855 | 0.84 | 0.2477 | 1.5557 | 0.8400 | 0.8205 | 0.1892 | 0.0556 | | 0.0718 | 63.0 | 1575 | 0.4856 | 0.845 | 0.2479 | 1.5511 | 0.845 | 0.8265 | 0.1804 | 0.0555 | | 0.0718 | 64.0 | 1600 | 0.4863 | 0.845 | 0.2482 | 1.5482 | 0.845 | 0.8265 | 0.1761 | 0.0559 | | 0.0718 | 65.0 | 1625 | 0.4857 | 0.845 | 0.2478 | 1.5503 | 0.845 | 0.8265 | 0.1944 | 0.0559 | | 0.0718 | 66.0 | 1650 | 0.4858 | 0.845 | 0.2480 | 1.5463 | 0.845 | 0.8265 | 0.1832 | 0.0556 | | 0.0718 | 67.0 | 1675 | 0.4855 | 0.845 | 0.2478 | 1.5493 | 0.845 | 0.8265 | 0.1947 | 0.0558 | | 0.0718 | 68.0 | 1700 | 0.4858 | 0.845 | 0.2480 | 1.5482 | 0.845 | 0.8265 | 0.1831 | 0.0553 | | 0.0718 | 69.0 | 1725 | 0.4863 | 0.845 | 0.2483 | 1.5470 | 0.845 | 0.8265 | 0.1904 | 0.0558 | | 0.0718 | 70.0 | 1750 | 0.4859 | 0.845 | 0.2479 | 1.5449 | 0.845 | 0.8265 | 0.1836 | 0.0556 | | 0.0718 | 71.0 | 1775 | 0.4865 | 0.845 | 0.2484 | 1.5476 | 0.845 | 0.8265 | 0.1999 | 0.0557 | | 0.0718 | 72.0 | 1800 | 0.4855 | 0.845 | 0.2479 | 1.5472 | 0.845 | 0.8265 | 0.1875 | 0.0555 | | 0.0718 | 73.0 | 1825 | 0.4859 | 0.845 | 0.2481 | 1.5514 | 0.845 | 0.8265 | 0.1946 | 0.0557 | | 0.0718 | 74.0 | 1850 | 0.4863 | 0.845 | 0.2483 | 1.6121 | 0.845 | 0.8265 | 0.1904 | 0.0559 | | 0.0718 | 75.0 | 1875 | 0.4861 | 0.845 | 0.2482 | 1.6150 | 0.845 | 0.8265 | 0.1807 | 0.0559 | | 0.0718 | 76.0 | 1900 | 0.4856 | 0.845 | 0.2479 | 1.5755 | 0.845 | 0.8265 | 0.1877 | 0.0555 | | 0.0718 | 77.0 | 1925 | 0.4857 | 0.845 | 0.2480 | 1.5550 | 0.845 | 0.8265 | 0.1880 | 0.0557 | | 0.0718 | 78.0 | 1950 | 0.4856 | 0.845 | 0.2479 | 1.6081 | 0.845 | 0.8265 | 0.1877 | 0.0553 | | 0.0718 | 79.0 | 1975 | 0.4858 | 0.845 | 0.2481 | 1.5526 | 0.845 | 0.8265 | 0.1878 | 0.0556 | | 0.0718 | 80.0 | 2000 | 0.4860 | 0.845 | 0.2481 | 1.5562 | 0.845 | 0.8265 | 0.1881 | 0.0556 | | 0.0718 | 81.0 | 2025 | 0.4860 | 0.845 | 0.2481 | 1.5569 | 0.845 | 0.8265 | 0.1837 | 0.0553 | | 0.0718 | 82.0 | 2050 | 0.4863 | 0.845 | 0.2483 | 1.5522 | 0.845 | 0.8265 | 0.1937 | 0.0558 | | 0.0718 | 83.0 | 2075 | 0.4859 | 0.845 | 0.2481 | 1.5539 | 0.845 | 0.8265 | 0.1878 | 0.0558 | | 0.0718 | 84.0 | 2100 | 0.4858 | 0.845 | 0.2480 | 1.5575 | 0.845 | 0.8265 | 0.1877 | 0.0557 | | 0.0718 | 85.0 | 2125 | 0.4859 | 0.845 | 0.2481 | 1.5652 | 0.845 | 0.8265 | 0.1884 | 0.0558 | | 0.0718 | 86.0 | 2150 | 0.4858 | 0.845 | 0.2480 | 1.5511 | 0.845 | 0.8265 | 0.1840 | 0.0555 | | 0.0718 | 87.0 | 2175 | 0.4858 | 0.845 | 0.2480 | 1.5571 | 0.845 | 0.8265 | 0.1883 | 0.0556 | | 0.0718 | 88.0 | 2200 | 0.4859 | 0.845 | 0.2481 | 1.5554 | 0.845 | 0.8265 | 0.1842 | 0.0556 | | 0.0718 | 89.0 | 2225 | 0.4858 | 0.845 | 0.2480 | 1.5627 | 0.845 | 0.8265 | 0.1884 | 0.0557 | | 0.0718 | 90.0 | 2250 | 0.4856 | 0.845 | 0.2479 | 1.6086 | 0.845 | 0.8265 | 0.1883 | 0.0557 | | 0.0718 | 91.0 | 2275 | 0.4859 | 0.845 | 0.2481 | 1.5552 | 0.845 | 0.8265 | 0.1884 | 0.0558 | | 0.0718 | 92.0 | 2300 | 0.4857 | 0.845 | 0.2479 | 1.6085 | 0.845 | 0.8265 | 0.1884 | 0.0557 | | 0.0718 | 93.0 | 2325 | 0.4858 | 0.845 | 0.2480 | 1.5633 | 0.845 | 0.8265 | 0.1884 | 0.0556 | | 0.0718 | 94.0 | 2350 | 0.4856 | 0.845 | 0.2480 | 1.6085 | 0.845 | 0.8265 | 0.1884 | 0.0556 | | 0.0718 | 95.0 | 2375 | 0.4857 | 0.845 | 0.2480 | 1.6082 | 0.845 | 0.8265 | 0.1884 | 0.0556 | | 0.0718 | 96.0 | 2400 | 0.4857 | 0.845 | 0.2480 | 1.6088 | 0.845 | 0.8265 | 0.1884 | 0.0556 | | 0.0718 | 97.0 | 2425 | 0.4858 | 0.845 | 0.2480 | 1.6082 | 0.845 | 0.8265 | 0.1885 | 0.0556 | | 0.0718 | 98.0 | 2450 | 0.4857 | 0.845 | 0.2480 | 1.6084 | 0.845 | 0.8265 | 0.1884 | 0.0556 | | 0.0718 | 99.0 | 2475 | 0.4858 | 0.845 | 0.2480 | 1.6086 | 0.845 | 0.8265 | 0.1885 | 0.0556 | | 0.0718 | 100.0 | 2500 | 0.4858 | 0.845 | 0.2480 | 1.6086 | 0.845 | 0.8265 | 0.1885 | 0.0556 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.9

# 6_e_200-tiny_tobacco3482_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5528 - Accuracy: 0.84 - Brier Loss: 0.2493 - Nll: 1.6062 - F1 Micro: 0.8400 - F1 Macro: 0.8256 - Ece: 0.1626 - Aurc: 0.0556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.9962 | 0.24 | 0.8634 | 4.6099 | 0.24 | 0.1987 | 0.2924 | 0.7628 | | No log | 2.0 | 50 | 1.2785 | 0.545 | 0.5960 | 2.6707 | 0.545 | 0.4456 | 0.2808 | 0.2569 | | No log | 3.0 | 75 | 0.9740 | 0.685 | 0.4455 | 2.0688 | 0.685 | 0.5937 | 0.2231 | 0.1314 | | No log | 4.0 | 100 | 0.8052 | 0.75 | 0.3628 | 1.5271 | 0.75 | 0.7144 | 0.2031 | 0.0915 | | No log | 5.0 | 125 | 0.7531 | 0.77 | 0.3324 | 1.5448 | 0.7700 | 0.7348 | 0.1868 | 0.0829 | | No log | 6.0 | 150 | 0.9730 | 0.735 | 0.4050 | 1.5875 | 0.735 | 0.7229 | 0.1899 | 0.1118 | | No log | 7.0 | 175 | 0.6437 | 0.83 | 0.2790 | 1.3915 | 0.83 | 0.7996 | 0.1967 | 0.0612 | | No log | 8.0 | 200 | 0.6670 | 0.78 | 0.2984 | 1.2128 | 0.78 | 0.7429 | 0.1701 | 0.0716 | | No log | 9.0 | 225 | 0.6012 | 0.845 | 0.2521 | 1.4007 | 0.845 | 0.8208 | 0.1573 | 0.0581 | | No log | 10.0 | 250 | 0.6754 | 0.795 | 0.3063 | 1.4194 | 0.795 | 0.7638 | 0.2036 | 0.0759 | | No log | 11.0 | 275 | 0.5950 | 0.83 | 0.2554 | 1.1558 | 0.83 | 0.8053 | 0.1877 | 0.0529 | | No log | 12.0 | 300 | 0.7061 | 0.79 | 0.3153 | 1.6781 | 0.79 | 0.7676 | 0.1879 | 0.0848 | | No log | 13.0 | 325 | 0.6349 | 0.805 | 0.2806 | 1.3102 | 0.805 | 0.7767 | 0.1523 | 0.0667 | | No log | 14.0 | 350 | 0.5973 | 0.82 | 0.2677 | 1.5498 | 0.82 | 0.8020 | 0.1734 | 0.0567 | | No log | 15.0 | 375 | 0.6186 | 0.825 | 0.2792 | 1.3816 | 0.825 | 0.8170 | 0.1558 | 0.0672 | | No log | 16.0 | 400 | 0.5694 | 0.815 | 0.2662 | 1.1759 | 0.815 | 0.7962 | 0.1675 | 0.0559 | | No log | 17.0 | 425 | 0.5993 | 0.825 | 0.2793 | 1.2547 | 0.825 | 0.8112 | 0.1822 | 0.0647 | | No log | 18.0 | 450 | 0.6333 | 0.815 | 0.2844 | 1.6540 | 0.815 | 0.8024 | 0.1562 | 0.0622 | | No log | 19.0 | 475 | 0.5208 | 0.845 | 0.2349 | 1.2879 | 0.845 | 0.8155 | 0.1553 | 0.0494 | | 0.4544 | 20.0 | 500 | 0.5412 | 0.86 | 0.2438 | 1.6726 | 0.8600 | 0.8465 | 0.1531 | 0.0485 | | 0.4544 | 21.0 | 525 | 0.6171 | 0.825 | 0.2775 | 1.9997 | 0.825 | 0.8183 | 0.1464 | 0.0549 | | 0.4544 | 22.0 | 550 | 0.5479 | 0.84 | 0.2447 | 1.5015 | 0.8400 | 0.8263 | 0.1481 | 0.0680 | | 0.4544 | 23.0 | 575 | 0.5508 | 0.835 | 0.2491 | 1.8095 | 0.835 | 0.8209 | 0.1616 | 0.0469 | | 0.4544 | 24.0 | 600 | 0.5597 | 0.825 | 0.2577 | 1.6676 | 0.825 | 0.8077 | 0.1572 | 0.0486 | | 0.4544 | 25.0 | 625 | 0.5505 | 0.835 | 0.2535 | 1.6085 | 0.835 | 0.8166 | 0.1664 | 0.0524 | | 0.4544 | 26.0 | 650 | 0.5347 | 0.84 | 0.2442 | 1.4694 | 0.8400 | 0.8288 | 0.1825 | 0.0505 | | 0.4544 | 27.0 | 675 | 0.5333 | 0.84 | 0.2418 | 1.5809 | 0.8400 | 0.8280 | 0.1634 | 0.0521 | | 0.4544 | 28.0 | 700 | 0.5417 | 0.84 | 0.2471 | 1.5289 | 0.8400 | 0.8231 | 0.1500 | 0.0503 | | 0.4544 | 29.0 | 725 | 0.5369 | 0.845 | 0.2434 | 1.5333 | 0.845 | 0.8318 | 0.1690 | 0.0523 | | 0.4544 | 30.0 | 750 | 0.5396 | 0.84 | 0.2448 | 1.5269 | 0.8400 | 0.8260 | 0.1689 | 0.0534 | | 0.4544 | 31.0 | 775 | 0.5411 | 0.845 | 0.2459 | 1.5325 | 0.845 | 0.8289 | 0.1524 | 0.0514 | | 0.4544 | 32.0 | 800 | 0.5429 | 0.845 | 0.2456 | 1.5239 | 0.845 | 0.8318 | 0.1550 | 0.0527 | | 0.4544 | 33.0 | 825 | 0.5445 | 0.84 | 0.2468 | 1.5275 | 0.8400 | 0.8231 | 0.1626 | 0.0535 | | 0.4544 | 34.0 | 850 | 0.5432 | 0.845 | 0.2461 | 1.5210 | 0.845 | 0.8289 | 0.1557 | 0.0533 | | 0.4544 | 35.0 | 875 | 0.5438 | 0.845 | 0.2459 | 1.5269 | 0.845 | 0.8318 | 0.1564 | 0.0533 | | 0.4544 | 36.0 | 900 | 0.5451 | 0.845 | 0.2466 | 1.5262 | 0.845 | 0.8289 | 0.1610 | 0.0541 | | 0.4544 | 37.0 | 925 | 0.5415 | 0.85 | 0.2448 | 1.5254 | 0.85 | 0.8348 | 0.1667 | 0.0528 | | 0.4544 | 38.0 | 950 | 0.5447 | 0.845 | 0.2461 | 1.5367 | 0.845 | 0.8318 | 0.1519 | 0.0535 | | 0.4544 | 39.0 | 975 | 0.5437 | 0.85 | 0.2454 | 1.5223 | 0.85 | 0.8348 | 0.1605 | 0.0536 | | 0.0607 | 40.0 | 1000 | 0.5445 | 0.845 | 0.2460 | 1.5252 | 0.845 | 0.8318 | 0.1610 | 0.0539 | | 0.0607 | 41.0 | 1025 | 0.5460 | 0.845 | 0.2465 | 1.5925 | 0.845 | 0.8318 | 0.1416 | 0.0541 | | 0.0607 | 42.0 | 1050 | 0.5466 | 0.84 | 0.2467 | 1.5304 | 0.8400 | 0.8260 | 0.1555 | 0.0542 | | 0.0607 | 43.0 | 1075 | 0.5458 | 0.84 | 0.2464 | 1.5272 | 0.8400 | 0.8231 | 0.1633 | 0.0539 | | 0.0607 | 44.0 | 1100 | 0.5460 | 0.85 | 0.2464 | 1.5459 | 0.85 | 0.8377 | 0.1534 | 0.0550 | | 0.0607 | 45.0 | 1125 | 0.5464 | 0.85 | 0.2465 | 1.5390 | 0.85 | 0.8377 | 0.1471 | 0.0544 | | 0.0607 | 46.0 | 1150 | 0.5462 | 0.85 | 0.2465 | 1.5972 | 0.85 | 0.8377 | 0.1549 | 0.0540 | | 0.0607 | 47.0 | 1175 | 0.5475 | 0.85 | 0.2472 | 1.5910 | 0.85 | 0.8377 | 0.1592 | 0.0546 | | 0.0607 | 48.0 | 1200 | 0.5482 | 0.845 | 0.2475 | 1.5943 | 0.845 | 0.8294 | 0.1548 | 0.0545 | | 0.0607 | 49.0 | 1225 | 0.5475 | 0.845 | 0.2471 | 1.5922 | 0.845 | 0.8294 | 0.1534 | 0.0545 | | 0.0607 | 50.0 | 1250 | 0.5476 | 0.85 | 0.2470 | 1.5908 | 0.85 | 0.8377 | 0.1539 | 0.0545 | | 0.0607 | 51.0 | 1275 | 0.5480 | 0.845 | 0.2471 | 1.5990 | 0.845 | 0.8322 | 0.1545 | 0.0547 | | 0.0607 | 52.0 | 1300 | 0.5479 | 0.85 | 0.2469 | 1.5917 | 0.85 | 0.8348 | 0.1688 | 0.0547 | | 0.0607 | 53.0 | 1325 | 0.5479 | 0.845 | 0.2472 | 1.6052 | 0.845 | 0.8322 | 0.1545 | 0.0543 | | 0.0607 | 54.0 | 1350 | 0.5490 | 0.85 | 0.2477 | 1.5948 | 0.85 | 0.8348 | 0.1610 | 0.0545 | | 0.0607 | 55.0 | 1375 | 0.5489 | 0.85 | 0.2474 | 1.5967 | 0.85 | 0.8377 | 0.1543 | 0.0560 | | 0.0607 | 56.0 | 1400 | 0.5499 | 0.845 | 0.2480 | 1.5939 | 0.845 | 0.8294 | 0.1561 | 0.0549 | | 0.0607 | 57.0 | 1425 | 0.5492 | 0.845 | 0.2476 | 1.6048 | 0.845 | 0.8322 | 0.1570 | 0.0549 | | 0.0607 | 58.0 | 1450 | 0.5497 | 0.845 | 0.2478 | 1.6004 | 0.845 | 0.8322 | 0.1724 | 0.0548 | | 0.0607 | 59.0 | 1475 | 0.5496 | 0.85 | 0.2477 | 1.5982 | 0.85 | 0.8377 | 0.1634 | 0.0546 | | 0.0589 | 60.0 | 1500 | 0.5497 | 0.845 | 0.2478 | 1.5969 | 0.845 | 0.8322 | 0.1592 | 0.0545 | | 0.0589 | 61.0 | 1525 | 0.5492 | 0.85 | 0.2476 | 1.6095 | 0.85 | 0.8377 | 0.1630 | 0.0547 | | 0.0589 | 62.0 | 1550 | 0.5507 | 0.845 | 0.2483 | 1.6060 | 0.845 | 0.8322 | 0.1649 | 0.0554 | | 0.0589 | 63.0 | 1575 | 0.5490 | 0.845 | 0.2474 | 1.6021 | 0.845 | 0.8322 | 0.1635 | 0.0546 | | 0.0589 | 64.0 | 1600 | 0.5508 | 0.845 | 0.2483 | 1.5970 | 0.845 | 0.8294 | 0.1697 | 0.0552 | | 0.0589 | 65.0 | 1625 | 0.5505 | 0.84 | 0.2483 | 1.6023 | 0.8400 | 0.8256 | 0.1658 | 0.0553 | | 0.0589 | 66.0 | 1650 | 0.5503 | 0.845 | 0.2481 | 1.6032 | 0.845 | 0.8322 | 0.1637 | 0.0546 | | 0.0589 | 67.0 | 1675 | 0.5514 | 0.84 | 0.2486 | 1.6000 | 0.8400 | 0.8227 | 0.1649 | 0.0559 | | 0.0589 | 68.0 | 1700 | 0.5516 | 0.84 | 0.2487 | 1.5979 | 0.8400 | 0.8227 | 0.1649 | 0.0550 | | 0.0589 | 69.0 | 1725 | 0.5510 | 0.84 | 0.2485 | 1.6005 | 0.8400 | 0.8256 | 0.1639 | 0.0548 | | 0.0589 | 70.0 | 1750 | 0.5510 | 0.84 | 0.2484 | 1.5990 | 0.8400 | 0.8256 | 0.1653 | 0.0549 | | 0.0589 | 71.0 | 1775 | 0.5517 | 0.84 | 0.2487 | 1.6080 | 0.8400 | 0.8256 | 0.1640 | 0.0558 | | 0.0589 | 72.0 | 1800 | 0.5525 | 0.84 | 0.2491 | 1.6069 | 0.8400 | 0.8227 | 0.1669 | 0.0558 | | 0.0589 | 73.0 | 1825 | 0.5519 | 0.84 | 0.2488 | 1.6147 | 0.8400 | 0.8256 | 0.1638 | 0.0554 | | 0.0589 | 74.0 | 1850 | 0.5519 | 0.84 | 0.2487 | 1.6027 | 0.8400 | 0.8256 | 0.1657 | 0.0558 | | 0.0589 | 75.0 | 1875 | 0.5522 | 0.84 | 0.2490 | 1.6082 | 0.8400 | 0.8256 | 0.1717 | 0.0556 | | 0.0589 | 76.0 | 1900 | 0.5523 | 0.84 | 0.2489 | 1.6022 | 0.8400 | 0.8256 | 0.1645 | 0.0553 | | 0.0589 | 77.0 | 1925 | 0.5514 | 0.84 | 0.2486 | 1.6027 | 0.8400 | 0.8256 | 0.1635 | 0.0551 | | 0.0589 | 78.0 | 1950 | 0.5518 | 0.84 | 0.2488 | 1.6007 | 0.8400 | 0.8256 | 0.1641 | 0.0556 | | 0.0589 | 79.0 | 1975 | 0.5522 | 0.84 | 0.2490 | 1.6057 | 0.8400 | 0.8256 | 0.1637 | 0.0556 | | 0.0588 | 80.0 | 2000 | 0.5520 | 0.84 | 0.2489 | 1.6110 | 0.8400 | 0.8256 | 0.1658 | 0.0552 | | 0.0588 | 81.0 | 2025 | 0.5521 | 0.84 | 0.2489 | 1.6047 | 0.8400 | 0.8256 | 0.1659 | 0.0555 | | 0.0588 | 82.0 | 2050 | 0.5521 | 0.84 | 0.2490 | 1.6015 | 0.8400 | 0.8256 | 0.1635 | 0.0551 | | 0.0588 | 83.0 | 2075 | 0.5521 | 0.84 | 0.2489 | 1.6115 | 0.8400 | 0.8256 | 0.1637 | 0.0553 | | 0.0588 | 84.0 | 2100 | 0.5523 | 0.84 | 0.2490 | 1.6033 | 0.8400 | 0.8256 | 0.1738 | 0.0553 | | 0.0588 | 85.0 | 2125 | 0.5525 | 0.84 | 0.2491 | 1.6072 | 0.8400 | 0.8256 | 0.1658 | 0.0555 | | 0.0588 | 86.0 | 2150 | 0.5521 | 0.84 | 0.2489 | 1.6057 | 0.8400 | 0.8256 | 0.1574 | 0.0553 | | 0.0588 | 87.0 | 2175 | 0.5527 | 0.84 | 0.2492 | 1.6605 | 0.8400 | 0.8256 | 0.1610 | 0.0555 | | 0.0588 | 88.0 | 2200 | 0.5526 | 0.84 | 0.2491 | 1.6056 | 0.8400 | 0.8256 | 0.1544 | 0.0556 | | 0.0588 | 89.0 | 2225 | 0.5527 | 0.84 | 0.2492 | 1.6126 | 0.8400 | 0.8256 | 0.1547 | 0.0556 | | 0.0588 | 90.0 | 2250 | 0.5525 | 0.84 | 0.2491 | 1.6059 | 0.8400 | 0.8256 | 0.1525 | 0.0556 | | 0.0588 | 91.0 | 2275 | 0.5528 | 0.84 | 0.2492 | 1.6060 | 0.8400 | 0.8256 | 0.1604 | 0.0556 | | 0.0588 | 92.0 | 2300 | 0.5526 | 0.84 | 0.2491 | 1.6080 | 0.8400 | 0.8256 | 0.1525 | 0.0555 | | 0.0588 | 93.0 | 2325 | 0.5527 | 0.84 | 0.2492 | 1.6034 | 0.8400 | 0.8256 | 0.1547 | 0.0556 | | 0.0588 | 94.0 | 2350 | 0.5526 | 0.84 | 0.2492 | 1.6040 | 0.8400 | 0.8256 | 0.1673 | 0.0555 | | 0.0588 | 95.0 | 2375 | 0.5529 | 0.84 | 0.2493 | 1.6053 | 0.8400 | 0.8256 | 0.1545 | 0.0556 | | 0.0588 | 96.0 | 2400 | 0.5526 | 0.84 | 0.2492 | 1.6050 | 0.8400 | 0.8256 | 0.1626 | 0.0555 | | 0.0588 | 97.0 | 2425 | 0.5528 | 0.84 | 0.2492 | 1.6040 | 0.8400 | 0.8256 | 0.1686 | 0.0557 | | 0.0588 | 98.0 | 2450 | 0.5528 | 0.84 | 0.2492 | 1.6068 | 0.8400 | 0.8256 | 0.1626 | 0.0555 | | 0.0588 | 99.0 | 2475 | 0.5528 | 0.84 | 0.2492 | 1.6065 | 0.8400 | 0.8256 | 0.1626 | 0.0556 | | 0.0588 | 100.0 | 2500 | 0.5528 | 0.84 | 0.2493 | 1.6062 | 0.8400 | 0.8256 | 0.1626 | 0.0556 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.5

# 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3932 - Accuracy: 0.83 - Brier Loss: 0.2507 - Nll: 1.3117 - F1 Micro: 0.83 - F1 Macro: 0.8164 - Ece: 0.1915 - Aurc: 0.0602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.5453 | 0.21 | 0.8721 | 5.2626 | 0.2100 | 0.1784 | 0.2602 | 0.7492 | | No log | 2.0 | 50 | 0.9799 | 0.515 | 0.6132 | 2.7421 | 0.515 | 0.4183 | 0.2927 | 0.2698 | | No log | 3.0 | 75 | 0.7948 | 0.655 | 0.4888 | 2.6042 | 0.655 | 0.5597 | 0.2331 | 0.1485 | | No log | 4.0 | 100 | 0.6422 | 0.735 | 0.3906 | 1.6168 | 0.735 | 0.6662 | 0.2448 | 0.1095 | | No log | 5.0 | 125 | 0.6261 | 0.785 | 0.3475 | 1.1890 | 0.785 | 0.7582 | 0.2616 | 0.0792 | | No log | 6.0 | 150 | 0.6060 | 0.775 | 0.3402 | 1.3317 | 0.775 | 0.7269 | 0.2750 | 0.0709 | | No log | 7.0 | 175 | 0.5659 | 0.8 | 0.3459 | 1.4773 | 0.8000 | 0.7741 | 0.2628 | 0.0960 | | No log | 8.0 | 200 | 0.5339 | 0.81 | 0.3038 | 1.5029 | 0.81 | 0.7882 | 0.2449 | 0.0699 | | No log | 9.0 | 225 | 0.5429 | 0.805 | 0.3117 | 1.4140 | 0.805 | 0.7840 | 0.2242 | 0.0771 | | No log | 10.0 | 250 | 0.5337 | 0.815 | 0.3139 | 1.4630 | 0.815 | 0.8167 | 0.2253 | 0.0801 | | No log | 11.0 | 275 | 0.5257 | 0.815 | 0.3084 | 1.4325 | 0.815 | 0.7943 | 0.2431 | 0.0823 | | No log | 12.0 | 300 | 0.4704 | 0.81 | 0.2879 | 1.3557 | 0.81 | 0.7859 | 0.2139 | 0.0770 | | No log | 13.0 | 325 | 0.4828 | 0.81 | 0.2898 | 1.5643 | 0.81 | 0.7767 | 0.2115 | 0.0712 | | No log | 14.0 | 350 | 0.4579 | 0.815 | 0.2733 | 1.4403 | 0.815 | 0.8061 | 0.1799 | 0.0609 | | No log | 15.0 | 375 | 0.4642 | 0.815 | 0.2892 | 1.4598 | 0.815 | 0.8017 | 0.1973 | 0.0537 | | No log | 16.0 | 400 | 0.4378 | 0.84 | 0.2683 | 1.1278 | 0.8400 | 0.8320 | 0.2135 | 0.0545 | | No log | 17.0 | 425 | 0.4403 | 0.825 | 0.2750 | 1.3817 | 0.825 | 0.8024 | 0.1898 | 0.0511 | | No log | 18.0 | 450 | 0.4211 | 0.825 | 0.2646 | 1.5604 | 0.825 | 0.8123 | 0.1918 | 0.0538 | | No log | 19.0 | 475 | 0.4280 | 0.82 | 0.2700 | 1.5824 | 0.82 | 0.8016 | 0.1814 | 0.0587 | | 0.4177 | 20.0 | 500 | 0.4223 | 0.83 | 0.2649 | 1.8744 | 0.83 | 0.8152 | 0.1885 | 0.0681 | | 0.4177 | 21.0 | 525 | 0.4219 | 0.835 | 0.2682 | 1.6053 | 0.835 | 0.8252 | 0.1932 | 0.0600 | | 0.4177 | 22.0 | 550 | 0.3935 | 0.835 | 0.2534 | 1.4134 | 0.835 | 0.8229 | 0.2049 | 0.0680 | | 0.4177 | 23.0 | 575 | 0.4231 | 0.82 | 0.2651 | 1.7267 | 0.82 | 0.8028 | 0.1943 | 0.0636 | | 0.4177 | 24.0 | 600 | 0.4135 | 0.845 | 0.2576 | 1.8708 | 0.845 | 0.8304 | 0.1872 | 0.0559 | | 0.4177 | 25.0 | 625 | 0.4027 | 0.835 | 0.2526 | 1.5970 | 0.835 | 0.8187 | 0.1886 | 0.0645 | | 0.4177 | 26.0 | 650 | 0.4000 | 0.835 | 0.2513 | 1.7233 | 0.835 | 0.8151 | 0.1998 | 0.0613 | | 0.4177 | 27.0 | 675 | 0.3956 | 0.83 | 0.2478 | 1.5716 | 0.83 | 0.8143 | 0.1871 | 0.0533 | | 0.4177 | 28.0 | 700 | 0.3952 | 0.835 | 0.2493 | 1.5638 | 0.835 | 0.8153 | 0.1942 | 0.0593 | | 0.4177 | 29.0 | 725 | 0.3965 | 0.83 | 0.2509 | 1.5069 | 0.83 | 0.8150 | 0.1811 | 0.0594 | | 0.4177 | 30.0 | 750 | 0.3935 | 0.835 | 0.2486 | 1.5657 | 0.835 | 0.8168 | 0.1948 | 0.0573 | | 0.4177 | 31.0 | 775 | 0.3951 | 0.83 | 0.2498 | 1.5061 | 0.83 | 0.8146 | 0.1886 | 0.0591 | | 0.4177 | 32.0 | 800 | 0.3940 | 0.835 | 0.2506 | 1.5054 | 0.835 | 0.8203 | 0.1991 | 0.0598 | | 0.4177 | 33.0 | 825 | 0.3935 | 0.84 | 0.2493 | 1.5025 | 0.8400 | 0.8230 | 0.1932 | 0.0590 | | 0.4177 | 34.0 | 850 | 0.3928 | 0.84 | 0.2485 | 1.5679 | 0.8400 | 0.8227 | 0.1981 | 0.0570 | | 0.4177 | 35.0 | 875 | 0.3942 | 0.835 | 0.2497 | 1.5670 | 0.835 | 0.8177 | 0.1940 | 0.0592 | | 0.4177 | 36.0 | 900 | 0.3935 | 0.835 | 0.2491 | 1.5120 | 0.835 | 0.8203 | 0.1931 | 0.0596 | | 0.4177 | 37.0 | 925 | 0.3932 | 0.835 | 0.2496 | 1.5715 | 0.835 | 0.8203 | 0.1989 | 0.0597 | | 0.4177 | 38.0 | 950 | 0.3924 | 0.835 | 0.2491 | 1.5091 | 0.835 | 0.8203 | 0.1973 | 0.0606 | | 0.4177 | 39.0 | 975 | 0.3936 | 0.835 | 0.2500 | 1.5036 | 0.835 | 0.8203 | 0.1954 | 0.0602 | | 0.0597 | 40.0 | 1000 | 0.3936 | 0.835 | 0.2497 | 1.4602 | 0.835 | 0.8203 | 0.2053 | 0.0597 | | 0.0597 | 41.0 | 1025 | 0.3936 | 0.835 | 0.2505 | 1.5040 | 0.835 | 0.8203 | 0.2026 | 0.0607 | | 0.0597 | 42.0 | 1050 | 0.3931 | 0.83 | 0.2500 | 1.4565 | 0.83 | 0.8164 | 0.1961 | 0.0590 | | 0.0597 | 43.0 | 1075 | 0.3931 | 0.835 | 0.2497 | 1.5208 | 0.835 | 0.8203 | 0.1972 | 0.0591 | | 0.0597 | 44.0 | 1100 | 0.3932 | 0.835 | 0.2503 | 1.5040 | 0.835 | 0.8203 | 0.2030 | 0.0606 | | 0.0597 | 45.0 | 1125 | 0.3930 | 0.835 | 0.2502 | 1.4555 | 0.835 | 0.8203 | 0.1992 | 0.0604 | | 0.0597 | 46.0 | 1150 | 0.3927 | 0.835 | 0.2500 | 1.4553 | 0.835 | 0.8203 | 0.1960 | 0.0616 | | 0.0597 | 47.0 | 1175 | 0.3928 | 0.835 | 0.2501 | 1.3970 | 0.835 | 0.8203 | 0.1965 | 0.0610 | | 0.0597 | 48.0 | 1200 | 0.3930 | 0.835 | 0.2498 | 1.3967 | 0.835 | 0.8203 | 0.1989 | 0.0599 | | 0.0597 | 49.0 | 1225 | 0.3931 | 0.835 | 0.2502 | 1.4578 | 0.835 | 0.8203 | 0.1963 | 0.0606 | | 0.0597 | 50.0 | 1250 | 0.3932 | 0.835 | 0.2504 | 1.4475 | 0.835 | 0.8203 | 0.1996 | 0.0604 | | 0.0597 | 51.0 | 1275 | 0.3928 | 0.835 | 0.2500 | 1.3382 | 0.835 | 0.8203 | 0.2002 | 0.0609 | | 0.0597 | 52.0 | 1300 | 0.3933 | 0.83 | 0.2502 | 1.4424 | 0.83 | 0.8164 | 0.1991 | 0.0597 | | 0.0597 | 53.0 | 1325 | 0.3933 | 0.83 | 0.2502 | 1.3390 | 0.83 | 0.8164 | 0.1965 | 0.0604 | | 0.0597 | 54.0 | 1350 | 0.3929 | 0.83 | 0.2502 | 1.3351 | 0.83 | 0.8164 | 0.1914 | 0.0608 | | 0.0597 | 55.0 | 1375 | 0.3932 | 0.83 | 0.2503 | 1.3422 | 0.83 | 0.8164 | 0.1969 | 0.0608 | | 0.0597 | 56.0 | 1400 | 0.3934 | 0.83 | 0.2506 | 1.3369 | 0.83 | 0.8164 | 0.1950 | 0.0599 | | 0.0597 | 57.0 | 1425 | 0.3930 | 0.83 | 0.2502 | 1.3829 | 0.83 | 0.8164 | 0.1966 | 0.0603 | | 0.0597 | 58.0 | 1450 | 0.3930 | 0.835 | 0.2503 | 1.3219 | 0.835 | 0.8203 | 0.1907 | 0.0607 | | 0.0597 | 59.0 | 1475 | 0.3930 | 0.83 | 0.2504 | 1.3268 | 0.83 | 0.8164 | 0.1919 | 0.0599 | | 0.0574 | 60.0 | 1500 | 0.3933 | 0.835 | 0.2505 | 1.3242 | 0.835 | 0.8203 | 0.1913 | 0.0601 | | 0.0574 | 61.0 | 1525 | 0.3930 | 0.83 | 0.2504 | 1.3205 | 0.83 | 0.8164 | 0.1943 | 0.0607 | | 0.0574 | 62.0 | 1550 | 0.3930 | 0.83 | 0.2504 | 1.3189 | 0.83 | 0.8164 | 0.1947 | 0.0608 | | 0.0574 | 63.0 | 1575 | 0.3931 | 0.83 | 0.2504 | 1.3197 | 0.83 | 0.8164 | 0.1917 | 0.0600 | | 0.0574 | 64.0 | 1600 | 0.3931 | 0.835 | 0.2505 | 1.3200 | 0.835 | 0.8203 | 0.1904 | 0.0597 | | 0.0574 | 65.0 | 1625 | 0.3932 | 0.83 | 0.2505 | 1.3175 | 0.83 | 0.8164 | 0.1915 | 0.0601 | | 0.0574 | 66.0 | 1650 | 0.3931 | 0.83 | 0.2506 | 1.3200 | 0.83 | 0.8164 | 0.1917 | 0.0608 | | 0.0574 | 67.0 | 1675 | 0.3929 | 0.83 | 0.2503 | 1.3188 | 0.83 | 0.8164 | 0.1940 | 0.0598 | | 0.0574 | 68.0 | 1700 | 0.3931 | 0.83 | 0.2505 | 1.3160 | 0.83 | 0.8164 | 0.1913 | 0.0599 | | 0.0574 | 69.0 | 1725 | 0.3931 | 0.83 | 0.2505 | 1.3161 | 0.83 | 0.8164 | 0.1941 | 0.0598 | | 0.0574 | 70.0 | 1750 | 0.3932 | 0.83 | 0.2506 | 1.3171 | 0.83 | 0.8164 | 0.1961 | 0.0608 | | 0.0574 | 71.0 | 1775 | 0.3930 | 0.83 | 0.2506 | 1.3161 | 0.83 | 0.8164 | 0.1913 | 0.0602 | | 0.0574 | 72.0 | 1800 | 0.3929 | 0.83 | 0.2505 | 1.3155 | 0.83 | 0.8164 | 0.1960 | 0.0603 | | 0.0574 | 73.0 | 1825 | 0.3930 | 0.83 | 0.2506 | 1.3152 | 0.83 | 0.8164 | 0.1941 | 0.0601 | | 0.0574 | 74.0 | 1850 | 0.3930 | 0.83 | 0.2506 | 1.3167 | 0.83 | 0.8164 | 0.1940 | 0.0602 | | 0.0574 | 75.0 | 1875 | 0.3933 | 0.83 | 0.2507 | 1.3148 | 0.83 | 0.8164 | 0.1918 | 0.0600 | | 0.0574 | 76.0 | 1900 | 0.3930 | 0.83 | 0.2505 | 1.3146 | 0.83 | 0.8164 | 0.1914 | 0.0602 | | 0.0574 | 77.0 | 1925 | 0.3930 | 0.83 | 0.2505 | 1.3147 | 0.83 | 0.8164 | 0.1914 | 0.0598 | | 0.0574 | 78.0 | 1950 | 0.3931 | 0.83 | 0.2506 | 1.3134 | 0.83 | 0.8164 | 0.1942 | 0.0601 | | 0.0574 | 79.0 | 1975 | 0.3931 | 0.83 | 0.2505 | 1.3137 | 0.83 | 0.8164 | 0.1916 | 0.0598 | | 0.0573 | 80.0 | 2000 | 0.3931 | 0.83 | 0.2506 | 1.3136 | 0.83 | 0.8164 | 0.1915 | 0.0601 | | 0.0573 | 81.0 | 2025 | 0.3932 | 0.83 | 0.2506 | 1.3132 | 0.83 | 0.8164 | 0.1915 | 0.0607 | | 0.0573 | 82.0 | 2050 | 0.3933 | 0.83 | 0.2507 | 1.3142 | 0.83 | 0.8164 | 0.1943 | 0.0603 | | 0.0573 | 83.0 | 2075 | 0.3933 | 0.83 | 0.2507 | 1.3135 | 0.83 | 0.8164 | 0.1916 | 0.0603 | | 0.0573 | 84.0 | 2100 | 0.3931 | 0.83 | 0.2506 | 1.3124 | 0.83 | 0.8164 | 0.1914 | 0.0601 | | 0.0573 | 85.0 | 2125 | 0.3931 | 0.83 | 0.2507 | 1.3128 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 86.0 | 2150 | 0.3931 | 0.83 | 0.2506 | 1.3128 | 0.83 | 0.8164 | 0.1916 | 0.0602 | | 0.0573 | 87.0 | 2175 | 0.3932 | 0.83 | 0.2507 | 1.3130 | 0.83 | 0.8164 | 0.1943 | 0.0602 | | 0.0573 | 88.0 | 2200 | 0.3932 | 0.83 | 0.2507 | 1.3123 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 89.0 | 2225 | 0.3932 | 0.83 | 0.2507 | 1.3123 | 0.83 | 0.8164 | 0.1915 | 0.0599 | | 0.0573 | 90.0 | 2250 | 0.3931 | 0.83 | 0.2507 | 1.3119 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 91.0 | 2275 | 0.3932 | 0.83 | 0.2507 | 1.3121 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 92.0 | 2300 | 0.3932 | 0.83 | 0.2507 | 1.3117 | 0.83 | 0.8164 | 0.1915 | 0.0601 | | 0.0573 | 93.0 | 2325 | 0.3931 | 0.83 | 0.2507 | 1.3117 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 94.0 | 2350 | 0.3932 | 0.83 | 0.2507 | 1.3120 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 95.0 | 2375 | 0.3932 | 0.83 | 0.2507 | 1.3120 | 0.83 | 0.8164 | 0.1916 | 0.0602 | | 0.0573 | 96.0 | 2400 | 0.3932 | 0.83 | 0.2507 | 1.3119 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 97.0 | 2425 | 0.3932 | 0.83 | 0.2507 | 1.3118 | 0.83 | 0.8164 | 0.1915 | 0.0601 | | 0.0573 | 98.0 | 2450 | 0.3932 | 0.83 | 0.2507 | 1.3117 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 99.0 | 2475 | 0.3932 | 0.83 | 0.2507 | 1.3118 | 0.83 | 0.8164 | 0.1915 | 0.0602 | | 0.0573 | 100.0 | 2500 | 0.3932 | 0.83 | 0.2507 | 1.3117 | 0.83 | 0.8164 | 0.1915 | 0.0602 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.7

# 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6810 - Accuracy: 0.795 - Brier Loss: 0.3284 - Nll: 1.4069 - F1 Micro: 0.795 - F1 Macro: 0.7636 - Ece: 0.2215 - Aurc: 0.0726 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 3.5123 | 0.19 | 1.2292 | 9.8836 | 0.19 | 0.0557 | 0.5319 | 0.8455 | | No log | 2.0 | 50 | 3.3061 | 0.19 | 1.1746 | 9.5259 | 0.19 | 0.0557 | 0.4948 | 0.8502 | | No log | 3.0 | 75 | 3.0163 | 0.18 | 1.1038 | 9.0069 | 0.18 | 0.0530 | 0.3959 | 0.8542 | | No log | 4.0 | 100 | 2.7051 | 0.16 | 1.0425 | 8.3332 | 0.16 | 0.0685 | 0.3637 | 0.8603 | | No log | 5.0 | 125 | 2.4101 | 0.145 | 0.9955 | 7.8875 | 0.145 | 0.0886 | 0.3276 | 0.8594 | | No log | 6.0 | 150 | 2.1622 | 0.16 | 0.9581 | 7.4210 | 0.16 | 0.1094 | 0.3098 | 0.8418 | | No log | 7.0 | 175 | 1.9665 | 0.17 | 0.9199 | 6.3502 | 0.17 | 0.1141 | 0.2939 | 0.8129 | | No log | 8.0 | 200 | 1.8218 | 0.19 | 0.8852 | 4.6467 | 0.19 | 0.1580 | 0.2691 | 0.7908 | | No log | 9.0 | 225 | 1.7041 | 0.205 | 0.8503 | 3.9915 | 0.205 | 0.1722 | 0.2746 | 0.7349 | | No log | 10.0 | 250 | 1.6030 | 0.315 | 0.8144 | 3.8156 | 0.315 | 0.2716 | 0.2999 | 0.6443 | | No log | 11.0 | 275 | 1.5078 | 0.39 | 0.7749 | 3.6634 | 0.39 | 0.3103 | 0.2992 | 0.5291 | | No log | 12.0 | 300 | 1.4231 | 0.46 | 0.7351 | 3.5329 | 0.46 | 0.3550 | 0.3143 | 0.4339 | | No log | 13.0 | 325 | 1.3487 | 0.465 | 0.6988 | 3.4534 | 0.465 | 0.3851 | 0.2929 | 0.3704 | | No log | 14.0 | 350 | 1.2823 | 0.51 | 0.6657 | 3.2203 | 0.51 | 0.4164 | 0.2835 | 0.3215 | | No log | 15.0 | 375 | 1.2282 | 0.54 | 0.6399 | 3.0431 | 0.54 | 0.4415 | 0.2943 | 0.2928 | | No log | 16.0 | 400 | 1.1793 | 0.555 | 0.6170 | 2.8562 | 0.555 | 0.4502 | 0.2813 | 0.2676 | | No log | 17.0 | 425 | 1.1466 | 0.565 | 0.6019 | 2.7630 | 0.565 | 0.4607 | 0.2450 | 0.2524 | | No log | 18.0 | 450 | 1.1114 | 0.59 | 0.5832 | 2.7390 | 0.59 | 0.4721 | 0.2850 | 0.2289 | | No log | 19.0 | 475 | 1.0835 | 0.6 | 0.5699 | 2.5928 | 0.6 | 0.5073 | 0.2760 | 0.2195 | | 1.9045 | 20.0 | 500 | 1.0547 | 0.615 | 0.5539 | 2.6261 | 0.615 | 0.5273 | 0.2883 | 0.2044 | | 1.9045 | 21.0 | 525 | 1.0294 | 0.625 | 0.5404 | 2.6118 | 0.625 | 0.5343 | 0.2703 | 0.1945 | | 1.9045 | 22.0 | 550 | 1.0085 | 0.635 | 0.5300 | 2.4378 | 0.635 | 0.5381 | 0.2727 | 0.1842 | | 1.9045 | 23.0 | 575 | 0.9900 | 0.64 | 0.5188 | 2.4290 | 0.64 | 0.5435 | 0.2781 | 0.1745 | | 1.9045 | 24.0 | 600 | 0.9674 | 0.645 | 0.5071 | 2.3513 | 0.645 | 0.5527 | 0.2631 | 0.1647 | | 1.9045 | 25.0 | 625 | 0.9522 | 0.64 | 0.4980 | 2.2815 | 0.64 | 0.5494 | 0.2687 | 0.1606 | | 1.9045 | 26.0 | 650 | 0.9336 | 0.65 | 0.4883 | 2.2513 | 0.65 | 0.5603 | 0.2727 | 0.1515 | | 1.9045 | 27.0 | 675 | 0.9175 | 0.665 | 0.4795 | 2.2466 | 0.665 | 0.5707 | 0.2848 | 0.1450 | | 1.9045 | 28.0 | 700 | 0.9060 | 0.655 | 0.4731 | 2.2223 | 0.655 | 0.5655 | 0.2598 | 0.1426 | | 1.9045 | 29.0 | 725 | 0.8924 | 0.67 | 0.4648 | 2.1571 | 0.67 | 0.5748 | 0.2504 | 0.1364 | | 1.9045 | 30.0 | 750 | 0.8808 | 0.675 | 0.4580 | 2.1970 | 0.675 | 0.5804 | 0.2124 | 0.1302 | | 1.9045 | 31.0 | 775 | 0.8698 | 0.675 | 0.4513 | 1.9818 | 0.675 | 0.5784 | 0.2413 | 0.1248 | | 1.9045 | 32.0 | 800 | 0.8581 | 0.685 | 0.4451 | 2.0653 | 0.685 | 0.6062 | 0.2783 | 0.1221 | | 1.9045 | 33.0 | 825 | 0.8493 | 0.68 | 0.4398 | 1.9229 | 0.68 | 0.5964 | 0.2430 | 0.1198 | | 1.9045 | 34.0 | 850 | 0.8416 | 0.675 | 0.4351 | 1.9147 | 0.675 | 0.5901 | 0.2547 | 0.1181 | | 1.9045 | 35.0 | 875 | 0.8329 | 0.69 | 0.4296 | 1.9727 | 0.69 | 0.6098 | 0.2498 | 0.1121 | | 1.9045 | 36.0 | 900 | 0.8222 | 0.7 | 0.4234 | 1.8988 | 0.7 | 0.6185 | 0.2404 | 0.1084 | | 1.9045 | 37.0 | 925 | 0.8178 | 0.69 | 0.4201 | 1.8900 | 0.69 | 0.6103 | 0.2338 | 0.1079 | | 1.9045 | 38.0 | 950 | 0.8091 | 0.69 | 0.4153 | 1.9396 | 0.69 | 0.6100 | 0.2469 | 0.1058 | | 1.9045 | 39.0 | 975 | 0.7992 | 0.705 | 0.4098 | 1.8177 | 0.705 | 0.6453 | 0.1971 | 0.1037 | | 0.8325 | 40.0 | 1000 | 0.7962 | 0.715 | 0.4069 | 1.7962 | 0.715 | 0.6560 | 0.2474 | 0.1008 | | 0.8325 | 41.0 | 1025 | 0.7890 | 0.715 | 0.4032 | 1.7233 | 0.715 | 0.6592 | 0.2417 | 0.1009 | | 0.8325 | 42.0 | 1050 | 0.7842 | 0.715 | 0.3997 | 1.6669 | 0.715 | 0.6573 | 0.2441 | 0.1005 | | 0.8325 | 43.0 | 1075 | 0.7788 | 0.71 | 0.3962 | 1.6468 | 0.7100 | 0.6515 | 0.2199 | 0.0998 | | 0.8325 | 44.0 | 1100 | 0.7713 | 0.725 | 0.3918 | 1.6398 | 0.7250 | 0.6698 | 0.2363 | 0.0945 | | 0.8325 | 45.0 | 1125 | 0.7687 | 0.725 | 0.3895 | 1.6397 | 0.7250 | 0.6677 | 0.2478 | 0.0939 | | 0.8325 | 46.0 | 1150 | 0.7642 | 0.73 | 0.3867 | 1.6259 | 0.7300 | 0.6684 | 0.2538 | 0.0941 | | 0.8325 | 47.0 | 1175 | 0.7571 | 0.74 | 0.3822 | 1.5033 | 0.74 | 0.6907 | 0.2269 | 0.0903 | | 0.8325 | 48.0 | 1200 | 0.7547 | 0.74 | 0.3806 | 1.5595 | 0.74 | 0.6830 | 0.2448 | 0.0912 | | 0.8325 | 49.0 | 1225 | 0.7516 | 0.75 | 0.3779 | 1.6264 | 0.75 | 0.6921 | 0.2489 | 0.0887 | | 0.8325 | 50.0 | 1250 | 0.7477 | 0.75 | 0.3759 | 1.5568 | 0.75 | 0.7029 | 0.2388 | 0.0888 | | 0.8325 | 51.0 | 1275 | 0.7431 | 0.755 | 0.3725 | 1.5037 | 0.755 | 0.6986 | 0.2254 | 0.0848 | | 0.8325 | 52.0 | 1300 | 0.7418 | 0.755 | 0.3713 | 1.4951 | 0.755 | 0.7085 | 0.2261 | 0.0862 | | 0.8325 | 53.0 | 1325 | 0.7360 | 0.77 | 0.3676 | 1.4881 | 0.7700 | 0.7241 | 0.2474 | 0.0825 | | 0.8325 | 54.0 | 1350 | 0.7339 | 0.77 | 0.3665 | 1.5554 | 0.7700 | 0.7241 | 0.2646 | 0.0827 | | 0.8325 | 55.0 | 1375 | 0.7294 | 0.775 | 0.3636 | 1.4885 | 0.775 | 0.7275 | 0.2283 | 0.0818 | | 0.8325 | 56.0 | 1400 | 0.7265 | 0.78 | 0.3617 | 1.5387 | 0.78 | 0.7306 | 0.2416 | 0.0799 | | 0.8325 | 57.0 | 1425 | 0.7247 | 0.77 | 0.3598 | 1.4382 | 0.7700 | 0.7241 | 0.2553 | 0.0806 | | 0.8325 | 58.0 | 1450 | 0.7234 | 0.78 | 0.3589 | 1.4888 | 0.78 | 0.7306 | 0.2231 | 0.0796 | | 0.8325 | 59.0 | 1475 | 0.7186 | 0.78 | 0.3565 | 1.5400 | 0.78 | 0.7306 | 0.2200 | 0.0790 | | 0.618 | 60.0 | 1500 | 0.7174 | 0.78 | 0.3549 | 1.4823 | 0.78 | 0.7306 | 0.2340 | 0.0787 | | 0.618 | 61.0 | 1525 | 0.7148 | 0.78 | 0.3534 | 1.4804 | 0.78 | 0.7306 | 0.2412 | 0.0785 | | 0.618 | 62.0 | 1550 | 0.7122 | 0.785 | 0.3516 | 1.4334 | 0.785 | 0.7445 | 0.2353 | 0.0787 | | 0.618 | 63.0 | 1575 | 0.7107 | 0.79 | 0.3501 | 1.4153 | 0.79 | 0.7516 | 0.2354 | 0.0777 | | 0.618 | 64.0 | 1600 | 0.7091 | 0.78 | 0.3491 | 1.4698 | 0.78 | 0.7306 | 0.2324 | 0.0780 | | 0.618 | 65.0 | 1625 | 0.7071 | 0.79 | 0.3481 | 1.4097 | 0.79 | 0.7516 | 0.2198 | 0.0785 | | 0.618 | 66.0 | 1650 | 0.7047 | 0.785 | 0.3464 | 1.4088 | 0.785 | 0.7458 | 0.2325 | 0.0778 | | 0.618 | 67.0 | 1675 | 0.7041 | 0.785 | 0.3457 | 1.4108 | 0.785 | 0.7458 | 0.2248 | 0.0781 | | 0.618 | 68.0 | 1700 | 0.7025 | 0.79 | 0.3444 | 1.4145 | 0.79 | 0.7516 | 0.2195 | 0.0773 | | 0.618 | 69.0 | 1725 | 0.7014 | 0.79 | 0.3436 | 1.4120 | 0.79 | 0.7516 | 0.2629 | 0.0771 | | 0.618 | 70.0 | 1750 | 0.6992 | 0.785 | 0.3422 | 1.4046 | 0.785 | 0.7458 | 0.2294 | 0.0767 | | 0.618 | 71.0 | 1775 | 0.6982 | 0.785 | 0.3412 | 1.4142 | 0.785 | 0.7458 | 0.2325 | 0.0761 | | 0.618 | 72.0 | 1800 | 0.6954 | 0.79 | 0.3395 | 1.4009 | 0.79 | 0.7516 | 0.2253 | 0.0763 | | 0.618 | 73.0 | 1825 | 0.6942 | 0.79 | 0.3389 | 1.3994 | 0.79 | 0.7559 | 0.2383 | 0.0763 | | 0.618 | 74.0 | 1850 | 0.6937 | 0.785 | 0.3382 | 1.4061 | 0.785 | 0.7458 | 0.2213 | 0.0762 | | 0.618 | 75.0 | 1875 | 0.6935 | 0.785 | 0.3378 | 1.4082 | 0.785 | 0.7458 | 0.2218 | 0.0762 | | 0.618 | 76.0 | 1900 | 0.6910 | 0.795 | 0.3359 | 1.4098 | 0.795 | 0.7599 | 0.2689 | 0.0746 | | 0.618 | 77.0 | 1925 | 0.6907 | 0.79 | 0.3356 | 1.4072 | 0.79 | 0.7541 | 0.2254 | 0.0741 | | 0.618 | 78.0 | 1950 | 0.6896 | 0.795 | 0.3352 | 1.3996 | 0.795 | 0.7636 | 0.2226 | 0.0743 | | 0.618 | 79.0 | 1975 | 0.6896 | 0.79 | 0.3349 | 1.4073 | 0.79 | 0.7541 | 0.2295 | 0.0742 | | 0.516 | 80.0 | 2000 | 0.6874 | 0.79 | 0.3335 | 1.4089 | 0.79 | 0.7541 | 0.2287 | 0.0743 | | 0.516 | 81.0 | 2025 | 0.6874 | 0.795 | 0.3333 | 1.3983 | 0.795 | 0.7636 | 0.2387 | 0.0742 | | 0.516 | 82.0 | 2050 | 0.6867 | 0.795 | 0.3327 | 1.4098 | 0.795 | 0.7636 | 0.2162 | 0.0736 | | 0.516 | 83.0 | 2075 | 0.6865 | 0.795 | 0.3323 | 1.4656 | 0.795 | 0.7636 | 0.2072 | 0.0738 | | 0.516 | 84.0 | 2100 | 0.6857 | 0.795 | 0.3323 | 1.4107 | 0.795 | 0.7636 | 0.2138 | 0.0741 | | 0.516 | 85.0 | 2125 | 0.6854 | 0.795 | 0.3316 | 1.4223 | 0.795 | 0.7636 | 0.2262 | 0.0732 | | 0.516 | 86.0 | 2150 | 0.6846 | 0.795 | 0.3311 | 1.4138 | 0.795 | 0.7636 | 0.2224 | 0.0733 | | 0.516 | 87.0 | 2175 | 0.6834 | 0.795 | 0.3302 | 1.4113 | 0.795 | 0.7636 | 0.2307 | 0.0731 | | 0.516 | 88.0 | 2200 | 0.6831 | 0.795 | 0.3300 | 1.4088 | 0.795 | 0.7636 | 0.2256 | 0.0730 | | 0.516 | 89.0 | 2225 | 0.6821 | 0.795 | 0.3295 | 1.4126 | 0.795 | 0.7636 | 0.2395 | 0.0728 | | 0.516 | 90.0 | 2250 | 0.6821 | 0.795 | 0.3294 | 1.4123 | 0.795 | 0.7636 | 0.2237 | 0.0728 | | 0.516 | 91.0 | 2275 | 0.6823 | 0.795 | 0.3294 | 1.4085 | 0.795 | 0.7636 | 0.2213 | 0.0728 | | 0.516 | 92.0 | 2300 | 0.6819 | 0.795 | 0.3290 | 1.4105 | 0.795 | 0.7636 | 0.2332 | 0.0730 | | 0.516 | 93.0 | 2325 | 0.6816 | 0.795 | 0.3289 | 1.4094 | 0.795 | 0.7636 | 0.2236 | 0.0729 | | 0.516 | 94.0 | 2350 | 0.6812 | 0.795 | 0.3287 | 1.4092 | 0.795 | 0.7636 | 0.2235 | 0.0729 | | 0.516 | 95.0 | 2375 | 0.6813 | 0.795 | 0.3286 | 1.4065 | 0.795 | 0.7636 | 0.2197 | 0.0727 | | 0.516 | 96.0 | 2400 | 0.6811 | 0.795 | 0.3285 | 1.4079 | 0.795 | 0.7636 | 0.2247 | 0.0729 | | 0.516 | 97.0 | 2425 | 0.6810 | 0.795 | 0.3284 | 1.4072 | 0.795 | 0.7636 | 0.2320 | 0.0729 | | 0.516 | 98.0 | 2450 | 0.6810 | 0.795 | 0.3284 | 1.4062 | 0.795 | 0.7636 | 0.2148 | 0.0727 | | 0.516 | 99.0 | 2475 | 0.6810 | 0.795 | 0.3284 | 1.4068 | 0.795 | 0.7636 | 0.2215 | 0.0726 | | 0.4715 | 100.0 | 2500 | 0.6810 | 0.795 | 0.3284 | 1.4069 | 0.795 | 0.7636 | 0.2215 | 0.0726 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9

# 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 - Accuracy: 0.82 - Brier Loss: 0.2563 - Nll: 1.8898 - F1 Micro: 0.82 - F1 Macro: 0.8009 - Ece: 0.1578 - Aurc: 0.0530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.9764 | 0.23 | 0.8621 | 4.6756 | 0.23 | 0.1902 | 0.2733 | 0.7604 | | No log | 2.0 | 50 | 1.2764 | 0.535 | 0.5973 | 2.7212 | 0.535 | 0.4337 | 0.2769 | 0.2592 | | No log | 3.0 | 75 | 0.9774 | 0.68 | 0.4478 | 2.1874 | 0.68 | 0.5915 | 0.2144 | 0.1334 | | No log | 4.0 | 100 | 0.8047 | 0.755 | 0.3617 | 1.4629 | 0.755 | 0.7257 | 0.1850 | 0.0888 | | No log | 5.0 | 125 | 0.7616 | 0.765 | 0.3363 | 1.4885 | 0.765 | 0.7391 | 0.2017 | 0.0843 | | No log | 6.0 | 150 | 1.0029 | 0.72 | 0.4200 | 1.6550 | 0.72 | 0.7047 | 0.2303 | 0.1169 | | No log | 7.0 | 175 | 0.6286 | 0.825 | 0.2766 | 1.2493 | 0.825 | 0.7930 | 0.1954 | 0.0646 | | No log | 8.0 | 200 | 0.6859 | 0.82 | 0.2857 | 1.4847 | 0.82 | 0.7971 | 0.1837 | 0.0699 | | No log | 9.0 | 225 | 0.6365 | 0.81 | 0.2765 | 1.1457 | 0.81 | 0.7913 | 0.1604 | 0.0669 | | No log | 10.0 | 250 | 0.6085 | 0.81 | 0.2614 | 1.5809 | 0.81 | 0.7928 | 0.1874 | 0.0536 | | No log | 11.0 | 275 | 0.5900 | 0.84 | 0.2620 | 1.1457 | 0.8400 | 0.8308 | 0.1695 | 0.0674 | | No log | 12.0 | 300 | 0.8544 | 0.75 | 0.3667 | 1.9577 | 0.75 | 0.7330 | 0.1988 | 0.1329 | | No log | 13.0 | 325 | 0.5265 | 0.845 | 0.2278 | 1.2521 | 0.845 | 0.8209 | 0.1518 | 0.0399 | | No log | 14.0 | 350 | 0.5702 | 0.815 | 0.2567 | 1.5233 | 0.815 | 0.8032 | 0.1551 | 0.0519 | | No log | 15.0 | 375 | 0.5933 | 0.845 | 0.2581 | 1.4776 | 0.845 | 0.8341 | 0.1659 | 0.0738 | | No log | 16.0 | 400 | 0.5697 | 0.84 | 0.2496 | 1.6732 | 0.8400 | 0.8235 | 0.1470 | 0.0557 | | No log | 17.0 | 425 | 0.5471 | 0.825 | 0.2428 | 1.7010 | 0.825 | 0.8093 | 0.1406 | 0.0461 | | No log | 18.0 | 450 | 0.5696 | 0.825 | 0.2546 | 1.4095 | 0.825 | 0.7977 | 0.1461 | 0.0612 | | No log | 19.0 | 475 | 0.6544 | 0.805 | 0.2959 | 1.8251 | 0.805 | 0.7970 | 0.1681 | 0.0605 | | 0.4416 | 20.0 | 500 | 0.5113 | 0.83 | 0.2327 | 1.4103 | 0.83 | 0.8093 | 0.1380 | 0.0541 | | 0.4416 | 21.0 | 525 | 0.5255 | 0.84 | 0.2375 | 1.6750 | 0.8400 | 0.8220 | 0.1320 | 0.0462 | | 0.4416 | 22.0 | 550 | 0.5889 | 0.835 | 0.2681 | 1.7850 | 0.835 | 0.8242 | 0.1507 | 0.0683 | | 0.4416 | 23.0 | 575 | 0.5456 | 0.835 | 0.2492 | 1.8481 | 0.835 | 0.8137 | 0.1716 | 0.0550 | | 0.4416 | 24.0 | 600 | 0.5661 | 0.83 | 0.2611 | 1.8434 | 0.83 | 0.8156 | 0.1618 | 0.0591 | | 0.4416 | 25.0 | 625 | 0.5444 | 0.83 | 0.2484 | 1.7579 | 0.83 | 0.8091 | 0.1478 | 0.0530 | | 0.4416 | 26.0 | 650 | 0.5418 | 0.83 | 0.2503 | 1.7188 | 0.83 | 0.8125 | 0.1564 | 0.0484 | | 0.4416 | 27.0 | 675 | 0.5532 | 0.835 | 0.2540 | 1.8931 | 0.835 | 0.8146 | 0.1694 | 0.0514 | | 0.4416 | 28.0 | 700 | 0.5492 | 0.835 | 0.2518 | 1.8959 | 0.835 | 0.8155 | 0.1505 | 0.0495 | | 0.4416 | 29.0 | 725 | 0.5478 | 0.825 | 0.2505 | 1.8907 | 0.825 | 0.8069 | 0.1548 | 0.0503 | | 0.4416 | 30.0 | 750 | 0.5478 | 0.835 | 0.2510 | 1.8881 | 0.835 | 0.8178 | 0.1467 | 0.0521 | | 0.4416 | 31.0 | 775 | 0.5472 | 0.825 | 0.2505 | 1.8888 | 0.825 | 0.8064 | 0.1527 | 0.0510 | | 0.4416 | 32.0 | 800 | 0.5522 | 0.83 | 0.2527 | 1.8927 | 0.83 | 0.8126 | 0.1449 | 0.0520 | | 0.4416 | 33.0 | 825 | 0.5513 | 0.825 | 0.2524 | 1.8989 | 0.825 | 0.8064 | 0.1625 | 0.0509 | | 0.4416 | 34.0 | 850 | 0.5465 | 0.835 | 0.2504 | 1.8880 | 0.835 | 0.8148 | 0.1519 | 0.0520 | | 0.4416 | 35.0 | 875 | 0.5489 | 0.825 | 0.2515 | 1.8866 | 0.825 | 0.8064 | 0.1538 | 0.0510 | | 0.4416 | 36.0 | 900 | 0.5508 | 0.825 | 0.2521 | 1.8922 | 0.825 | 0.8053 | 0.1356 | 0.0526 | | 0.4416 | 37.0 | 925 | 0.5495 | 0.825 | 0.2522 | 1.8881 | 0.825 | 0.8064 | 0.1517 | 0.0514 | | 0.4416 | 38.0 | 950 | 0.5483 | 0.825 | 0.2514 | 1.8859 | 0.825 | 0.8064 | 0.1749 | 0.0511 | | 0.4416 | 39.0 | 975 | 0.5508 | 0.825 | 0.2524 | 1.8868 | 0.825 | 0.8064 | 0.1459 | 0.0514 | | 0.0519 | 40.0 | 1000 | 0.5519 | 0.825 | 0.2529 | 1.8862 | 0.825 | 0.8064 | 0.1532 | 0.0513 | | 0.0519 | 41.0 | 1025 | 0.5522 | 0.825 | 0.2530 | 1.8882 | 0.825 | 0.8064 | 0.1665 | 0.0519 | | 0.0519 | 42.0 | 1050 | 0.5507 | 0.825 | 0.2525 | 1.8870 | 0.825 | 0.8064 | 0.1613 | 0.0508 | | 0.0519 | 43.0 | 1075 | 0.5528 | 0.825 | 0.2536 | 1.8884 | 0.825 | 0.8064 | 0.1634 | 0.0517 | | 0.0519 | 44.0 | 1100 | 0.5520 | 0.825 | 0.2531 | 1.8879 | 0.825 | 0.8064 | 0.1519 | 0.0525 | | 0.0519 | 45.0 | 1125 | 0.5524 | 0.825 | 0.2535 | 1.8876 | 0.825 | 0.8053 | 0.1582 | 0.0515 | | 0.0519 | 46.0 | 1150 | 0.5525 | 0.825 | 0.2534 | 1.8867 | 0.825 | 0.8064 | 0.1592 | 0.0519 | | 0.0519 | 47.0 | 1175 | 0.5532 | 0.825 | 0.2539 | 1.8875 | 0.825 | 0.8064 | 0.1621 | 0.0521 | | 0.0519 | 48.0 | 1200 | 0.5540 | 0.825 | 0.2540 | 1.8865 | 0.825 | 0.8064 | 0.1502 | 0.0522 | | 0.0519 | 49.0 | 1225 | 0.5523 | 0.825 | 0.2538 | 1.8268 | 0.825 | 0.8072 | 0.1625 | 0.0514 | | 0.0519 | 50.0 | 1250 | 0.5535 | 0.825 | 0.2539 | 1.8871 | 0.825 | 0.8064 | 0.1684 | 0.0517 | | 0.0519 | 51.0 | 1275 | 0.5526 | 0.825 | 0.2534 | 1.8850 | 0.825 | 0.8064 | 0.1621 | 0.0519 | | 0.0519 | 52.0 | 1300 | 0.5543 | 0.825 | 0.2543 | 1.8865 | 0.825 | 0.8064 | 0.1429 | 0.0521 | | 0.0519 | 53.0 | 1325 | 0.5526 | 0.825 | 0.2538 | 1.8866 | 0.825 | 0.8064 | 0.1613 | 0.0515 | | 0.0519 | 54.0 | 1350 | 0.5530 | 0.82 | 0.2538 | 1.8877 | 0.82 | 0.8009 | 0.1620 | 0.0518 | | 0.0519 | 55.0 | 1375 | 0.5550 | 0.825 | 0.2547 | 1.8872 | 0.825 | 0.8064 | 0.1567 | 0.0522 | | 0.0519 | 56.0 | 1400 | 0.5565 | 0.825 | 0.2552 | 1.8859 | 0.825 | 0.8064 | 0.1400 | 0.0523 | | 0.0519 | 57.0 | 1425 | 0.5552 | 0.825 | 0.2548 | 1.8874 | 0.825 | 0.8064 | 0.1543 | 0.0520 | | 0.0519 | 58.0 | 1450 | 0.5537 | 0.825 | 0.2542 | 1.8860 | 0.825 | 0.8064 | 0.1531 | 0.0516 | | 0.0519 | 59.0 | 1475 | 0.5559 | 0.825 | 0.2551 | 1.8879 | 0.825 | 0.8064 | 0.1564 | 0.0525 | | 0.0508 | 60.0 | 1500 | 0.5548 | 0.825 | 0.2545 | 1.8866 | 0.825 | 0.8064 | 0.1526 | 0.0522 | | 0.0508 | 61.0 | 1525 | 0.5557 | 0.825 | 0.2550 | 1.8884 | 0.825 | 0.8064 | 0.1443 | 0.0524 | | 0.0508 | 62.0 | 1550 | 0.5548 | 0.82 | 0.2546 | 1.8874 | 0.82 | 0.8009 | 0.1709 | 0.0527 | | 0.0508 | 63.0 | 1575 | 0.5556 | 0.825 | 0.2551 | 1.8899 | 0.825 | 0.8064 | 0.1606 | 0.0524 | | 0.0508 | 64.0 | 1600 | 0.5562 | 0.825 | 0.2553 | 1.8872 | 0.825 | 0.8064 | 0.1467 | 0.0527 | | 0.0508 | 65.0 | 1625 | 0.5569 | 0.825 | 0.2554 | 1.8879 | 0.825 | 0.8064 | 0.1537 | 0.0524 | | 0.0508 | 66.0 | 1650 | 0.5567 | 0.825 | 0.2555 | 1.8873 | 0.825 | 0.8064 | 0.1601 | 0.0525 | | 0.0508 | 67.0 | 1675 | 0.5556 | 0.825 | 0.2550 | 1.8878 | 0.825 | 0.8064 | 0.1601 | 0.0527 | | 0.0508 | 68.0 | 1700 | 0.5570 | 0.825 | 0.2555 | 1.8879 | 0.825 | 0.8064 | 0.1679 | 0.0528 | | 0.0508 | 69.0 | 1725 | 0.5560 | 0.825 | 0.2553 | 1.8886 | 0.825 | 0.8064 | 0.1525 | 0.0521 | | 0.0508 | 70.0 | 1750 | 0.5562 | 0.825 | 0.2553 | 1.8878 | 0.825 | 0.8064 | 0.1531 | 0.0528 | | 0.0508 | 71.0 | 1775 | 0.5572 | 0.82 | 0.2557 | 1.8883 | 0.82 | 0.8009 | 0.1718 | 0.0530 | | 0.0508 | 72.0 | 1800 | 0.5567 | 0.82 | 0.2555 | 1.8888 | 0.82 | 0.8009 | 0.1630 | 0.0525 | | 0.0508 | 73.0 | 1825 | 0.5571 | 0.825 | 0.2556 | 1.8882 | 0.825 | 0.8064 | 0.1598 | 0.0528 | | 0.0508 | 74.0 | 1850 | 0.5580 | 0.825 | 0.2561 | 1.8901 | 0.825 | 0.8064 | 0.1543 | 0.0530 | | 0.0508 | 75.0 | 1875 | 0.5579 | 0.82 | 0.2561 | 1.8892 | 0.82 | 0.8009 | 0.1721 | 0.0530 | | 0.0508 | 76.0 | 1900 | 0.5574 | 0.82 | 0.2559 | 1.8892 | 0.82 | 0.8009 | 0.1636 | 0.0528 | | 0.0508 | 77.0 | 1925 | 0.5569 | 0.82 | 0.2557 | 1.8393 | 0.82 | 0.8009 | 0.1634 | 0.0526 | | 0.0508 | 78.0 | 1950 | 0.5572 | 0.82 | 0.2558 | 1.8887 | 0.82 | 0.8009 | 0.1637 | 0.0530 | | 0.0508 | 79.0 | 1975 | 0.5578 | 0.82 | 0.2560 | 1.8888 | 0.82 | 0.8009 | 0.1579 | 0.0530 | | 0.0507 | 80.0 | 2000 | 0.5577 | 0.82 | 0.2559 | 1.8889 | 0.82 | 0.8009 | 0.1578 | 0.0532 | | 0.0507 | 81.0 | 2025 | 0.5578 | 0.82 | 0.2560 | 1.8889 | 0.82 | 0.8009 | 0.1578 | 0.0533 | | 0.0507 | 82.0 | 2050 | 0.5579 | 0.825 | 0.2561 | 1.8891 | 0.825 | 0.8064 | 0.1602 | 0.0528 | | 0.0507 | 83.0 | 2075 | 0.5581 | 0.825 | 0.2562 | 1.8894 | 0.825 | 0.8064 | 0.1544 | 0.0528 | | 0.0507 | 84.0 | 2100 | 0.5579 | 0.82 | 0.2561 | 1.8894 | 0.82 | 0.8009 | 0.1581 | 0.0531 | | 0.0507 | 85.0 | 2125 | 0.5580 | 0.82 | 0.2561 | 1.8896 | 0.82 | 0.8009 | 0.1578 | 0.0528 | | 0.0507 | 86.0 | 2150 | 0.5581 | 0.82 | 0.2562 | 1.8891 | 0.82 | 0.8009 | 0.1580 | 0.0532 | | 0.0507 | 87.0 | 2175 | 0.5582 | 0.82 | 0.2562 | 1.8467 | 0.82 | 0.8009 | 0.1581 | 0.0528 | | 0.0507 | 88.0 | 2200 | 0.5583 | 0.82 | 0.2562 | 1.8891 | 0.82 | 0.8009 | 0.1580 | 0.0531 | | 0.0507 | 89.0 | 2225 | 0.5584 | 0.815 | 0.2563 | 1.8894 | 0.815 | 0.7976 | 0.1608 | 0.0534 | | 0.0507 | 90.0 | 2250 | 0.5578 | 0.82 | 0.2561 | 1.8894 | 0.82 | 0.8009 | 0.1578 | 0.0530 | | 0.0507 | 91.0 | 2275 | 0.5584 | 0.815 | 0.2563 | 1.8896 | 0.815 | 0.7976 | 0.1607 | 0.0532 | | 0.0507 | 92.0 | 2300 | 0.5583 | 0.82 | 0.2562 | 1.8893 | 0.82 | 0.8009 | 0.1581 | 0.0531 | | 0.0507 | 93.0 | 2325 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1579 | 0.0530 | | 0.0507 | 94.0 | 2350 | 0.5582 | 0.82 | 0.2562 | 1.8392 | 0.82 | 0.8009 | 0.1578 | 0.0530 | | 0.0507 | 95.0 | 2375 | 0.5584 | 0.82 | 0.2563 | 1.8897 | 0.82 | 0.8009 | 0.1582 | 0.0531 | | 0.0507 | 96.0 | 2400 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 | | 0.0507 | 97.0 | 2425 | 0.5583 | 0.82 | 0.2563 | 1.8896 | 0.82 | 0.8009 | 0.1580 | 0.0530 | | 0.0507 | 98.0 | 2450 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 | | 0.0507 | 99.0 | 2475 | 0.5583 | 0.82 | 0.2563 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 | | 0.0507 | 100.0 | 2500 | 0.5583 | 0.82 | 0.2563 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

ayanban011/6_e_200-tiny_tobacco3482

# 6_e_200-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5797 - Accuracy: 0.68 - Brier Loss: 0.4846 - Nll: 2.7977 - F1 Micro: 0.68 - F1 Macro: 0.6624 - Ece: 0.2589 - Aurc: 0.2179 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.6199 | 0.545 | 0.5837 | 2.2367 | 0.545 | 0.4488 | 0.2385 | 0.2462 | | No log | 2.0 | 50 | 0.4278 | 0.72 | 0.4443 | 1.6749 | 0.72 | 0.7298 | 0.3095 | 0.1339 | | No log | 3.0 | 75 | 0.6756 | 0.625 | 0.4913 | 2.4923 | 0.625 | 0.5751 | 0.2278 | 0.1729 | | No log | 4.0 | 100 | 0.6851 | 0.615 | 0.5031 | 2.5374 | 0.615 | 0.5988 | 0.2487 | 0.1568 | | No log | 5.0 | 125 | 0.5550 | 0.69 | 0.4473 | 1.6315 | 0.69 | 0.6549 | 0.2726 | 0.1697 | | No log | 6.0 | 150 | 0.4135 | 0.79 | 0.3326 | 1.3743 | 0.79 | 0.7851 | 0.2019 | 0.0767 | | No log | 7.0 | 175 | 0.4111 | 0.77 | 0.3527 | 1.6472 | 0.7700 | 0.7388 | 0.1917 | 0.0849 | | No log | 8.0 | 200 | 0.4809 | 0.745 | 0.3805 | 2.1093 | 0.745 | 0.7260 | 0.2375 | 0.0818 | | No log | 9.0 | 225 | 0.6527 | 0.65 | 0.4651 | 2.5269 | 0.65 | 0.6271 | 0.2237 | 0.1333 | | No log | 10.0 | 250 | 0.5215 | 0.715 | 0.4203 | 1.7818 | 0.715 | 0.6875 | 0.2155 | 0.1328 | | No log | 11.0 | 275 | 0.5673 | 0.64 | 0.4965 | 2.1840 | 0.64 | 0.5458 | 0.2547 | 0.1750 | | No log | 12.0 | 300 | 0.4713 | 0.75 | 0.3879 | 2.0629 | 0.75 | 0.7397 | 0.2143 | 0.1262 | | No log | 13.0 | 325 | 0.5793 | 0.655 | 0.4791 | 2.6089 | 0.655 | 0.6206 | 0.2461 | 0.1870 | | No log | 14.0 | 350 | 0.6098 | 0.685 | 0.4375 | 2.0773 | 0.685 | 0.6802 | 0.2415 | 0.1480 | | No log | 15.0 | 375 | 0.5338 | 0.65 | 0.4777 | 2.1062 | 0.65 | 0.6267 | 0.2486 | 0.1577 | | No log | 16.0 | 400 | 0.6278 | 0.675 | 0.4482 | 2.3275 | 0.675 | 0.6822 | 0.2425 | 0.1606 | | No log | 17.0 | 425 | 0.5165 | 0.69 | 0.4524 | 2.1670 | 0.69 | 0.6661 | 0.2650 | 0.1554 | | No log | 18.0 | 450 | 0.6064 | 0.64 | 0.4978 | 2.5380 | 0.64 | 0.5838 | 0.2508 | 0.1850 | | No log | 19.0 | 475 | 0.6753 | 0.645 | 0.5125 | 2.4190 | 0.645 | 0.5875 | 0.2664 | 0.2124 | | 0.3785 | 20.0 | 500 | 0.6946 | 0.65 | 0.5331 | 3.1366 | 0.65 | 0.6336 | 0.2715 | 0.2605 | | 0.3785 | 21.0 | 525 | 0.5328 | 0.695 | 0.4699 | 2.8365 | 0.695 | 0.6863 | 0.2500 | 0.1660 | | 0.3785 | 22.0 | 550 | 0.6684 | 0.62 | 0.5374 | 3.2087 | 0.62 | 0.6071 | 0.2363 | 0.2429 | | 0.3785 | 23.0 | 575 | 0.7235 | 0.615 | 0.5613 | 3.4750 | 0.615 | 0.5866 | 0.2748 | 0.2185 | | 0.3785 | 24.0 | 600 | 0.6748 | 0.67 | 0.5028 | 3.0615 | 0.67 | 0.6185 | 0.2697 | 0.2251 | | 0.3785 | 25.0 | 625 | 0.6778 | 0.645 | 0.5068 | 2.7608 | 0.645 | 0.6235 | 0.2442 | 0.1589 | | 0.3785 | 26.0 | 650 | 0.7163 | 0.6 | 0.5690 | 2.7443 | 0.6 | 0.5766 | 0.2655 | 0.2821 | | 0.3785 | 27.0 | 675 | 0.7571 | 0.635 | 0.5278 | 3.3670 | 0.635 | 0.6085 | 0.2635 | 0.2025 | | 0.3785 | 28.0 | 700 | 0.6955 | 0.605 | 0.5718 | 3.1717 | 0.605 | 0.5973 | 0.3092 | 0.2624 | | 0.3785 | 29.0 | 725 | 0.7951 | 0.585 | 0.5869 | 3.2346 | 0.585 | 0.5777 | 0.2849 | 0.3039 | | 0.3785 | 30.0 | 750 | 0.5426 | 0.655 | 0.4898 | 2.6384 | 0.655 | 0.6295 | 0.2758 | 0.1781 | | 0.3785 | 31.0 | 775 | 0.7721 | 0.6 | 0.5956 | 3.5480 | 0.6 | 0.5717 | 0.2908 | 0.2548 | | 0.3785 | 32.0 | 800 | 0.6102 | 0.65 | 0.4974 | 2.6613 | 0.65 | 0.6348 | 0.2529 | 0.1661 | | 0.3785 | 33.0 | 825 | 0.7592 | 0.62 | 0.5666 | 3.5174 | 0.62 | 0.5736 | 0.2821 | 0.2752 | | 0.3785 | 34.0 | 850 | 0.6516 | 0.655 | 0.5283 | 3.1254 | 0.655 | 0.6341 | 0.2596 | 0.1954 | | 0.3785 | 35.0 | 875 | 0.6626 | 0.65 | 0.5329 | 2.9794 | 0.65 | 0.6120 | 0.2793 | 0.2390 | | 0.3785 | 36.0 | 900 | 0.6939 | 0.66 | 0.5190 | 3.4020 | 0.66 | 0.6258 | 0.2473 | 0.1785 | | 0.3785 | 37.0 | 925 | 0.7580 | 0.605 | 0.5970 | 3.1545 | 0.605 | 0.5466 | 0.2996 | 0.2424 | | 0.3785 | 38.0 | 950 | 0.6088 | 0.655 | 0.5187 | 2.7205 | 0.655 | 0.6457 | 0.2636 | 0.2413 | | 0.3785 | 39.0 | 975 | 0.7394 | 0.605 | 0.5815 | 2.8167 | 0.605 | 0.5798 | 0.2975 | 0.2782 | | 0.0886 | 40.0 | 1000 | 0.6910 | 0.65 | 0.5015 | 2.9680 | 0.65 | 0.5993 | 0.2697 | 0.1652 | | 0.0886 | 41.0 | 1025 | 0.6618 | 0.635 | 0.5752 | 3.5088 | 0.635 | 0.5937 | 0.2929 | 0.2653 | | 0.0886 | 42.0 | 1050 | 0.7742 | 0.6 | 0.5556 | 3.5946 | 0.6 | 0.5644 | 0.2556 | 0.1974 | | 0.0886 | 43.0 | 1075 | 0.7379 | 0.62 | 0.5589 | 2.7882 | 0.62 | 0.6042 | 0.3169 | 0.2143 | | 0.0886 | 44.0 | 1100 | 0.6702 | 0.64 | 0.5359 | 2.9335 | 0.64 | 0.6088 | 0.2765 | 0.2133 | | 0.0886 | 45.0 | 1125 | 0.8900 | 0.585 | 0.6173 | 3.8349 | 0.585 | 0.5639 | 0.2934 | 0.2364 | | 0.0886 | 46.0 | 1150 | 0.7800 | 0.62 | 0.5707 | 3.2446 | 0.62 | 0.6171 | 0.3002 | 0.2156 | | 0.0886 | 47.0 | 1175 | 0.8554 | 0.57 | 0.6256 | 3.6828 | 0.57 | 0.5583 | 0.3191 | 0.2611 | | 0.0886 | 48.0 | 1200 | 0.6486 | 0.67 | 0.4911 | 3.4792 | 0.67 | 0.6449 | 0.2741 | 0.1870 | | 0.0886 | 49.0 | 1225 | 0.7315 | 0.59 | 0.5829 | 3.4916 | 0.59 | 0.5963 | 0.2720 | 0.2101 | | 0.0886 | 50.0 | 1250 | 0.6939 | 0.665 | 0.5022 | 2.9091 | 0.665 | 0.6362 | 0.2743 | 0.1829 | | 0.0886 | 51.0 | 1275 | 0.7256 | 0.625 | 0.5687 | 3.4914 | 0.625 | 0.5740 | 0.2943 | 0.2493 | | 0.0886 | 52.0 | 1300 | 0.6374 | 0.66 | 0.5144 | 2.7071 | 0.66 | 0.6297 | 0.2529 | 0.2006 | | 0.0886 | 53.0 | 1325 | 0.7862 | 0.645 | 0.5470 | 3.2902 | 0.645 | 0.6385 | 0.2899 | 0.2053 | | 0.0886 | 54.0 | 1350 | 0.7717 | 0.63 | 0.5762 | 3.8614 | 0.63 | 0.6027 | 0.2954 | 0.2150 | | 0.0886 | 55.0 | 1375 | 0.6664 | 0.675 | 0.5120 | 3.1014 | 0.675 | 0.6582 | 0.2850 | 0.1842 | | 0.0886 | 56.0 | 1400 | 0.6957 | 0.615 | 0.5602 | 3.0253 | 0.615 | 0.5977 | 0.3033 | 0.2229 | | 0.0886 | 57.0 | 1425 | 0.6794 | 0.64 | 0.5581 | 3.0174 | 0.64 | 0.6205 | 0.2802 | 0.2056 | | 0.0886 | 58.0 | 1450 | 0.6345 | 0.655 | 0.5162 | 2.7909 | 0.655 | 0.6422 | 0.2856 | 0.2789 | | 0.0886 | 59.0 | 1475 | 0.6447 | 0.655 | 0.5271 | 2.9860 | 0.655 | 0.6432 | 0.2735 | 0.1774 | | 0.0219 | 60.0 | 1500 | 0.7042 | 0.665 | 0.5404 | 3.1132 | 0.665 | 0.6268 | 0.2871 | 0.2981 | | 0.0219 | 61.0 | 1525 | 0.7288 | 0.64 | 0.5486 | 3.3084 | 0.64 | 0.6225 | 0.2869 | 0.1861 | | 0.0219 | 62.0 | 1550 | 0.6605 | 0.69 | 0.5078 | 2.9123 | 0.69 | 0.6642 | 0.2668 | 0.2487 | | 0.0219 | 63.0 | 1575 | 0.5905 | 0.715 | 0.4712 | 3.4707 | 0.715 | 0.7013 | 0.2548 | 0.2257 | | 0.0219 | 64.0 | 1600 | 0.6209 | 0.69 | 0.4940 | 2.6873 | 0.69 | 0.6770 | 0.2771 | 0.2263 | | 0.0219 | 65.0 | 1625 | 0.6039 | 0.68 | 0.4914 | 2.6448 | 0.68 | 0.6620 | 0.2926 | 0.2184 | | 0.0219 | 66.0 | 1650 | 0.5985 | 0.69 | 0.4918 | 2.7592 | 0.69 | 0.6757 | 0.2844 | 0.2181 | | 0.0219 | 67.0 | 1675 | 0.5955 | 0.69 | 0.4903 | 2.7566 | 0.69 | 0.6757 | 0.2617 | 0.2227 | | 0.0219 | 68.0 | 1700 | 0.5944 | 0.69 | 0.4898 | 2.7730 | 0.69 | 0.6757 | 0.2683 | 0.2211 | | 0.0219 | 69.0 | 1725 | 0.5934 | 0.695 | 0.4893 | 2.7575 | 0.695 | 0.6823 | 0.2666 | 0.2171 | | 0.0219 | 70.0 | 1750 | 0.5913 | 0.695 | 0.4890 | 2.7043 | 0.695 | 0.6823 | 0.2649 | 0.2160 | | 0.0219 | 71.0 | 1775 | 0.5904 | 0.69 | 0.4888 | 2.7476 | 0.69 | 0.6742 | 0.2718 | 0.2163 | | 0.0219 | 72.0 | 1800 | 0.5895 | 0.69 | 0.4883 | 2.7463 | 0.69 | 0.6742 | 0.2714 | 0.2160 | | 0.0219 | 73.0 | 1825 | 0.5882 | 0.69 | 0.4877 | 2.7478 | 0.69 | 0.6742 | 0.2779 | 0.2171 | | 0.0219 | 74.0 | 1850 | 0.5878 | 0.69 | 0.4876 | 2.7489 | 0.69 | 0.6742 | 0.2813 | 0.2169 | | 0.0219 | 75.0 | 1875 | 0.5879 | 0.69 | 0.4871 | 2.7592 | 0.69 | 0.6742 | 0.2765 | 0.2185 | | 0.0219 | 76.0 | 1900 | 0.5868 | 0.69 | 0.4870 | 2.8058 | 0.69 | 0.6742 | 0.2670 | 0.2183 | | 0.0219 | 77.0 | 1925 | 0.5843 | 0.69 | 0.4864 | 2.8037 | 0.69 | 0.6745 | 0.2764 | 0.2185 | | 0.0219 | 78.0 | 1950 | 0.5844 | 0.69 | 0.4862 | 2.8040 | 0.69 | 0.6745 | 0.2788 | 0.2191 | | 0.0219 | 79.0 | 1975 | 0.5831 | 0.69 | 0.4857 | 2.8018 | 0.69 | 0.6745 | 0.2655 | 0.2178 | | 0.0013 | 80.0 | 2000 | 0.5846 | 0.69 | 0.4858 | 2.8022 | 0.69 | 0.6745 | 0.2633 | 0.2182 | | 0.0013 | 81.0 | 2025 | 0.5821 | 0.69 | 0.4851 | 2.8020 | 0.69 | 0.6745 | 0.2750 | 0.2177 | | 0.0013 | 82.0 | 2050 | 0.5826 | 0.685 | 0.4852 | 2.8013 | 0.685 | 0.6713 | 0.2728 | 0.2180 | | 0.0013 | 83.0 | 2075 | 0.5821 | 0.685 | 0.4851 | 2.8005 | 0.685 | 0.6713 | 0.2705 | 0.2179 | | 0.0013 | 84.0 | 2100 | 0.5817 | 0.685 | 0.4850 | 2.8007 | 0.685 | 0.6713 | 0.2773 | 0.2180 | | 0.0013 | 85.0 | 2125 | 0.5814 | 0.685 | 0.4849 | 2.7998 | 0.685 | 0.6713 | 0.2740 | 0.2176 | | 0.0013 | 86.0 | 2150 | 0.5814 | 0.685 | 0.4848 | 2.7997 | 0.685 | 0.6713 | 0.2686 | 0.2174 | | 0.0013 | 87.0 | 2175 | 0.5807 | 0.68 | 0.4847 | 2.7994 | 0.68 | 0.6624 | 0.2658 | 0.2186 | | 0.0013 | 88.0 | 2200 | 0.5803 | 0.68 | 0.4845 | 2.7992 | 0.68 | 0.6624 | 0.2703 | 0.2180 | | 0.0013 | 89.0 | 2225 | 0.5805 | 0.68 | 0.4846 | 2.7990 | 0.68 | 0.6624 | 0.2632 | 0.2194 | | 0.0013 | 90.0 | 2250 | 0.5803 | 0.685 | 0.4846 | 2.7979 | 0.685 | 0.6703 | 0.2596 | 0.2183 | | 0.0013 | 91.0 | 2275 | 0.5805 | 0.685 | 0.4847 | 2.7980 | 0.685 | 0.6703 | 0.2674 | 0.2183 | | 0.0013 | 92.0 | 2300 | 0.5805 | 0.685 | 0.4846 | 2.7993 | 0.685 | 0.6703 | 0.2562 | 0.2181 | | 0.0013 | 93.0 | 2325 | 0.5802 | 0.68 | 0.4847 | 2.7974 | 0.68 | 0.6624 | 0.2598 | 0.2182 | | 0.0013 | 94.0 | 2350 | 0.5800 | 0.68 | 0.4846 | 2.7981 | 0.68 | 0.6624 | 0.2613 | 0.2175 | | 0.0013 | 95.0 | 2375 | 0.5796 | 0.68 | 0.4846 | 2.7985 | 0.68 | 0.6624 | 0.2589 | 0.2179 | | 0.0013 | 96.0 | 2400 | 0.5799 | 0.68 | 0.4846 | 2.7980 | 0.68 | 0.6624 | 0.2560 | 0.2183 | | 0.0013 | 97.0 | 2425 | 0.5796 | 0.68 | 0.4846 | 2.7978 | 0.68 | 0.6624 | 0.2588 | 0.2175 | | 0.0013 | 98.0 | 2450 | 0.5798 | 0.68 | 0.4846 | 2.7977 | 0.68 | 0.6624 | 0.2589 | 0.2179 | | 0.0013 | 99.0 | 2475 | 0.5797 | 0.68 | 0.4846 | 2.7977 | 0.68 | 0.6624 | 0.2589 | 0.2178 | | 0.0003 | 100.0 | 2500 | 0.5797 | 0.68 | 0.4846 | 2.7977 | 0.68 | 0.6624 | 0.2589 | 0.2179 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

achyudev/vit-base-patch16-224-in21k

# vit-base-patch16-224-in21k This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3383 - F1: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 10 | 0.7369 | 0.9461 | | No log | 2.0 | 20 | 0.6143 | 0.9526 | | No log | 3.0 | 30 | 0.5401 | 0.9594 | | No log | 4.0 | 40 | 0.4809 | 0.9662 | | No log | 5.0 | 50 | 0.4219 | 0.9595 | | No log | 6.0 | 60 | 0.3864 | 0.9730 | | No log | 7.0 | 70 | 0.3628 | 0.9730 | | No log | 8.0 | 80 | 0.3496 | 0.9732 | | No log | 9.0 | 90 | 0.3419 | 0.9799 | | No log | 10.0 | 100 | 0.3383 | 0.9799 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

[ "viola", "cello", "sax", "flute", "trumpet", "oboe" ]

Akshay-123/vit-base-patch16-224-in21k

# vit-base-patch16-224-in21k This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7692 - F1: 0.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 10 | 1.5877 | 0.6675 | | No log | 2.0 | 20 | 1.4149 | 0.8402 | | No log | 3.0 | 30 | 1.2687 | 0.8917 | | No log | 4.0 | 40 | 1.1382 | 0.9113 | | No log | 5.0 | 50 | 1.0214 | 0.9523 | | No log | 6.0 | 60 | 0.9285 | 0.9662 | | No log | 7.0 | 70 | 0.8601 | 0.9728 | | No log | 8.0 | 80 | 0.8089 | 0.9797 | | No log | 9.0 | 90 | 0.7796 | 0.9865 | | No log | 10.0 | 100 | 0.7692 | 0.9865 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "cello", "oboe", "sax", "viola", "trumpet", "flute" ]

Schwab/vit-base-patch16-224-finetuned-flower

# vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]

3b3r/vit_model

# vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0414 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1498 | 3.85 | 500 | 0.0414 | 0.9925 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "angular_leaf_spot", "bean_rust", "healthy" ]

jordyvl/vit-base_rvl_tobacco

# vit-base_rvl_tobacco This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4152 - Accuracy: 0.905 - Brier Loss: 0.1584 - Nll: 0.7130 - F1 Micro: 0.905 - F1 Macro: 0.9056 - Ece: 0.1601 - Aurc: 0.0196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 2.3234 | 0.045 | 0.9050 | 9.6090 | 0.045 | 0.0479 | 0.1570 | 0.9674 | | No log | 1.96 | 6 | 2.3007 | 0.05 | 0.9005 | 8.5690 | 0.0500 | 0.0549 | 0.1567 | 0.9599 | | No log | 2.96 | 9 | 2.2614 | 0.095 | 0.8924 | 6.9011 | 0.095 | 0.0853 | 0.1807 | 0.9128 | | No log | 3.96 | 12 | 2.2062 | 0.255 | 0.8804 | 5.5442 | 0.255 | 0.1609 | 0.2738 | 0.7469 | | No log | 4.96 | 15 | 2.1348 | 0.385 | 0.8636 | 4.0613 | 0.3850 | 0.2330 | 0.3605 | 0.4157 | | No log | 5.96 | 18 | 2.0473 | 0.48 | 0.8410 | 2.5353 | 0.48 | 0.3152 | 0.4376 | 0.2329 | | No log | 6.96 | 21 | 1.9483 | 0.64 | 0.8128 | 2.0469 | 0.64 | 0.5131 | 0.5355 | 0.1314 | | No log | 7.96 | 24 | 1.8371 | 0.735 | 0.7783 | 1.7309 | 0.735 | 0.6333 | 0.5897 | 0.0802 | | No log | 8.96 | 27 | 1.7227 | 0.775 | 0.7393 | 1.3371 | 0.775 | 0.6937 | 0.6049 | 0.0560 | | No log | 9.96 | 30 | 1.6124 | 0.805 | 0.6978 | 1.1320 | 0.805 | 0.7319 | 0.5981 | 0.0462 | | No log | 10.96 | 33 | 1.4990 | 0.82 | 0.6518 | 0.9973 | 0.82 | 0.7658 | 0.5882 | 0.0444 | | No log | 11.96 | 36 | 1.3922 | 0.855 | 0.6064 | 0.8830 | 0.855 | 0.8127 | 0.5823 | 0.0397 | | No log | 12.96 | 39 | 1.2985 | 0.865 | 0.5653 | 0.8957 | 0.865 | 0.8350 | 0.5604 | 0.0365 | | No log | 13.96 | 42 | 1.2141 | 0.89 | 0.5271 | 0.6892 | 0.89 | 0.8733 | 0.5564 | 0.0331 | | No log | 14.96 | 45 | 1.1402 | 0.895 | 0.4926 | 0.6695 | 0.895 | 0.8803 | 0.5341 | 0.0321 | | No log | 15.96 | 48 | 1.0699 | 0.91 | 0.4596 | 0.6407 | 0.91 | 0.8999 | 0.5185 | 0.0285 | | No log | 16.96 | 51 | 1.0037 | 0.91 | 0.4282 | 0.6163 | 0.91 | 0.8979 | 0.4831 | 0.0270 | | No log | 17.96 | 54 | 0.9457 | 0.915 | 0.4004 | 0.6126 | 0.915 | 0.9011 | 0.4618 | 0.0247 | | No log | 18.96 | 57 | 0.8914 | 0.915 | 0.3742 | 0.6066 | 0.915 | 0.9011 | 0.4426 | 0.0242 | | No log | 19.96 | 60 | 0.8405 | 0.92 | 0.3495 | 0.5898 | 0.92 | 0.9102 | 0.4314 | 0.0216 | | No log | 20.96 | 63 | 0.7995 | 0.915 | 0.3291 | 0.5934 | 0.915 | 0.9049 | 0.4033 | 0.0204 | | No log | 21.96 | 66 | 0.7583 | 0.915 | 0.3089 | 0.5883 | 0.915 | 0.9049 | 0.3818 | 0.0206 | | No log | 22.96 | 69 | 0.7228 | 0.915 | 0.2915 | 0.5835 | 0.915 | 0.9049 | 0.3707 | 0.0199 | | No log | 23.96 | 72 | 0.6889 | 0.925 | 0.2747 | 0.5703 | 0.925 | 0.9169 | 0.3649 | 0.0191 | | No log | 24.96 | 75 | 0.6624 | 0.925 | 0.2614 | 0.5769 | 0.925 | 0.9200 | 0.3375 | 0.0190 | | No log | 25.96 | 78 | 0.6373 | 0.925 | 0.2491 | 0.5764 | 0.925 | 0.9218 | 0.3206 | 0.0191 | | No log | 26.96 | 81 | 0.6106 | 0.93 | 0.2363 | 0.5570 | 0.93 | 0.9251 | 0.3276 | 0.0186 | | No log | 27.96 | 84 | 0.5945 | 0.93 | 0.2281 | 0.5721 | 0.93 | 0.9251 | 0.3201 | 0.0187 | | No log | 28.96 | 87 | 0.5780 | 0.92 | 0.2206 | 0.5668 | 0.92 | 0.9190 | 0.3008 | 0.0200 | | No log | 29.96 | 90 | 0.5613 | 0.925 | 0.2125 | 0.5709 | 0.925 | 0.9218 | 0.2961 | 0.0191 | | No log | 30.96 | 93 | 0.5456 | 0.925 | 0.2051 | 0.6155 | 0.925 | 0.9175 | 0.2764 | 0.0182 | | No log | 31.96 | 96 | 0.5354 | 0.91 | 0.2008 | 0.6139 | 0.91 | 0.9104 | 0.2600 | 0.0187 | | No log | 32.96 | 99 | 0.5248 | 0.91 | 0.1961 | 0.6078 | 0.91 | 0.9104 | 0.2610 | 0.0194 | | No log | 33.96 | 102 | 0.5151 | 0.91 | 0.1915 | 0.6158 | 0.91 | 0.9084 | 0.2529 | 0.0186 | | No log | 34.96 | 105 | 0.5066 | 0.91 | 0.1880 | 0.6121 | 0.91 | 0.9084 | 0.2409 | 0.0186 | | No log | 35.96 | 108 | 0.4986 | 0.91 | 0.1846 | 0.6070 | 0.91 | 0.9084 | 0.2429 | 0.0186 | | No log | 36.96 | 111 | 0.4920 | 0.91 | 0.1817 | 0.6208 | 0.91 | 0.9084 | 0.2380 | 0.0187 | | No log | 37.96 | 114 | 0.4858 | 0.91 | 0.1793 | 0.6081 | 0.91 | 0.9084 | 0.2319 | 0.0185 | | No log | 38.96 | 117 | 0.4792 | 0.91 | 0.1766 | 0.6044 | 0.91 | 0.9084 | 0.2276 | 0.0184 | | No log | 39.96 | 120 | 0.4753 | 0.91 | 0.1749 | 0.6671 | 0.91 | 0.9084 | 0.2245 | 0.0185 | | No log | 40.96 | 123 | 0.4704 | 0.905 | 0.1731 | 0.6137 | 0.905 | 0.9056 | 0.2321 | 0.0186 | | No log | 41.96 | 126 | 0.4656 | 0.91 | 0.1714 | 0.6028 | 0.91 | 0.9084 | 0.2259 | 0.0187 | | No log | 42.96 | 129 | 0.4624 | 0.91 | 0.1703 | 0.6048 | 0.91 | 0.9084 | 0.2080 | 0.0189 | | No log | 43.96 | 132 | 0.4604 | 0.905 | 0.1695 | 0.6674 | 0.905 | 0.9056 | 0.2167 | 0.0187 | | No log | 44.96 | 135 | 0.4553 | 0.905 | 0.1678 | 0.6190 | 0.905 | 0.9056 | 0.2130 | 0.0185 | | No log | 45.96 | 138 | 0.4512 | 0.905 | 0.1663 | 0.6002 | 0.905 | 0.9056 | 0.2182 | 0.0186 | | No log | 46.96 | 141 | 0.4513 | 0.905 | 0.1665 | 0.6681 | 0.905 | 0.9056 | 0.1902 | 0.0185 | | No log | 47.96 | 144 | 0.4480 | 0.905 | 0.1656 | 0.6661 | 0.905 | 0.9056 | 0.1900 | 0.0186 | | No log | 48.96 | 147 | 0.4451 | 0.905 | 0.1647 | 0.6085 | 0.905 | 0.9056 | 0.1969 | 0.0185 | | No log | 49.96 | 150 | 0.4429 | 0.905 | 0.1638 | 0.6729 | 0.905 | 0.9056 | 0.1954 | 0.0186 | | No log | 50.96 | 153 | 0.4416 | 0.905 | 0.1637 | 0.7300 | 0.905 | 0.9056 | 0.1730 | 0.0188 | | No log | 51.96 | 156 | 0.4390 | 0.905 | 0.1627 | 0.6832 | 0.905 | 0.9056 | 0.1881 | 0.0187 | | No log | 52.96 | 159 | 0.4377 | 0.905 | 0.1625 | 0.6708 | 0.905 | 0.9056 | 0.1724 | 0.0187 | | No log | 53.96 | 162 | 0.4360 | 0.905 | 0.1620 | 0.7300 | 0.905 | 0.9056 | 0.1714 | 0.0189 | | No log | 54.96 | 165 | 0.4338 | 0.905 | 0.1613 | 0.6734 | 0.905 | 0.9056 | 0.1923 | 0.0190 | | No log | 55.96 | 168 | 0.4321 | 0.905 | 0.1609 | 0.6635 | 0.905 | 0.9056 | 0.1846 | 0.0189 | | No log | 56.96 | 171 | 0.4326 | 0.905 | 0.1614 | 0.6722 | 0.905 | 0.9056 | 0.1851 | 0.0190 | | No log | 57.96 | 174 | 0.4322 | 0.905 | 0.1613 | 0.7871 | 0.905 | 0.9056 | 0.1850 | 0.0191 | | No log | 58.96 | 177 | 0.4286 | 0.905 | 0.1600 | 0.6660 | 0.905 | 0.9056 | 0.1733 | 0.0190 | | No log | 59.96 | 180 | 0.4267 | 0.905 | 0.1596 | 0.6581 | 0.905 | 0.9056 | 0.1720 | 0.0190 | | No log | 60.96 | 183 | 0.4277 | 0.905 | 0.1601 | 0.7252 | 0.905 | 0.9056 | 0.1772 | 0.0189 | | No log | 61.96 | 186 | 0.4274 | 0.905 | 0.1601 | 0.7841 | 0.905 | 0.9056 | 0.1866 | 0.0192 | | No log | 62.96 | 189 | 0.4264 | 0.905 | 0.1598 | 0.7830 | 0.905 | 0.9056 | 0.1669 | 0.0191 | | No log | 63.96 | 192 | 0.4246 | 0.905 | 0.1595 | 0.7188 | 0.905 | 0.9056 | 0.1671 | 0.0191 | | No log | 64.96 | 195 | 0.4236 | 0.905 | 0.1592 | 0.7170 | 0.905 | 0.9056 | 0.1762 | 0.0193 | | No log | 65.96 | 198 | 0.4238 | 0.905 | 0.1594 | 0.7235 | 0.905 | 0.9056 | 0.1757 | 0.0192 | | No log | 66.96 | 201 | 0.4227 | 0.905 | 0.1591 | 0.7218 | 0.905 | 0.9056 | 0.1724 | 0.0192 | | No log | 67.96 | 204 | 0.4220 | 0.905 | 0.1590 | 0.7195 | 0.905 | 0.9056 | 0.1715 | 0.0191 | | No log | 68.96 | 207 | 0.4214 | 0.905 | 0.1589 | 0.7201 | 0.905 | 0.9056 | 0.1708 | 0.0191 | | No log | 69.96 | 210 | 0.4210 | 0.905 | 0.1588 | 0.7210 | 0.905 | 0.9056 | 0.1703 | 0.0193 | | No log | 70.96 | 213 | 0.4211 | 0.905 | 0.1590 | 0.7226 | 0.905 | 0.9056 | 0.1697 | 0.0193 | | No log | 71.96 | 216 | 0.4201 | 0.905 | 0.1587 | 0.7165 | 0.905 | 0.9056 | 0.1785 | 0.0193 | | No log | 72.96 | 219 | 0.4194 | 0.905 | 0.1587 | 0.7145 | 0.905 | 0.9056 | 0.1780 | 0.0194 | | No log | 73.96 | 222 | 0.4194 | 0.905 | 0.1587 | 0.7189 | 0.905 | 0.9056 | 0.1777 | 0.0194 | | No log | 74.96 | 225 | 0.4192 | 0.905 | 0.1587 | 0.7193 | 0.905 | 0.9056 | 0.1770 | 0.0194 | | No log | 75.96 | 228 | 0.4188 | 0.905 | 0.1586 | 0.7186 | 0.905 | 0.9056 | 0.1764 | 0.0192 | | No log | 76.96 | 231 | 0.4180 | 0.905 | 0.1585 | 0.7148 | 0.905 | 0.9056 | 0.1786 | 0.0192 | | No log | 77.96 | 234 | 0.4174 | 0.905 | 0.1584 | 0.7121 | 0.905 | 0.9056 | 0.1746 | 0.0193 | | No log | 78.96 | 237 | 0.4178 | 0.905 | 0.1585 | 0.7159 | 0.905 | 0.9056 | 0.1720 | 0.0195 | | No log | 79.96 | 240 | 0.4177 | 0.905 | 0.1586 | 0.7161 | 0.905 | 0.9056 | 0.1627 | 0.0195 | | No log | 80.96 | 243 | 0.4173 | 0.905 | 0.1585 | 0.7147 | 0.905 | 0.9056 | 0.1627 | 0.0195 | | No log | 81.96 | 246 | 0.4171 | 0.905 | 0.1585 | 0.7159 | 0.905 | 0.9056 | 0.1650 | 0.0195 | | No log | 82.96 | 249 | 0.4162 | 0.905 | 0.1582 | 0.7135 | 0.905 | 0.9056 | 0.1742 | 0.0194 | | No log | 83.96 | 252 | 0.4163 | 0.905 | 0.1584 | 0.7138 | 0.905 | 0.9056 | 0.1522 | 0.0196 | | No log | 84.96 | 255 | 0.4161 | 0.905 | 0.1583 | 0.7136 | 0.905 | 0.9056 | 0.1616 | 0.0195 | | No log | 85.96 | 258 | 0.4163 | 0.905 | 0.1585 | 0.7143 | 0.905 | 0.9056 | 0.1615 | 0.0196 | | No log | 86.96 | 261 | 0.4161 | 0.905 | 0.1585 | 0.7132 | 0.905 | 0.9056 | 0.1614 | 0.0195 | | No log | 87.96 | 264 | 0.4159 | 0.905 | 0.1584 | 0.7133 | 0.905 | 0.9056 | 0.1514 | 0.0195 | | No log | 88.96 | 267 | 0.4157 | 0.905 | 0.1584 | 0.7132 | 0.905 | 0.9056 | 0.1513 | 0.0195 | | No log | 89.96 | 270 | 0.4156 | 0.905 | 0.1584 | 0.7134 | 0.905 | 0.9056 | 0.1511 | 0.0195 | | No log | 90.96 | 273 | 0.4153 | 0.905 | 0.1583 | 0.7124 | 0.905 | 0.9056 | 0.1605 | 0.0195 | | No log | 91.96 | 276 | 0.4153 | 0.905 | 0.1584 | 0.7121 | 0.905 | 0.9056 | 0.1604 | 0.0195 | | No log | 92.96 | 279 | 0.4154 | 0.905 | 0.1584 | 0.7127 | 0.905 | 0.9056 | 0.1603 | 0.0195 | | No log | 93.96 | 282 | 0.4154 | 0.905 | 0.1585 | 0.7131 | 0.905 | 0.9056 | 0.1603 | 0.0195 | | No log | 94.96 | 285 | 0.4154 | 0.905 | 0.1585 | 0.7132 | 0.905 | 0.9056 | 0.1603 | 0.0195 | | No log | 95.96 | 288 | 0.4154 | 0.905 | 0.1585 | 0.7135 | 0.905 | 0.9056 | 0.1603 | 0.0196 | | No log | 96.96 | 291 | 0.4153 | 0.905 | 0.1585 | 0.7133 | 0.905 | 0.9056 | 0.1602 | 0.0195 | | No log | 97.96 | 294 | 0.4152 | 0.905 | 0.1584 | 0.7132 | 0.905 | 0.9056 | 0.1601 | 0.0196 | | No log | 98.96 | 297 | 0.4152 | 0.905 | 0.1584 | 0.7130 | 0.905 | 0.9056 | 0.1601 | 0.0196 | | No log | 99.96 | 300 | 0.4152 | 0.905 | 0.1584 | 0.7130 | 0.905 | 0.9056 | 0.1601 | 0.0196 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

XO-Appleton/vit-base-patch16-224-in21k-MR

# XO-Appleton/vit-base-patch16-224-in21k-MR This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0042 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 0.0126 - Validation Accuracy: 0.9983 - Validation Top-3-accuracy: 1.0 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4485, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 0.1624 | 0.9544 | 1.0 | 0.0380 | 0.9933 | 1.0 | 0 | | 0.0178 | 0.9979 | 1.0 | 0.0197 | 0.9966 | 1.0 | 1 | | 0.0063 | 1.0 | 1.0 | 0.0139 | 0.9983 | 1.0 | 2 | | 0.0042 | 1.0 | 1.0 | 0.0126 | 0.9983 | 1.0 | 3 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "flip", "notflip" ]

sghirardelli/vit-base-patch16-224-in21k-rgbd

# vit-base-patch16-224-in21k-rgbd This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5496 - Train Accuracy: 1.0 - Train Top-3-accuracy: 1.0 - Validation Loss: 0.3955 - Validation Accuracy: 0.9994 - Validation Top-3-accuracy: 1.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 1.6822 | 0.9392 | 0.9664 | 0.7810 | 0.9994 | 1.0 | 0 | | 0.5496 | 1.0 | 1.0 | 0.3955 | 0.9994 | 1.0 | 1 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "apple", "ball", "cereal_box", "coffee_mug", "comb", "dry_battery", "flashlight", "food_bag", "food_box", "food_can", "food_cup", "food_jar", "banana", "garlic", "glue_stick", "greens", "hand_towel", "instant_noodles", "keyboard", "kleenex", "lemon", "lightbulb", "lime", "bell_pepper", "marker", "mushroom", "notebook", "onion", "orange", "peach", "pear", "pitcher", "plate", "pliers", "binder", "potato", "rubber_eraser", "scissors", "shampoo", "soda_can", "sponge", "stapler", "tomato", "toothbrush", "toothpaste", "bowl", "water_bottle", "calculator", "camera", "cap", "cell_phone" ]

ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20

# beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0275 - Accuracy: 0.9939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.46 | 1.0 | 199 | 0.3950 | 0.8482 | | 0.2048 | 2.0 | 398 | 0.1886 | 0.9189 | | 0.182 | 3.0 | 597 | 0.1382 | 0.9481 | | 0.0826 | 4.0 | 796 | 0.0760 | 0.9694 | | 0.0886 | 5.0 | 995 | 0.0600 | 0.9788 | | 0.0896 | 6.0 | 1194 | 0.0523 | 0.9802 | | 0.0774 | 7.0 | 1393 | 0.0482 | 0.9826 | | 0.0876 | 8.0 | 1592 | 0.0289 | 0.9877 | | 0.1105 | 9.0 | 1791 | 0.0580 | 0.9821 | | 0.0289 | 10.0 | 1990 | 0.0294 | 0.9925 | | 0.0594 | 11.0 | 2189 | 0.0331 | 0.9906 | | 0.0011 | 12.0 | 2388 | 0.0275 | 0.9939 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "benign", "malignant" ]

ALM-AHME/convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20

# convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0353 - Accuracy: 0.9901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5207 | 1.0 | 199 | 0.4745 | 0.8887 | | 0.2029 | 2.0 | 398 | 0.2072 | 0.9401 | | 0.1615 | 3.0 | 597 | 0.1489 | 0.9547 | | 0.1662 | 4.0 | 796 | 0.1312 | 0.9562 | | 0.1986 | 5.0 | 995 | 0.1026 | 0.9698 | | 0.0854 | 6.0 | 1194 | 0.0583 | 0.9802 | | 0.0538 | 7.0 | 1393 | 0.0568 | 0.9835 | | 0.0977 | 8.0 | 1592 | 0.0654 | 0.9793 | | 0.6971 | 9.0 | 1791 | 0.6821 | 0.5450 | | 0.211 | 10.0 | 1990 | 0.1654 | 0.9326 | | 0.1775 | 11.0 | 2189 | 0.0859 | 0.9665 | | 0.0042 | 12.0 | 2388 | 0.0353 | 0.9901 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "benign", "malignant" ]

akar49/mri_classifier

# akar49/mri_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1032 - Validation Loss: 0.1556 - Train Accuracy: 0.9367 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'momentum': 0.0, 'nesterov': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6447 | 0.6133 | 0.7004 | 0 | | 0.5405 | 0.5010 | 0.8256 | 1 | | 0.4181 | 0.3917 | 0.8650 | 2 | | 0.3122 | 0.3189 | 0.9058 | 3 | | 0.2474 | 0.3069 | 0.8875 | 4 | | 0.2021 | 0.2733 | 0.9044 | 5 | | 0.1745 | 0.2455 | 0.9100 | 6 | | 0.1591 | 0.2203 | 0.9212 | 7 | | 0.1450 | 0.2350 | 0.9142 | 8 | | 0.1397 | 0.2122 | 0.9198 | 9 | | 0.1227 | 0.2098 | 0.9212 | 10 | | 0.1169 | 0.1754 | 0.9325 | 11 | | 0.1080 | 0.1782 | 0.9339 | 12 | | 0.0971 | 0.1705 | 0.9353 | 13 | | 0.1032 | 0.1556 | 0.9367 | 14 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "notumor", "tumor" ]

jvadlamudi2/swin-tiny-patch4-window7-224-jvadlamudi2

# swin-tiny-patch4-window7-224-jvadlamudi2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4467 - Accuracy: 0.8304 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.4580 | 0.8214 | | 0.4488 | 2.0 | 14 | 0.4733 | 0.8304 | | 0.4924 | 3.0 | 21 | 0.4467 | 0.8304 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

[ "0", "1" ]

ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20

# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2053 | 1.0 | 199 | 0.1227 | 0.9496 | | 0.1302 | 2.0 | 398 | 0.0665 | 0.9736 | | 0.0784 | 3.0 | 597 | 0.0600 | 0.9778 | | 0.1181 | 4.0 | 796 | 0.0449 | 0.9849 | | 0.208 | 5.0 | 995 | 0.0393 | 0.9887 | | 0.0057 | 6.0 | 1194 | 0.0229 | 0.9943 | | 0.0017 | 7.0 | 1393 | 0.0263 | 0.9939 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "benign", "malignant" ]

dyvapandhu/vit-base-molecul-v2-5-epoch

# vit-base-molecul-v2-5-epoch This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5290 - Accuracy: 0.77 - F1: 0.7698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.11.0

[ "a", "c" ]

margosabry/food_classifier

# margosabry/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3853 - Validation Loss: 0.3150 - Train Accuracy: 0.928 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.8055 | 1.6705 | 0.808 | 0 | | 1.2418 | 0.8233 | 0.883 | 1 | | 0.7004 | 0.5248 | 0.912 | 2 | | 0.5037 | 0.3802 | 0.926 | 3 | | 0.3853 | 0.3150 | 0.928 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

rshrott/vit-base-beans-demo-v5

# vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.8460 - Accuracy: 0.6695 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0616 | 0.17 | 100 | 1.0267 | 0.5818 | | 0.9594 | 0.34 | 200 | 0.9468 | 0.6073 | | 1.1785 | 0.51 | 300 | 0.9976 | 0.5869 | | 0.865 | 0.68 | 400 | 0.9288 | 0.6388 | | 0.8494 | 0.85 | 500 | 0.8573 | 0.6516 | | 0.8151 | 1.02 | 600 | 0.8729 | 0.6397 | | 0.5787 | 1.19 | 700 | 0.9067 | 0.6448 | | 0.7768 | 1.36 | 800 | 0.8996 | 0.6533 | | 0.6098 | 1.53 | 900 | 0.8460 | 0.6695 | | 0.6251 | 1.7 | 1000 | 0.8610 | 0.6704 | | 0.7863 | 1.87 | 1100 | 0.8668 | 0.6431 | | 0.2595 | 2.04 | 1200 | 0.8725 | 0.6840 | | 0.2735 | 2.21 | 1300 | 0.9307 | 0.6746 | | 0.2429 | 2.39 | 1400 | 1.0958 | 0.6354 | | 0.3224 | 2.56 | 1500 | 1.0305 | 0.6687 | | 0.1602 | 2.73 | 1600 | 1.0072 | 0.6746 | | 0.2042 | 2.9 | 1700 | 1.0971 | 0.6789 | | 0.0604 | 3.07 | 1800 | 1.0817 | 0.6917 | | 0.0716 | 3.24 | 1900 | 1.1307 | 0.6925 | | 0.0822 | 3.41 | 2000 | 1.1827 | 0.6925 | | 0.0889 | 3.58 | 2100 | 1.2424 | 0.6934 | | 0.0855 | 3.75 | 2200 | 1.2667 | 0.6899 | | 0.0682 | 3.92 | 2300 | 1.2470 | 0.6951 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "not applicable", "very poor", "poor", "fair", "good", "great", "excellent" ]

jthetzel/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2326 | 1.0 | 190 | 0.1175 | 0.9604 | | 0.1789 | 2.0 | 380 | 0.0765 | 0.9763 | | 0.1414 | 3.0 | 570 | 0.0604 | 0.9822 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

hyeongjin99/vit-base-aihub_model-v2

# vit-base-aihub_model-v2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3076 - Accuracy: 0.9639 - Precision: 0.9610 - Recall: 0.9614 - F1: 0.9604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 3 | 1.2753 | 0.8373 | 0.8563 | 0.7993 | 0.8022 | | No log | 2.0 | 6 | 1.1252 | 0.8675 | 0.8895 | 0.8300 | 0.8333 | | No log | 3.0 | 9 | 0.9427 | 0.8976 | 0.9185 | 0.8696 | 0.8760 | | 1.1721 | 4.0 | 12 | 0.7995 | 0.9398 | 0.9474 | 0.9195 | 0.9246 | | 1.1721 | 5.0 | 15 | 0.6820 | 0.9699 | 0.9704 | 0.9613 | 0.9642 | | 1.1721 | 6.0 | 18 | 0.5927 | 0.9639 | 0.9603 | 0.9583 | 0.9587 | | 0.7084 | 7.0 | 21 | 0.5239 | 0.9759 | 0.9725 | 0.9729 | 0.9725 | | 0.7084 | 8.0 | 24 | 0.4743 | 0.9699 | 0.9665 | 0.9671 | 0.9665 | | 0.7084 | 9.0 | 27 | 0.4436 | 0.9578 | 0.9558 | 0.9556 | 0.9544 | | 0.4668 | 10.0 | 30 | 0.4070 | 0.9639 | 0.9610 | 0.9614 | 0.9604 | | 0.4668 | 11.0 | 33 | 0.3817 | 0.9699 | 0.9665 | 0.9671 | 0.9665 | | 0.4668 | 12.0 | 36 | 0.3625 | 0.9699 | 0.9665 | 0.9671 | 0.9665 | | 0.4668 | 13.0 | 39 | 0.3536 | 0.9578 | 0.9558 | 0.9556 | 0.9544 | | 0.3611 | 14.0 | 42 | 0.3384 | 0.9578 | 0.9558 | 0.9556 | 0.9544 | | 0.3611 | 15.0 | 45 | 0.3249 | 0.9699 | 0.9665 | 0.9671 | 0.9665 | | 0.3611 | 16.0 | 48 | 0.3164 | 0.9699 | 0.9665 | 0.9671 | 0.9665 | | 0.3063 | 17.0 | 51 | 0.3142 | 0.9639 | 0.9610 | 0.9614 | 0.9604 | | 0.3063 | 18.0 | 54 | 0.3122 | 0.9639 | 0.9610 | 0.9614 | 0.9604 | | 0.3063 | 19.0 | 57 | 0.3093 | 0.9639 | 0.9610 | 0.9614 | 0.9604 | | 0.294 | 20.0 | 60 | 0.3076 | 0.9639 | 0.9610 | 0.9614 | 0.9604 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

[ "cloudy", "rainy", "sandy", "shine", "snowy", "sunrise" ]

sezer12138/results

# results This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5248 - Accuracy: 0.6505 ## Model description On ADE20K Dataset, do image classification. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8495 | 1.0 | 158 | 2.3313 | 0.5525 | | 1.8825 | 2.0 | 316 | 1.6815 | 0.623 | | 1.4956 | 3.0 | 474 | 1.5248 | 0.6505 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

[ "abbey", "access_road", "airport_terminal", "bathroom", "village", "vinery", "vineyard", "volcano", "volleyball_court_indoor", "volleyball_court_outdoor", "voting_booth", "waiting_room", "walk_in_freezer", "walkway", "batters_box", "war_room", "warehouse_indoor", "warehouse_outdoor", "washhouse_indoor", "washhouse_outdoor", "washroom", "watchtower", "water", "water_fountain", "water_gate", "batting_cage_indoor", "water_mill", "water_park", "water_tower", "water_treatment_plant_indoor", "water_treatment_plant_outdoor", "watering_hole", "waterscape", "waterway", "wave", "weighbridge", "batting_cage_outdoor", "western", "wet_bar", "wetland", "wharf", "wheat_field", "whispering_gallery", "widows_walk_indoor", "widows_walk_interior", "wild", "wind_farm", "battlefield", "windmill", "window_seat", "windstorm", "winery", "witness_stand", "woodland", "workroom", "workshop", "wrestling_ring_indoor", "wrestling_ring_outdoor", "battlement", "yard", "youth_hostel", "zen_garden", "ziggurat", "zoo", "bay", "bayou", "bazaar_indoor", "bazaar_outdoor", "airport_ticket_counter", "beach", "beach_house", "beauty_salon", "bedchamber", "bedroom", "beer_garden", "beer_hall", "belfry", "bell_foundry", "berth", "alcove", "berth_deck", "betting_shop", "bicycle_racks", "bindery", "biology_laboratory", "bistro_indoor", "bistro_outdoor", "bleachers_indoor", "bleachers_outdoor", "block", "alley", "boardwalk", "boat", "boat_deck", "boathouse", "bog", "bomb_shelter_indoor", "bookbindery", "bookshelf", "bookstore", "booth", "amphitheater", "booth_indoor", "booth_outdoor", "botanical_garden", "bottle_storage", "bottomland", "bow_window_indoor", "bow_window_outdoor", "bowling_alley", "box_seat", "boxing_ring", "amphitheater_indoor", "breakfast_table", "breakroom", "brewery_indoor", "brewery_outdoor", "bric-a-brac", "brickyard_indoor", "brickyard_outdoor", "bridge", "bridle_path", "broadleaf", "amusement_arcade", "brooklet", "bubble_chamber", "buffet", "building_complex", "building_facade", "bulkhead", "bullpen", "bullring", "bunk_bed", "burial_chamber", "amusement_park", "bus_depot_indoor", "bus_depot_outdoor", "bus_interior", "bus_shelter", "bus_station_indoor", "bus_station_outdoor", "butchers_shop", "butte", "bypass", "byroad", "anechoic_chamber", "cabana", "cabin_cruiser", "cabin_indoor", "cabin_outdoor", "cafeteria", "call_center", "campsite", "campus", "candy_store", "canteen", "apartment_building_outdoor", "canyon", "car_dealership", "caravansary", "cardroom", "cargo_container_interior", "cargo_deck", "cargo_helicopter", "carport_indoor", "carport_outdoor", "carrousel", "acropolis", "apse_indoor", "cascade", "casino_indoor", "casino_outdoor", "castle", "catacomb", "cataract", "cathedral_indoor", "cathedral_outdoor", "catwalk", "cavern_indoor", "apse_outdoor", "cavern_outdoor", "cellar", "cemetery", "chair_lift", "chalet", "chaparral", "chapel", "checkout_counter", "cheese_factory", "chemical_plant", "aquarium", "chemistry_lab", "chicken_coop_indoor", "chicken_coop_outdoor", "chicken_farm_indoor", "chicken_farm_outdoor", "childs_room", "choir_loft_interior", "chuck_wagon", "church_indoor", "church_outdoor", "aquatic_theater", "circus_tent_indoor", "circus_tent_outdoor", "city", "classroom", "clean_room", "cliff", "clock_tower_indoor", "cloister_indoor", "cloister_outdoor", "closet", "aqueduct", "clothing_store", "coast", "coast_road", "cockpit", "cocktail_lounge", "coffee_shop", "computer_room", "conference_center", "conference_hall", "conference_room", "arbor", "confessional", "construction_site", "control_room", "control_tower_indoor", "control_tower_outdoor", "convenience_store_indoor", "convenience_store_outdoor", "coral_reef", "corn_field", "corner", "arcade", "corral", "corridor", "cottage", "cottage_garden", "country_house", "country_road", "courthouse", "courtroom", "courtyard", "covered_bridge_interior", "arch", "crawl_space", "creek", "crevasse", "crosswalk", "cultivated", "customhouse", "cybercafe", "dacha", "dairy_indoor", "dairy_outdoor", "archaelogical_excavation", "dam", "dance_floor", "dance_school", "darkroom", "day_care_center", "deck-house_boat_deck_house", "deck-house_deck_house", "delicatessen", "dentists_office", "department_store", "archipelago", "departure_lounge", "desert_road", "diner_indoor", "diner_outdoor", "dinette_home", "dining_area", "dining_car", "dining_hall", "dining_room", "dirt_track", "air_base", "archive", "discotheque", "distillery", "ditch", "diving_board", "dock", "dolmen", "donjon", "door", "doorway_indoor", "doorway_outdoor", "armory", "dorm_room", "downtown", "drainage_ditch", "dress_shop", "dressing_room", "drill_rig", "driveway", "driving_range_indoor", "driving_range_outdoor", "drugstore", "army_base", "dry", "dry_dock", "dugout", "earth_fissure", "east_asia", "editing_room", "electrical_substation", "elevated_catwalk", "elevator_interior", "elevator_lobby", "arrival_gate_indoor", "elevator_shaft", "embankment", "embassy", "embrasure", "engine_room", "entrance", "entrance_hall", "entranceway_indoor", "entranceway_outdoor", "entryway_outdoor", "arrival_gate_outdoor", "escalator_indoor", "escalator_outdoor", "escarpment", "establishment", "estaminet", "estuary", "excavation", "exhibition_hall", "exterior", "fabric_store", "art_gallery", "factory_indoor", "factory_outdoor", "fairway", "fan", "farm", "farm_building", "farmhouse", "fastfood_restaurant", "feed_bunk", "fence", "art_school", "ferryboat_indoor", "field_house", "field_road", "field_tent_indoor", "field_tent_outdoor", "fire_escape", "fire_station", "fire_trench", "fireplace", "firing_range_indoor", "art_studio", "firing_range_outdoor", "fish_farm", "fishmarket", "fishpond", "fitting_room_interior", "fjord", "flashflood", "flatlet", "flea_market_indoor", "flea_market_outdoor", "artificial", "floating_dock", "floating_dry_dock", "flood", "flood_plain", "florist_shop_indoor", "florist_shop_outdoor", "flowerbed", "flume_indoor", "fly_bridge", "flying_buttress", "artists_loft", "food_court", "football", "football_field", "foothill", "forecourt", "foreshore", "forest_fire", "forest_path", "forest_road", "forklift", "aircraft_carrier_object", "assembly_hall", "formal_garden", "fort", "fortress", "foundry_indoor", "foundry_outdoor", "fountain", "freestanding", "freeway", "freight_elevator", "front_porch", "assembly_line", "frontseat", "funeral_chapel", "funeral_home", "furnace_room", "galley", "game_room", "gangplank", "garage_indoor", "garage_outdoor", "garbage_dump", "assembly_plant", "garden", "gas_station", "gas_well", "gasworks", "gate", "gatehouse", "gazebo_interior", "general_store_indoor", "general_store_outdoor", "geodesic_dome_indoor", "athletic_field_indoor", "geodesic_dome_outdoor", "ghost_town", "gift_shop", "glacier", "glade", "glen", "golf_course", "gorge", "granary", "grape_arbor", "athletic_field_outdoor", "great_hall", "greengrocery", "greenhouse_indoor", "greenhouse_outdoor", "grotto", "grove", "guardhouse", "guardroom", "guesthouse", "gulch", "atrium_home", "gun_deck_indoor", "gun_deck_outdoor", "gun_store", "gymnasium_indoor", "gymnasium_outdoor", "hacienda", "hallway", "handball_court", "hangar_indoor", "hangar_outdoor", "atrium_public", "harbor", "hardware_store", "hat_shop", "hatchery", "hayfield", "hayloft", "head_shop", "hearth", "heath", "hedge_maze", "attic", "hedgerow", "heliport", "hen_yard", "herb_garden", "highway", "hill", "hillock", "hockey", "hollow", "home_office", "auditorium", "home_theater", "hoodoo", "hospital", "hospital_room", "hot_spring", "hot_tub_indoor", "hot_tub_outdoor", "hotel_breakfast_area", "hotel_outdoor", "hotel_room", "auto_factory", "house", "housing_estate", "housing_project", "howdah", "hunting_lodge_indoor", "hunting_lodge_outdoor", "hut", "hutment", "ice_cream_parlor", "ice_floe", "airfield", "auto_mechanics_indoor", "ice_shelf", "ice_skating_rink_indoor", "ice_skating_rink_outdoor", "iceberg", "igloo", "imaret", "incinerator_indoor", "incinerator_outdoor", "indoor_procenium", "indoor_round", "auto_mechanics_outdoor", "indoor_seats", "industrial_area", "industrial_park", "inlet", "inn_indoor", "inn_outdoor", "insane_asylum", "irrigation_ditch", "islet", "jacuzzi_indoor", "auto_racing_paddock", "jacuzzi_outdoor", "jail_cell", "jail_indoor", "jail_outdoor", "japanese_garden", "jetty", "jewelry_shop", "joss_house", "juke_joint", "jungle", "auto_showroom", "junk_pile", "junkyard", "jury_box", "kasbah", "kennel_indoor", "kennel_outdoor", "kindergarden_classroom", "kiosk_indoor", "kiosk_outdoor", "kitchen", "awning_deck", "kitchenette", "kraal", "lab_classroom", "laboratorywet", "labyrinth_indoor", "labyrinth_outdoor", "lagoon", "landfill", "landing", "landing_deck", "back_porch", "landing_strip", "laundromat", "lava_flow", "lavatory", "lawn", "layby", "lean-to", "lean-to_tent", "lecture_room", "legislative_chamber", "backdrop", "levee", "library", "library_indoor", "library_outdoor", "lido_deck_indoor", "lido_deck_outdoor", "lift_bridge", "lighthouse", "limousine_interior", "liquor_store_indoor", "backroom", "liquor_store_outdoor", "living_room", "loading_dock", "lobby", "lock_chamber", "locker_room", "loft", "loge", "loggia_outdoor", "lookout_station_indoor", "backseat", "lookout_station_outdoor", "lower_deck", "luggage_van", "lumberyard_indoor", "lumberyard_outdoor", "lyceum", "machine_shop", "manhole", "mansard", "mansion", "backstage", "manufactured_home", "market_indoor", "market_outdoor", "marsh", "martial_arts_gym", "massage_room", "mastaba", "maternity_ward", "mausoleum", "meadow", "airlock", "backstage_outdoor", "meat_house", "medina", "megalith", "menhir", "mens_store_outdoor", "mental_institution_indoor", "mental_institution_outdoor", "mesa", "mesoamerican", "mess_hall", "backstairs", "mews", "mezzanine", "military_headquarters", "military_hospital", "military_hut", "military_tent", "millpond", "millrace", "mine", "mineral_bath", "backstairs_indoor", "mineshaft", "mini_golf_course_indoor", "mini_golf_course_outdoor", "misc", "mission", "mobile_home", "monastery_indoor", "monastery_outdoor", "moon_bounce", "moor", "backwoods", "morgue", "mosque_indoor", "mosque_outdoor", "motel", "mountain", "mountain_path", "mountain_road", "mountain_snowy", "movie_theater_indoor", "movie_theater_outdoor", "badlands", "mudflat", "museum_indoor", "museum_outdoor", "music_store", "music_studio", "natural", "natural_history_museum", "natural_spring", "naval_base", "needleleaf", "badminton_court_indoor", "newsroom", "newsstand_indoor", "newsstand_outdoor", "nightclub", "nook", "nuclear_power_plant_indoor", "nuclear_power_plant_outdoor", "nunnery", "nursery", "nursing_home", "badminton_court_outdoor", "nursing_home_outdoor", "oasis", "oast_house", "observation_station", "observatory_indoor", "observatory_outdoor", "observatory_post", "ocean", "ocean_deep", "ocean_shallow", "baggage_claim", "office", "office_building", "office_cubicles", "oil_refinery_indoor", "oil_refinery_outdoor", "oilrig", "one-way_street", "open-hearth_furnace", "operating_room", "operating_table", "balcony_interior", "optician", "orchard", "orchestra_pit", "organ_loft_interior", "orlop_deck", "ossuary", "outbuilding", "outcropping", "outhouse_indoor", "outhouse_outdoor", "ball_pit", "outside", "overpass", "oyster_bar", "oyster_farm", "packaging_plant", "pagoda", "palace", "palace_hall", "palestra", "pantry", "airplane", "ballet", "paper_mill", "parade_ground", "park", "parking_garage_indoor", "parking_garage_outdoor", "parking_lot", "parkway", "parlor", "particle_accelerator", "party_tent_indoor", "ballroom", "party_tent_outdoor", "passenger_deck", "pasture", "patio", "patio_indoor", "pavement", "pavilion", "pawnshop", "pawnshop_outdoor", "pedestrian_overpass_indoor", "balustrade", "penalty_box", "performance", "perfume_shop", "pet_shop", "pharmacy", "phone_booth", "physics_laboratory", "piano_store", "picnic_area", "pier", "bamboo_forest", "pig_farm", "pilothouse_indoor", "pilothouse_outdoor", "pinetum", "piste_road", "pitchers_mound", "pizzeria", "pizzeria_outdoor", "planetarium_indoor", "planetarium_outdoor", "bank_indoor", "plantation_house", "platform", "playground", "playroom", "plaza", "plunge", "podium_indoor", "podium_outdoor", "police_station", "pond", "bank_outdoor", "pontoon_bridge", "poolroom_home", "poop_deck", "porch", "portico", "portrait_studio", "postern", "powder_room", "power_plant_outdoor", "preserve", "bank_vault", "print_shop", "priory", "promenade", "promenade_deck", "pub_indoor", "pub_outdoor", "pueblo", "pulpit", "pump_room", "pumping_station", "banquet_hall", "putting_green", "quadrangle", "questionable", "quicksand", "quonset_hut_indoor", "quonset_hut_outdoor", "racecourse", "raceway", "raft", "rail_indoor", "baptistry_indoor", "rail_outdoor", "railroad_track", "railway_yard", "rainforest", "ramp", "ranch", "ranch_house", "reading_room", "reception", "reception_room", "baptistry_outdoor", "recreation_room", "rectory", "recycling_plant_indoor", "recycling_plant_outdoor", "refectory", "repair_shop", "residential_neighborhood", "resort", "rest_area", "rest_stop", "airplane_cabin", "bar", "restaurant", "restaurant_kitchen", "restaurant_patio", "restroom_indoor", "restroom_outdoor", "retaining_wall", "revolving_door", "rice_paddy", "riding_arena", "rift_valley", "barbeque", "river", "road", "road_cut", "road_indoor", "road_outdoor", "rock_arch", "rock_garden", "rodeo", "roller_skating_rink_indoor", "roller_skating_rink_outdoor", "barbershop", "rolling_mill", "roof", "roof_garden", "room", "root_cellar", "rope_bridge", "rotisserie", "roundabout", "roundhouse", "rubble", "barn", "ruin", "runway", "sacristy", "safari_park", "salon", "saloon", "salt_plain", "sanatorium", "sand", "sand_trap", "barndoor", "sandbar", "sandbox", "sauna", "savanna", "sawmill", "schoolhouse", "schoolyard", "science_laboratory", "science_museum", "scriptorium", "barnyard", "scrubland", "scullery", "sea_cliff", "seaside", "seawall", "security_check_point", "semidesert", "server_room", "sewer", "sewing_room", "barrack", "shed", "shelter", "shelter_deck", "shelter_tent", "shipping_room", "shipyard_outdoor", "shoe_shop", "shop", "shopfront", "shopping_mall_indoor", "barrel_storage", "shopping_mall_outdoor", "shore", "shower", "shower_room", "shrine", "shrubbery", "sidewalk", "signal_box", "sinkhole", "ski_jump", "baseball", "ski_lodge", "ski_resort", "ski_slope", "sky", "skyscraper", "skywalk_indoor", "skywalk_outdoor", "slum", "snack_bar", "snowbank", "baseball_field", "snowfield", "soccer", "south_asia", "spillway", "sporting_goods_store", "squash_court", "stable", "stadium_outdoor", "stage_indoor", "stage_outdoor", "airport", "basement", "stage_set", "staircase", "stall", "starting_gate", "stateroom", "station", "steam_plant_outdoor", "steel_mill_indoor", "steel_mill_outdoor", "stone_circle", "basilica", "storage_room", "store", "storm_cellar", "street", "streetcar_track", "strip_mall", "strip_mine", "student_center", "student_residence", "study_hall", "basin_outdoor", "submarine_interior", "subway_interior", "sugar_refinery", "sun_deck", "sunroom", "supermarket", "supply_chamber", "sushi_bar", "swamp", "swimming_hole", "basketball", "swimming_pool_indoor", "swimming_pool_outdoor", "synagogue_indoor", "synagogue_outdoor", "t-bar_lift", "tannery", "taxistand", "taxiway", "tea_garden", "teahouse", "basketball_court_indoor", "tearoom", "teashop", "television_room", "television_studio", "tennis_court_indoor", "tennis_court_outdoor", "tent_outdoor", "terrace_farm", "theater_outdoor", "threshing_floor", "basketball_court_outdoor", "thriftshop", "throne_room", "ticket_booth", "ticket_window_indoor", "tidal_basin", "tidal_river", "tiltyard", "tobacco_shop_indoor", "toll_plaza", "tollbooth", "bath_indoor", "tollgate", "tomb", "topiary_garden", "tower", "town_house", "toyshop", "track_outdoor", "tract_housing", "trading_floor", "traffic_island", "bath_outdoor", "trailer_park", "train_interior", "train_railway", "train_station_outdoor", "tree_farm", "tree_house", "trellis", "trench", "trestle_bridge", "truck_stop", "bathhouse", "tundra", "turkish_bath", "upper_balcony", "urban", "utility_room", "valley", "van_interior", "vat", "vegetable_garden", "vegetation", "bathhouse_outdoor", "vehicle", "velodrome_indoor", "velodrome_outdoor", "ventilation_shaft", "veranda", "vestibule", "vestry", "veterinarians_office", "viaduct", "videostore" ]

adam-bourne/food-classifier

# food-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2136 - Validation Loss: 0.2284 - Train Accuracy: 0.94 - Epoch: 4 ## Model description This is an image classification model fine tuned from the Google Vision Transformer (ViT) to classify images of food. ## Intended uses & limitations For messing around! ## Training and evaluation data The training set contained 101 food classes, over a dataset of 101,000 images. The train/eval split was 80/20 ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.3409 | 0.2903 | 0.932 | 0 | | 0.2838 | 0.2897 | 0.917 | 1 | | 0.2415 | 0.2869 | 0.914 | 2 | | 0.2143 | 0.2630 | 0.924 | 3 | | 0.2136 | 0.2284 | 0.94 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

bgirardot/my_awesome_food_model_v2

# my_awesome_food_model_v2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.9051 - Accuracy: 0.965 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.6221 | 1.0 | 25 | 2.8425 | 0.875 | | 2.195 | 2.0 | 50 | 2.0761 | 0.955 | | 1.9134 | 3.0 | 75 | 1.9051 | 0.965 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

rshrott/vit-base-renovation

# vit-base-renovation This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the renovations dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.382 | 0.2 | 25 | 1.1103 | 0.6073 | | 0.5741 | 0.4 | 50 | 1.0628 | 0.6210 | | 0.5589 | 0.6 | 75 | 1.0025 | 0.6667 | | 0.4074 | 0.81 | 100 | 1.1324 | 0.6073 | | 0.3581 | 1.01 | 125 | 1.1935 | 0.6438 | | 0.2618 | 1.21 | 150 | 1.8300 | 0.5023 | | 0.1299 | 1.41 | 175 | 1.2577 | 0.6301 | | 0.2562 | 1.61 | 200 | 1.0924 | 0.6895 | | 0.2573 | 1.81 | 225 | 1.1285 | 0.6849 | | 0.2471 | 2.02 | 250 | 1.3387 | 0.6256 | | 0.0618 | 2.22 | 275 | 1.2246 | 0.6667 | | 0.0658 | 2.42 | 300 | 1.4132 | 0.6347 | | 0.0592 | 2.62 | 325 | 1.4326 | 0.6530 | | 0.0464 | 2.82 | 350 | 1.2484 | 0.6849 | | 0.0567 | 3.02 | 375 | 1.5350 | 0.6347 | | 0.0269 | 3.23 | 400 | 1.4797 | 0.6667 | | 0.0239 | 3.43 | 425 | 1.4444 | 0.6530 | | 0.0184 | 3.63 | 450 | 1.4474 | 0.6575 | | 0.0286 | 3.83 | 475 | 1.4621 | 0.6667 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "not applicable", "very poor", "poor", "fair", "good", "excellent", "exceptional" ]

jordyvl/vit-small_tobacco3482_og_simkd_

# vit-small_tobacco3482_og_simkd_ This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 212.9178 - Accuracy: 0.855 - Brier Loss: 0.2563 - Nll: 1.4722 - F1 Micro: 0.855 - F1 Macro: 0.8333 - Ece: 0.1253 - Aurc: 0.0422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 219.2297 | 0.145 | 0.8895 | 6.5562 | 0.145 | 0.0567 | 0.2109 | 0.7661 | | No log | 2.0 | 50 | 217.9786 | 0.49 | 0.6839 | 2.2035 | 0.49 | 0.4113 | 0.3237 | 0.3097 | | No log | 3.0 | 75 | 216.6085 | 0.565 | 0.5671 | 1.7658 | 0.565 | 0.4471 | 0.2471 | 0.2239 | | No log | 4.0 | 100 | 216.0210 | 0.68 | 0.4722 | 1.8682 | 0.68 | 0.5586 | 0.2322 | 0.1557 | | No log | 5.0 | 125 | 215.5695 | 0.68 | 0.4668 | 1.9385 | 0.68 | 0.5570 | 0.2289 | 0.1440 | | No log | 6.0 | 150 | 215.3762 | 0.745 | 0.3963 | 2.1043 | 0.745 | 0.6608 | 0.1949 | 0.0976 | | No log | 7.0 | 175 | 214.8964 | 0.745 | 0.3675 | 1.7226 | 0.745 | 0.6693 | 0.1765 | 0.0949 | | No log | 8.0 | 200 | 215.0440 | 0.735 | 0.3838 | 1.9180 | 0.735 | 0.6935 | 0.2056 | 0.0958 | | No log | 9.0 | 225 | 214.7017 | 0.775 | 0.3466 | 1.5816 | 0.775 | 0.6897 | 0.1756 | 0.0661 | | No log | 10.0 | 250 | 214.6309 | 0.775 | 0.3505 | 1.6245 | 0.775 | 0.7604 | 0.1828 | 0.0763 | | No log | 11.0 | 275 | 214.6275 | 0.735 | 0.4314 | 2.3367 | 0.735 | 0.7342 | 0.2234 | 0.1203 | | No log | 12.0 | 300 | 214.5664 | 0.75 | 0.3769 | 1.7889 | 0.75 | 0.7420 | 0.1873 | 0.1070 | | No log | 13.0 | 325 | 214.6764 | 0.735 | 0.4425 | 2.3533 | 0.735 | 0.7404 | 0.2267 | 0.1508 | | No log | 14.0 | 350 | 214.5261 | 0.805 | 0.3093 | 1.8504 | 0.805 | 0.7870 | 0.1732 | 0.0580 | | No log | 15.0 | 375 | 214.4932 | 0.79 | 0.3255 | 1.4649 | 0.79 | 0.7575 | 0.1796 | 0.0543 | | No log | 16.0 | 400 | 214.3134 | 0.85 | 0.2467 | 1.4769 | 0.85 | 0.8388 | 0.1149 | 0.0513 | | No log | 17.0 | 425 | 214.3825 | 0.82 | 0.2845 | 1.4858 | 0.82 | 0.8014 | 0.1445 | 0.0540 | | No log | 18.0 | 450 | 214.2077 | 0.85 | 0.2681 | 1.4891 | 0.85 | 0.8406 | 0.1462 | 0.0684 | | No log | 19.0 | 475 | 214.1675 | 0.845 | 0.2623 | 1.5311 | 0.845 | 0.8329 | 0.1414 | 0.0485 | | 220.0633 | 20.0 | 500 | 214.1433 | 0.84 | 0.2663 | 1.5269 | 0.8400 | 0.8182 | 0.1302 | 0.0562 | | 220.0633 | 21.0 | 525 | 214.0829 | 0.805 | 0.3293 | 2.0021 | 0.805 | 0.8019 | 0.1710 | 0.0833 | | 220.0633 | 22.0 | 550 | 213.9282 | 0.84 | 0.2586 | 1.5127 | 0.8400 | 0.8205 | 0.1397 | 0.0453 | | 220.0633 | 23.0 | 575 | 213.9303 | 0.87 | 0.2260 | 1.4450 | 0.87 | 0.8552 | 0.1205 | 0.0365 | | 220.0633 | 24.0 | 600 | 213.9140 | 0.84 | 0.2620 | 1.5244 | 0.8400 | 0.8161 | 0.1462 | 0.0490 | | 220.0633 | 25.0 | 625 | 213.7616 | 0.86 | 0.2306 | 1.5288 | 0.8600 | 0.8409 | 0.1215 | 0.0361 | | 220.0633 | 26.0 | 650 | 213.7738 | 0.845 | 0.2431 | 1.5303 | 0.845 | 0.8271 | 0.1335 | 0.0443 | | 220.0633 | 27.0 | 675 | 213.8470 | 0.85 | 0.2427 | 1.3459 | 0.85 | 0.8275 | 0.1296 | 0.0445 | | 220.0633 | 28.0 | 700 | 213.7198 | 0.85 | 0.2381 | 1.3868 | 0.85 | 0.8328 | 0.1267 | 0.0424 | | 220.0633 | 29.0 | 725 | 213.6302 | 0.855 | 0.2293 | 1.4191 | 0.855 | 0.8361 | 0.1157 | 0.0394 | | 220.0633 | 30.0 | 750 | 213.6385 | 0.85 | 0.2424 | 1.5410 | 0.85 | 0.8334 | 0.1339 | 0.0464 | | 220.0633 | 31.0 | 775 | 213.6397 | 0.865 | 0.2234 | 1.4012 | 0.865 | 0.8464 | 0.1226 | 0.0402 | | 220.0633 | 32.0 | 800 | 213.6658 | 0.86 | 0.2271 | 1.3863 | 0.8600 | 0.8470 | 0.1164 | 0.0354 | | 220.0633 | 33.0 | 825 | 213.6526 | 0.85 | 0.2448 | 1.5357 | 0.85 | 0.8292 | 0.1214 | 0.0397 | | 220.0633 | 34.0 | 850 | 213.5407 | 0.855 | 0.2282 | 1.3470 | 0.855 | 0.8405 | 0.1245 | 0.0393 | | 220.0633 | 35.0 | 875 | 213.6166 | 0.83 | 0.2624 | 1.4288 | 0.83 | 0.8102 | 0.1415 | 0.0458 | | 220.0633 | 36.0 | 900 | 213.5887 | 0.84 | 0.2613 | 1.3928 | 0.8400 | 0.8135 | 0.1298 | 0.0442 | | 220.0633 | 37.0 | 925 | 213.4976 | 0.845 | 0.2338 | 1.3784 | 0.845 | 0.8244 | 0.1319 | 0.0355 | | 220.0633 | 38.0 | 950 | 213.4554 | 0.85 | 0.2374 | 1.3680 | 0.85 | 0.8323 | 0.1192 | 0.0385 | | 220.0633 | 39.0 | 975 | 213.4758 | 0.845 | 0.2319 | 1.4895 | 0.845 | 0.8274 | 0.1185 | 0.0385 | | 217.7609 | 40.0 | 1000 | 213.4440 | 0.845 | 0.2432 | 1.3737 | 0.845 | 0.8265 | 0.1310 | 0.0415 | | 217.7609 | 41.0 | 1025 | 213.4492 | 0.845 | 0.2385 | 1.4970 | 0.845 | 0.8297 | 0.1207 | 0.0373 | | 217.7609 | 42.0 | 1050 | 213.4319 | 0.85 | 0.2384 | 1.3580 | 0.85 | 0.8276 | 0.1250 | 0.0383 | | 217.7609 | 43.0 | 1075 | 213.3094 | 0.855 | 0.2287 | 1.4375 | 0.855 | 0.8334 | 0.1188 | 0.0353 | | 217.7609 | 44.0 | 1100 | 213.3809 | 0.845 | 0.2514 | 1.4969 | 0.845 | 0.8250 | 0.1318 | 0.0435 | | 217.7609 | 45.0 | 1125 | 213.3981 | 0.85 | 0.2478 | 1.6052 | 0.85 | 0.8287 | 0.1268 | 0.0408 | | 217.7609 | 46.0 | 1150 | 213.3004 | 0.86 | 0.2292 | 1.3632 | 0.8600 | 0.8430 | 0.1180 | 0.0355 | | 217.7609 | 47.0 | 1175 | 213.3041 | 0.86 | 0.2363 | 1.3407 | 0.8600 | 0.8444 | 0.1235 | 0.0359 | | 217.7609 | 48.0 | 1200 | 213.2955 | 0.845 | 0.2462 | 1.5071 | 0.845 | 0.8179 | 0.1253 | 0.0396 | | 217.7609 | 49.0 | 1225 | 213.2531 | 0.85 | 0.2433 | 1.2946 | 0.85 | 0.8277 | 0.1270 | 0.0392 | | 217.7609 | 50.0 | 1250 | 213.2612 | 0.845 | 0.2378 | 1.2852 | 0.845 | 0.8193 | 0.1281 | 0.0361 | | 217.7609 | 51.0 | 1275 | 213.2246 | 0.855 | 0.2370 | 1.5829 | 0.855 | 0.8393 | 0.1234 | 0.0357 | | 217.7609 | 52.0 | 1300 | 213.1795 | 0.845 | 0.2431 | 1.4923 | 0.845 | 0.8300 | 0.1280 | 0.0372 | | 217.7609 | 53.0 | 1325 | 213.2721 | 0.855 | 0.2467 | 1.5096 | 0.855 | 0.8333 | 0.1248 | 0.0385 | | 217.7609 | 54.0 | 1350 | 213.1976 | 0.85 | 0.2453 | 1.4167 | 0.85 | 0.8275 | 0.1240 | 0.0384 | | 217.7609 | 55.0 | 1375 | 213.2822 | 0.845 | 0.2430 | 1.4438 | 0.845 | 0.8193 | 0.1283 | 0.0396 | | 217.7609 | 56.0 | 1400 | 213.1443 | 0.85 | 0.2479 | 1.5246 | 0.85 | 0.8277 | 0.1304 | 0.0389 | | 217.7609 | 57.0 | 1425 | 213.1679 | 0.85 | 0.2455 | 1.4468 | 0.85 | 0.8291 | 0.1224 | 0.0387 | | 217.7609 | 58.0 | 1450 | 213.1116 | 0.85 | 0.2467 | 1.4372 | 0.85 | 0.8287 | 0.1269 | 0.0378 | | 217.7609 | 59.0 | 1475 | 213.1005 | 0.85 | 0.2490 | 1.4214 | 0.85 | 0.8271 | 0.1316 | 0.0392 | | 217.1217 | 60.0 | 1500 | 213.1516 | 0.855 | 0.2425 | 1.4600 | 0.855 | 0.8343 | 0.1316 | 0.0369 | | 217.1217 | 61.0 | 1525 | 213.1205 | 0.855 | 0.2458 | 1.4436 | 0.855 | 0.8303 | 0.1197 | 0.0409 | | 217.1217 | 62.0 | 1550 | 213.1318 | 0.85 | 0.2488 | 1.4405 | 0.85 | 0.8275 | 0.1304 | 0.0378 | | 217.1217 | 63.0 | 1575 | 213.0243 | 0.855 | 0.2521 | 1.5810 | 0.855 | 0.8328 | 0.1341 | 0.0447 | | 217.1217 | 64.0 | 1600 | 213.1191 | 0.84 | 0.2567 | 1.4478 | 0.8400 | 0.8185 | 0.1292 | 0.0436 | | 217.1217 | 65.0 | 1625 | 213.0329 | 0.855 | 0.2528 | 1.3910 | 0.855 | 0.8333 | 0.1311 | 0.0404 | | 217.1217 | 66.0 | 1650 | 212.9868 | 0.85 | 0.2525 | 1.4652 | 0.85 | 0.8275 | 0.1226 | 0.0408 | | 217.1217 | 67.0 | 1675 | 213.0856 | 0.84 | 0.2561 | 1.4601 | 0.8400 | 0.8178 | 0.1367 | 0.0419 | | 217.1217 | 68.0 | 1700 | 213.0379 | 0.845 | 0.2544 | 1.5222 | 0.845 | 0.8216 | 0.1362 | 0.0426 | | 217.1217 | 69.0 | 1725 | 213.0535 | 0.835 | 0.2606 | 1.5085 | 0.835 | 0.8093 | 0.1346 | 0.0445 | | 217.1217 | 70.0 | 1750 | 213.0247 | 0.85 | 0.2530 | 1.4349 | 0.85 | 0.8274 | 0.1373 | 0.0427 | | 217.1217 | 71.0 | 1775 | 213.0161 | 0.855 | 0.2510 | 1.4529 | 0.855 | 0.8333 | 0.1212 | 0.0411 | | 217.1217 | 72.0 | 1800 | 213.0249 | 0.845 | 0.2494 | 1.4511 | 0.845 | 0.8229 | 0.1358 | 0.0412 | | 217.1217 | 73.0 | 1825 | 213.0014 | 0.85 | 0.2548 | 1.4435 | 0.85 | 0.8264 | 0.1277 | 0.0390 | | 217.1217 | 74.0 | 1850 | 213.0011 | 0.845 | 0.2527 | 1.3719 | 0.845 | 0.8206 | 0.1360 | 0.0379 | | 217.1217 | 75.0 | 1875 | 213.0240 | 0.845 | 0.2576 | 1.4072 | 0.845 | 0.8221 | 0.1284 | 0.0425 | | 217.1217 | 76.0 | 1900 | 212.9793 | 0.845 | 0.2534 | 1.4026 | 0.845 | 0.8212 | 0.1241 | 0.0404 | | 217.1217 | 77.0 | 1925 | 212.9800 | 0.85 | 0.2514 | 1.5023 | 0.85 | 0.8271 | 0.1279 | 0.0407 | | 217.1217 | 78.0 | 1950 | 212.9125 | 0.845 | 0.2564 | 1.4258 | 0.845 | 0.8211 | 0.1298 | 0.0427 | | 217.1217 | 79.0 | 1975 | 212.9454 | 0.85 | 0.2527 | 1.5227 | 0.85 | 0.8271 | 0.1279 | 0.0423 | | 216.765 | 80.0 | 2000 | 212.9475 | 0.845 | 0.2551 | 1.5025 | 0.845 | 0.8206 | 0.1311 | 0.0423 | | 216.765 | 81.0 | 2025 | 212.9739 | 0.84 | 0.2567 | 1.5305 | 0.8400 | 0.8162 | 0.1294 | 0.0431 | | 216.765 | 82.0 | 2050 | 212.9351 | 0.855 | 0.2526 | 1.5373 | 0.855 | 0.8339 | 0.1277 | 0.0401 | | 216.765 | 83.0 | 2075 | 213.0053 | 0.845 | 0.2560 | 1.4724 | 0.845 | 0.8228 | 0.1341 | 0.0417 | | 216.765 | 84.0 | 2100 | 212.9326 | 0.845 | 0.2568 | 1.5217 | 0.845 | 0.8206 | 0.1303 | 0.0472 | | 216.765 | 85.0 | 2125 | 212.9555 | 0.855 | 0.2537 | 1.5265 | 0.855 | 0.8339 | 0.1233 | 0.0416 | | 216.765 | 86.0 | 2150 | 212.9121 | 0.85 | 0.2534 | 1.5224 | 0.85 | 0.8280 | 0.1283 | 0.0398 | | 216.765 | 87.0 | 2175 | 212.8850 | 0.845 | 0.2551 | 1.4480 | 0.845 | 0.8221 | 0.1328 | 0.0412 | | 216.765 | 88.0 | 2200 | 212.9121 | 0.855 | 0.2518 | 1.5069 | 0.855 | 0.8339 | 0.1234 | 0.0404 | | 216.765 | 89.0 | 2225 | 212.9327 | 0.845 | 0.2517 | 1.4532 | 0.845 | 0.8206 | 0.1231 | 0.0401 | | 216.765 | 90.0 | 2250 | 212.9305 | 0.85 | 0.2542 | 1.4506 | 0.85 | 0.8271 | 0.1374 | 0.0398 | | 216.765 | 91.0 | 2275 | 212.9274 | 0.85 | 0.2567 | 1.5045 | 0.85 | 0.8280 | 0.1297 | 0.0419 | | 216.765 | 92.0 | 2300 | 212.8962 | 0.85 | 0.2545 | 1.4956 | 0.85 | 0.8280 | 0.1261 | 0.0405 | | 216.765 | 93.0 | 2325 | 212.9133 | 0.845 | 0.2567 | 1.5274 | 0.845 | 0.8212 | 0.1291 | 0.0431 | | 216.765 | 94.0 | 2350 | 212.8708 | 0.85 | 0.2576 | 1.4410 | 0.85 | 0.8280 | 0.1302 | 0.0410 | | 216.765 | 95.0 | 2375 | 212.9661 | 0.855 | 0.2546 | 1.3988 | 0.855 | 0.8339 | 0.1248 | 0.0404 | | 216.765 | 96.0 | 2400 | 212.9099 | 0.855 | 0.2547 | 1.5096 | 0.855 | 0.8333 | 0.1256 | 0.0402 | | 216.765 | 97.0 | 2425 | 212.9668 | 0.85 | 0.2549 | 1.5337 | 0.85 | 0.8271 | 0.1289 | 0.0390 | | 216.765 | 98.0 | 2450 | 212.9587 | 0.845 | 0.2545 | 1.5161 | 0.845 | 0.8215 | 0.1304 | 0.0412 | | 216.765 | 99.0 | 2475 | 212.9395 | 0.855 | 0.2554 | 1.4606 | 0.855 | 0.8333 | 0.1253 | 0.0410 | | 216.6085 | 100.0 | 2500 | 212.9178 | 0.855 | 0.2563 | 1.4722 | 0.855 | 0.8333 | 0.1253 | 0.0422 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

hbenitez/AV_classifier_resnet50

# hbenitez/AV_classifier_resnet50 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8261 - Validation Loss: 2.4425 - Train Accuracy: 0.6 - Epoch: 99 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 8.2326 | 8.1060 | 0.0 | 0 | | 8.3420 | 7.6394 | 0.05 | 1 | | 7.9643 | 7.5706 | 0.05 | 2 | | 7.9337 | 7.6265 | 0.05 | 3 | | 7.8018 | 7.7736 | 0.05 | 4 | | 7.8009 | 7.7905 | 0.05 | 5 | | 7.6369 | 7.6354 | 0.05 | 6 | | 7.4782 | 7.5608 | 0.05 | 7 | | 7.3655 | 7.6271 | 0.05 | 8 | | 7.2886 | 7.6028 | 0.0 | 9 | | 7.1145 | 7.5211 | 0.0 | 10 | | 7.1232 | 7.2993 | 0.0 | 11 | | 6.8393 | 7.2079 | 0.0 | 12 | | 6.8202 | 7.2143 | 0.0 | 13 | | 6.7180 | 7.1236 | 0.05 | 14 | | 6.7318 | 7.1061 | 0.0 | 15 | | 6.4563 | 6.9758 | 0.05 | 16 | | 6.3765 | 6.9413 | 0.05 | 17 | | 6.1791 | 6.8315 | 0.05 | 18 | | 6.1946 | 6.7703 | 0.05 | 19 | | 5.8448 | 6.7431 | 0.1 | 20 | | 5.8514 | 6.6876 | 0.1 | 21 | | 5.8200 | 6.6353 | 0.05 | 22 | | 5.8323 | 6.5814 | 0.05 | 23 | | 5.5553 | 6.4306 | 0.05 | 24 | | 5.4999 | 6.4455 | 0.05 | 25 | | 5.4370 | 6.3026 | 0.05 | 26 | | 5.2288 | 6.0093 | 0.1 | 27 | | 5.2173 | 6.0593 | 0.05 | 28 | | 5.2280 | 6.0598 | 0.05 | 29 | | 5.0484 | 5.9769 | 0.05 | 30 | | 4.8703 | 5.8336 | 0.05 | 31 | | 4.9881 | 5.7711 | 0.1 | 32 | | 4.5905 | 5.6685 | 0.1 | 33 | | 4.7240 | 5.6156 | 0.15 | 34 | | 4.5095 | 5.4680 | 0.15 | 35 | | 4.2225 | 5.3962 | 0.15 | 36 | | 4.3615 | 5.3290 | 0.2 | 37 | | 4.1862 | 5.3602 | 0.15 | 38 | | 3.9455 | 5.2635 | 0.15 | 39 | | 3.9737 | 5.2337 | 0.15 | 40 | | 4.0922 | 5.1268 | 0.15 | 41 | | 3.6042 | 4.9972 | 0.2 | 42 | | 3.7219 | 4.8787 | 0.15 | 43 | | 3.5563 | 4.9075 | 0.2 | 44 | | 3.5897 | 4.9157 | 0.25 | 45 | | 3.5769 | 4.7936 | 0.25 | 46 | | 3.6225 | 4.8689 | 0.2 | 47 | | 3.4568 | 4.8767 | 0.2 | 48 | | 3.1431 | 4.7520 | 0.25 | 49 | | 3.0607 | 4.5815 | 0.3 | 50 | | 2.8904 | 4.5007 | 0.2 | 51 | | 2.8308 | 4.5054 | 0.25 | 52 | | 2.8136 | 4.2745 | 0.25 | 53 | | 2.6192 | 4.3300 | 0.2 | 54 | | 2.5308 | 4.3180 | 0.2 | 55 | | 2.5192 | 4.2706 | 0.2 | 56 | | 2.5761 | 4.1395 | 0.25 | 57 | | 2.3516 | 3.9031 | 0.3 | 58 | | 2.3231 | 3.8172 | 0.35 | 59 | | 2.2735 | 3.7651 | 0.35 | 60 | | 2.1215 | 3.8034 | 0.35 | 61 | | 2.3229 | 3.8096 | 0.35 | 62 | | 2.2230 | 3.7000 | 0.35 | 63 | | 1.9059 | 3.6666 | 0.25 | 64 | | 2.0289 | 3.6743 | 0.25 | 65 | | 1.9178 | 3.5819 | 0.3 | 66 | | 2.0295 | 3.5087 | 0.35 | 67 | | 1.6499 | 3.4962 | 0.4 | 68 | | 1.6261 | 3.4146 | 0.3 | 69 | | 1.7059 | 3.4097 | 0.35 | 70 | | 1.4837 | 3.2702 | 0.35 | 71 | | 1.3766 | 3.2214 | 0.4 | 72 | | 1.5898 | 3.2674 | 0.4 | 73 | | 1.5002 | 3.1907 | 0.4 | 74 | | 1.2641 | 3.1176 | 0.4 | 75 | | 1.3456 | 3.1562 | 0.4 | 76 | | 1.2655 | 2.9548 | 0.5 | 77 | | 1.5449 | 2.8738 | 0.5 | 78 | | 1.2519 | 2.8336 | 0.45 | 79 | | 1.0682 | 2.8478 | 0.35 | 80 | | 1.1891 | 2.8408 | 0.5 | 81 | | 1.2920 | 2.6254 | 0.5 | 82 | | 1.1239 | 2.7507 | 0.5 | 83 | | 1.0857 | 2.7772 | 0.4 | 84 | | 0.9821 | 2.8372 | 0.45 | 85 | | 1.0457 | 2.8636 | 0.45 | 86 | | 1.1419 | 2.8426 | 0.45 | 87 | | 1.0782 | 2.7856 | 0.5 | 88 | | 0.9906 | 2.6826 | 0.55 | 89 | | 1.0766 | 2.6707 | 0.5 | 90 | | 1.1115 | 2.6457 | 0.5 | 91 | | 1.2201 | 2.6838 | 0.55 | 92 | | 0.8706 | 2.5262 | 0.55 | 93 | | 0.7441 | 2.5422 | 0.55 | 94 | | 0.9710 | 2.4211 | 0.6 | 95 | | 0.9731 | 2.4090 | 0.6 | 96 | | 0.8942 | 2.3773 | 0.6 | 97 | | 1.0461 | 2.4159 | 0.55 | 98 | | 0.8261 | 2.4425 | 0.6 | 99 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.13.0-rc2 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

jordyvl/vit-tiny_tobacco3482_og_simkd_

# vit-tiny_tobacco3482_og_simkd_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 212.9255 - Accuracy: 0.825 - Brier Loss: 0.2955 - Nll: 1.3247 - F1 Micro: 0.825 - F1 Macro: 0.8027 - Ece: 0.1660 - Aurc: 0.0554 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 219.5859 | 0.225 | 0.8919 | 5.3905 | 0.225 | 0.1403 | 0.2467 | 0.7026 | | No log | 2.0 | 50 | 218.7605 | 0.385 | 0.8149 | 2.3886 | 0.3850 | 0.3040 | 0.3416 | 0.4009 | | No log | 3.0 | 75 | 217.4839 | 0.49 | 0.6311 | 1.8415 | 0.49 | 0.3775 | 0.2502 | 0.2789 | | No log | 4.0 | 100 | 216.7303 | 0.605 | 0.5512 | 1.9525 | 0.605 | 0.4738 | 0.2423 | 0.1956 | | No log | 5.0 | 125 | 215.7727 | 0.625 | 0.4792 | 1.8491 | 0.625 | 0.5148 | 0.2037 | 0.1503 | | No log | 6.0 | 150 | 215.9141 | 0.595 | 0.5723 | 2.5766 | 0.595 | 0.4836 | 0.2297 | 0.2039 | | No log | 7.0 | 175 | 215.2466 | 0.715 | 0.4245 | 1.8434 | 0.715 | 0.5962 | 0.2236 | 0.1201 | | No log | 8.0 | 200 | 215.0580 | 0.72 | 0.3878 | 1.8760 | 0.72 | 0.6108 | 0.2068 | 0.0879 | | No log | 9.0 | 225 | 214.9401 | 0.745 | 0.3919 | 1.7852 | 0.745 | 0.7183 | 0.1855 | 0.0936 | | No log | 10.0 | 250 | 214.6627 | 0.66 | 0.4840 | 2.1588 | 0.66 | 0.6144 | 0.2371 | 0.1261 | | No log | 11.0 | 275 | 214.9265 | 0.69 | 0.5027 | 1.7910 | 0.69 | 0.6599 | 0.2514 | 0.1279 | | No log | 12.0 | 300 | 214.4700 | 0.78 | 0.3372 | 1.7013 | 0.78 | 0.7459 | 0.1740 | 0.0741 | | No log | 13.0 | 325 | 214.5191 | 0.805 | 0.3164 | 1.5824 | 0.805 | 0.7689 | 0.1662 | 0.0689 | | No log | 14.0 | 350 | 214.3915 | 0.8 | 0.3278 | 1.5290 | 0.8000 | 0.7742 | 0.1716 | 0.0675 | | No log | 15.0 | 375 | 214.2643 | 0.8 | 0.3315 | 1.5964 | 0.8000 | 0.7697 | 0.1491 | 0.0876 | | No log | 16.0 | 400 | 214.2815 | 0.78 | 0.3751 | 1.8388 | 0.78 | 0.7670 | 0.1944 | 0.0836 | | No log | 17.0 | 425 | 214.0954 | 0.78 | 0.3505 | 1.4905 | 0.78 | 0.7521 | 0.1832 | 0.0662 | | No log | 18.0 | 450 | 214.1399 | 0.785 | 0.3392 | 1.2883 | 0.785 | 0.7662 | 0.1760 | 0.0749 | | No log | 19.0 | 475 | 214.1986 | 0.81 | 0.3229 | 1.6305 | 0.81 | 0.7984 | 0.1754 | 0.0777 | | 220.3818 | 20.0 | 500 | 214.1931 | 0.815 | 0.2994 | 1.5637 | 0.815 | 0.7940 | 0.1520 | 0.0606 | | 220.3818 | 21.0 | 525 | 213.9438 | 0.815 | 0.3066 | 1.4756 | 0.815 | 0.7983 | 0.1616 | 0.0678 | | 220.3818 | 22.0 | 550 | 213.9014 | 0.8 | 0.3485 | 1.9629 | 0.8000 | 0.7885 | 0.1838 | 0.0727 | | 220.3818 | 23.0 | 575 | 214.0186 | 0.83 | 0.2863 | 1.6314 | 0.83 | 0.8107 | 0.1516 | 0.0574 | | 220.3818 | 24.0 | 600 | 213.8764 | 0.805 | 0.3323 | 1.4873 | 0.805 | 0.7903 | 0.1716 | 0.0726 | | 220.3818 | 25.0 | 625 | 214.0043 | 0.81 | 0.3094 | 1.5562 | 0.81 | 0.7764 | 0.1625 | 0.0598 | | 220.3818 | 26.0 | 650 | 213.6884 | 0.825 | 0.3165 | 1.6150 | 0.825 | 0.8038 | 0.1667 | 0.0755 | | 220.3818 | 27.0 | 675 | 213.7763 | 0.81 | 0.3164 | 1.5526 | 0.81 | 0.7904 | 0.1767 | 0.0747 | | 220.3818 | 28.0 | 700 | 213.9658 | 0.825 | 0.2996 | 1.7947 | 0.825 | 0.8149 | 0.1686 | 0.0651 | | 220.3818 | 29.0 | 725 | 213.7030 | 0.815 | 0.3155 | 1.3772 | 0.815 | 0.8024 | 0.1616 | 0.0613 | | 220.3818 | 30.0 | 750 | 213.7211 | 0.805 | 0.3421 | 1.5621 | 0.805 | 0.7986 | 0.1794 | 0.0624 | | 220.3818 | 31.0 | 775 | 213.6852 | 0.815 | 0.3094 | 1.5177 | 0.815 | 0.7872 | 0.1500 | 0.0735 | | 220.3818 | 32.0 | 800 | 213.6889 | 0.785 | 0.3345 | 1.4134 | 0.785 | 0.7652 | 0.1669 | 0.0563 | | 220.3818 | 33.0 | 825 | 213.6302 | 0.805 | 0.3298 | 1.8630 | 0.805 | 0.7865 | 0.1689 | 0.0693 | | 220.3818 | 34.0 | 850 | 213.6116 | 0.83 | 0.2890 | 1.5365 | 0.83 | 0.8033 | 0.1555 | 0.0661 | | 220.3818 | 35.0 | 875 | 213.6136 | 0.805 | 0.3026 | 1.1797 | 0.805 | 0.7744 | 0.1551 | 0.0477 | | 220.3818 | 36.0 | 900 | 213.5340 | 0.815 | 0.3008 | 1.6963 | 0.815 | 0.7938 | 0.1531 | 0.0687 | | 220.3818 | 37.0 | 925 | 213.5380 | 0.84 | 0.2968 | 1.3848 | 0.8400 | 0.8396 | 0.1631 | 0.0577 | | 220.3818 | 38.0 | 950 | 213.5322 | 0.83 | 0.3016 | 1.5511 | 0.83 | 0.8133 | 0.1647 | 0.0599 | | 220.3818 | 39.0 | 975 | 213.4971 | 0.82 | 0.3155 | 1.4175 | 0.82 | 0.7999 | 0.1571 | 0.0535 | | 217.7955 | 40.0 | 1000 | 213.4139 | 0.825 | 0.3103 | 1.6359 | 0.825 | 0.8043 | 0.1749 | 0.0683 | | 217.7955 | 41.0 | 1025 | 213.4513 | 0.83 | 0.3002 | 1.5369 | 0.83 | 0.8139 | 0.1580 | 0.0606 | | 217.7955 | 42.0 | 1050 | 213.4196 | 0.8 | 0.3251 | 1.3570 | 0.8000 | 0.7779 | 0.1745 | 0.0608 | | 217.7955 | 43.0 | 1075 | 213.3506 | 0.815 | 0.3142 | 1.3579 | 0.815 | 0.7988 | 0.1724 | 0.0563 | | 217.7955 | 44.0 | 1100 | 213.3151 | 0.805 | 0.3217 | 1.3796 | 0.805 | 0.7820 | 0.1748 | 0.0580 | | 217.7955 | 45.0 | 1125 | 213.3202 | 0.825 | 0.3114 | 1.4198 | 0.825 | 0.8090 | 0.1568 | 0.0614 | | 217.7955 | 46.0 | 1150 | 213.3313 | 0.805 | 0.3203 | 1.3750 | 0.805 | 0.7860 | 0.1667 | 0.0563 | | 217.7955 | 47.0 | 1175 | 213.3293 | 0.835 | 0.2910 | 1.3909 | 0.835 | 0.8190 | 0.1515 | 0.0576 | | 217.7955 | 48.0 | 1200 | 213.2646 | 0.825 | 0.2916 | 1.3674 | 0.825 | 0.8022 | 0.1526 | 0.0577 | | 217.7955 | 49.0 | 1225 | 213.2620 | 0.83 | 0.3137 | 1.4579 | 0.83 | 0.8101 | 0.1634 | 0.0565 | | 217.7955 | 50.0 | 1250 | 213.2164 | 0.815 | 0.3087 | 1.2599 | 0.815 | 0.7917 | 0.1618 | 0.0573 | | 217.7955 | 51.0 | 1275 | 213.2495 | 0.795 | 0.3155 | 1.2060 | 0.795 | 0.7648 | 0.1712 | 0.0573 | | 217.7955 | 52.0 | 1300 | 213.2231 | 0.82 | 0.3232 | 1.4463 | 0.82 | 0.8029 | 0.1658 | 0.0544 | | 217.7955 | 53.0 | 1325 | 213.2242 | 0.83 | 0.2891 | 1.3586 | 0.83 | 0.8056 | 0.1427 | 0.0520 | | 217.7955 | 54.0 | 1350 | 213.2049 | 0.83 | 0.2959 | 1.2968 | 0.83 | 0.8063 | 0.1573 | 0.0523 | | 217.7955 | 55.0 | 1375 | 213.1500 | 0.84 | 0.2844 | 1.4084 | 0.8400 | 0.8137 | 0.1490 | 0.0570 | | 217.7955 | 56.0 | 1400 | 213.1851 | 0.815 | 0.3097 | 1.4005 | 0.815 | 0.7892 | 0.1605 | 0.0605 | | 217.7955 | 57.0 | 1425 | 213.1577 | 0.805 | 0.3130 | 1.2482 | 0.805 | 0.7805 | 0.1635 | 0.0550 | | 217.7955 | 58.0 | 1450 | 213.1812 | 0.835 | 0.2943 | 1.3191 | 0.835 | 0.8047 | 0.1584 | 0.0550 | | 217.7955 | 59.0 | 1475 | 213.0962 | 0.82 | 0.3102 | 1.3163 | 0.82 | 0.7906 | 0.1672 | 0.0513 | | 217.1347 | 60.0 | 1500 | 213.1257 | 0.835 | 0.2876 | 1.3165 | 0.835 | 0.8122 | 0.1433 | 0.0562 | | 217.1347 | 61.0 | 1525 | 213.1092 | 0.83 | 0.2896 | 1.3196 | 0.83 | 0.8061 | 0.1533 | 0.0549 | | 217.1347 | 62.0 | 1550 | 213.0827 | 0.815 | 0.3072 | 1.3053 | 0.815 | 0.7925 | 0.1546 | 0.0523 | | 217.1347 | 63.0 | 1575 | 213.0972 | 0.825 | 0.2965 | 1.2148 | 0.825 | 0.8044 | 0.1472 | 0.0507 | | 217.1347 | 64.0 | 1600 | 213.0849 | 0.83 | 0.2923 | 1.3196 | 0.83 | 0.8106 | 0.1637 | 0.0551 | | 217.1347 | 65.0 | 1625 | 213.0354 | 0.815 | 0.3158 | 1.2749 | 0.815 | 0.7867 | 0.1675 | 0.0515 | | 217.1347 | 66.0 | 1650 | 213.0493 | 0.825 | 0.2993 | 1.3046 | 0.825 | 0.7949 | 0.1636 | 0.0558 | | 217.1347 | 67.0 | 1675 | 212.9869 | 0.815 | 0.3081 | 1.3846 | 0.815 | 0.7868 | 0.1579 | 0.0583 | | 217.1347 | 68.0 | 1700 | 213.0629 | 0.84 | 0.2915 | 1.3902 | 0.8400 | 0.8046 | 0.1590 | 0.0540 | | 217.1347 | 69.0 | 1725 | 213.0391 | 0.825 | 0.3068 | 1.3801 | 0.825 | 0.8015 | 0.1553 | 0.0531 | | 217.1347 | 70.0 | 1750 | 212.9991 | 0.835 | 0.2864 | 1.3331 | 0.835 | 0.8097 | 0.1531 | 0.0562 | | 217.1347 | 71.0 | 1775 | 213.0157 | 0.83 | 0.2897 | 1.2788 | 0.83 | 0.8002 | 0.1584 | 0.0551 | | 217.1347 | 72.0 | 1800 | 212.9134 | 0.82 | 0.3051 | 1.3131 | 0.82 | 0.7960 | 0.1690 | 0.0522 | | 217.1347 | 73.0 | 1825 | 213.0014 | 0.825 | 0.2926 | 1.3111 | 0.825 | 0.8040 | 0.1390 | 0.0574 | | 217.1347 | 74.0 | 1850 | 212.9525 | 0.82 | 0.2985 | 1.3181 | 0.82 | 0.7962 | 0.1579 | 0.0543 | | 217.1347 | 75.0 | 1875 | 212.9581 | 0.815 | 0.3024 | 1.2835 | 0.815 | 0.7810 | 0.1648 | 0.0504 | | 217.1347 | 76.0 | 1900 | 213.0073 | 0.835 | 0.2970 | 1.3745 | 0.835 | 0.8095 | 0.1597 | 0.0579 | | 217.1347 | 77.0 | 1925 | 213.0066 | 0.805 | 0.3046 | 1.3071 | 0.805 | 0.7783 | 0.1502 | 0.0547 | | 217.1347 | 78.0 | 1950 | 212.9872 | 0.82 | 0.3018 | 1.4088 | 0.82 | 0.7928 | 0.1527 | 0.0527 | | 217.1347 | 79.0 | 1975 | 212.9629 | 0.82 | 0.3024 | 1.3665 | 0.82 | 0.8012 | 0.1626 | 0.0551 | | 216.794 | 80.0 | 2000 | 212.9545 | 0.825 | 0.3080 | 1.3609 | 0.825 | 0.8062 | 0.1652 | 0.0541 | | 216.794 | 81.0 | 2025 | 212.9253 | 0.825 | 0.3077 | 1.3779 | 0.825 | 0.8044 | 0.1662 | 0.0547 | | 216.794 | 82.0 | 2050 | 212.9501 | 0.82 | 0.3024 | 1.3636 | 0.82 | 0.7928 | 0.1677 | 0.0553 | | 216.794 | 83.0 | 2075 | 212.9160 | 0.81 | 0.3055 | 1.3686 | 0.81 | 0.7786 | 0.1624 | 0.0578 | | 216.794 | 84.0 | 2100 | 212.9532 | 0.84 | 0.2914 | 1.4589 | 0.8400 | 0.8129 | 0.1482 | 0.0510 | | 216.794 | 85.0 | 2125 | 212.9397 | 0.825 | 0.3067 | 1.3768 | 0.825 | 0.7981 | 0.1653 | 0.0537 | | 216.794 | 86.0 | 2150 | 212.8927 | 0.83 | 0.2980 | 1.3825 | 0.83 | 0.8118 | 0.1662 | 0.0560 | | 216.794 | 87.0 | 2175 | 212.8856 | 0.825 | 0.3004 | 1.4017 | 0.825 | 0.8063 | 0.1595 | 0.0555 | | 216.794 | 88.0 | 2200 | 212.9423 | 0.82 | 0.3033 | 1.3619 | 0.82 | 0.8012 | 0.1539 | 0.0517 | | 216.794 | 89.0 | 2225 | 212.8776 | 0.84 | 0.2922 | 1.3845 | 0.8400 | 0.8176 | 0.1555 | 0.0537 | | 216.794 | 90.0 | 2250 | 212.9439 | 0.82 | 0.3011 | 1.3923 | 0.82 | 0.8012 | 0.1526 | 0.0535 | | 216.794 | 91.0 | 2275 | 212.8640 | 0.815 | 0.3006 | 1.3680 | 0.815 | 0.7920 | 0.1443 | 0.0548 | | 216.794 | 92.0 | 2300 | 212.8850 | 0.825 | 0.2940 | 1.3317 | 0.825 | 0.8087 | 0.1466 | 0.0548 | | 216.794 | 93.0 | 2325 | 212.8843 | 0.825 | 0.3024 | 1.3848 | 0.825 | 0.8027 | 0.1688 | 0.0529 | | 216.794 | 94.0 | 2350 | 212.9464 | 0.825 | 0.3013 | 1.3634 | 0.825 | 0.8027 | 0.1607 | 0.0515 | | 216.794 | 95.0 | 2375 | 212.9154 | 0.825 | 0.3001 | 1.3262 | 0.825 | 0.8087 | 0.1604 | 0.0539 | | 216.794 | 96.0 | 2400 | 212.9131 | 0.825 | 0.2984 | 1.3121 | 0.825 | 0.8027 | 0.1568 | 0.0538 | | 216.794 | 97.0 | 2425 | 212.8888 | 0.82 | 0.3003 | 1.3975 | 0.82 | 0.8012 | 0.1614 | 0.0527 | | 216.794 | 98.0 | 2450 | 212.8581 | 0.825 | 0.2989 | 1.2911 | 0.825 | 0.8027 | 0.1673 | 0.0542 | | 216.794 | 99.0 | 2475 | 212.9360 | 0.83 | 0.2983 | 1.3929 | 0.83 | 0.8118 | 0.1616 | 0.0534 | | 216.6176 | 100.0 | 2500 | 212.9255 | 0.825 | 0.2955 | 1.3247 | 0.825 | 0.8027 | 0.1660 | 0.0554 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

mvkvc/convnextv2-base-22k-224-finetuned-critique

# convnextv2-base-22k-224-finetuned-critique This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2878 - Accuracy: 0.8825 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2128 | 1.0 | 56 | 0.3349 | 0.8588 | | 0.2064 | 1.99 | 112 | 0.2999 | 0.8812 | | 0.1788 | 2.99 | 168 | 0.2878 | 0.8825 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "ai", "real" ]

annazhong/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.1055 - Accuracy: 0.9674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5173 | 1.0 | 351 | 0.1623 | 0.9444 | | 0.4107 | 2.0 | 703 | 0.1182 | 0.962 | | 0.3376 | 2.99 | 1053 | 0.1055 | 0.9674 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ]

mvkvc/convnextv2-base-22k-224-finetuned-critique-100k

# convnextv2-base-22k-224-finetuned-critique-100k This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1309 - Accuracy: 0.9479 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6277 | 0.07 | 50 | 0.5987 | 0.6767 | | 0.5459 | 0.14 | 100 | 0.5187 | 0.7401 | | 0.4397 | 0.21 | 150 | 0.4448 | 0.7768 | | 0.4197 | 0.28 | 200 | 0.3686 | 0.8401 | | 0.3397 | 0.36 | 250 | 0.3153 | 0.8664 | | 0.3345 | 0.43 | 300 | 0.3071 | 0.8701 | | 0.3177 | 0.5 | 350 | 0.2576 | 0.8938 | | 0.3182 | 0.57 | 400 | 0.2546 | 0.8926 | | 0.2596 | 0.64 | 450 | 0.2320 | 0.9004 | | 0.2563 | 0.71 | 500 | 0.2205 | 0.9082 | | 0.2543 | 0.78 | 550 | 0.2142 | 0.9147 | | 0.2768 | 0.85 | 600 | 0.2136 | 0.9132 | | 0.2486 | 0.92 | 650 | 0.2052 | 0.9175 | | 0.2504 | 1.0 | 700 | 0.2314 | 0.9058 | | 0.2437 | 1.07 | 750 | 0.1943 | 0.9235 | | 0.212 | 1.14 | 800 | 0.2019 | 0.9183 | | 0.1891 | 1.21 | 850 | 0.1845 | 0.9254 | | 0.2105 | 1.28 | 900 | 0.1834 | 0.9288 | | 0.2285 | 1.35 | 950 | 0.1994 | 0.9206 | | 0.2214 | 1.42 | 1000 | 0.1804 | 0.9251 | | 0.1848 | 1.49 | 1050 | 0.1975 | 0.9196 | | 0.191 | 1.56 | 1100 | 0.1795 | 0.9269 | | 0.1794 | 1.64 | 1150 | 0.1606 | 0.9358 | | 0.2084 | 1.71 | 1200 | 0.1807 | 0.9293 | | 0.199 | 1.78 | 1250 | 0.1697 | 0.9307 | | 0.1874 | 1.85 | 1300 | 0.1650 | 0.9372 | | 0.1681 | 1.92 | 1350 | 0.1515 | 0.939 | | 0.1696 | 1.99 | 1400 | 0.1473 | 0.9416 | | 0.1651 | 2.06 | 1450 | 0.1489 | 0.9428 | | 0.1627 | 2.13 | 1500 | 0.1529 | 0.9395 | | 0.1754 | 2.2 | 1550 | 0.1540 | 0.9379 | | 0.1302 | 2.28 | 1600 | 0.1579 | 0.939 | | 0.1643 | 2.35 | 1650 | 0.1518 | 0.9401 | | 0.1938 | 2.42 | 1700 | 0.1479 | 0.941 | | 0.1441 | 2.49 | 1750 | 0.1451 | 0.9436 | | 0.1478 | 2.56 | 1800 | 0.1324 | 0.9472 | | 0.1275 | 2.63 | 1850 | 0.1340 | 0.9466 | | 0.1582 | 2.7 | 1900 | 0.1501 | 0.9391 | | 0.1472 | 2.77 | 1950 | 0.1354 | 0.9451 | | 0.1522 | 2.84 | 2000 | 0.1309 | 0.9479 | | 0.1593 | 2.92 | 2050 | 0.1433 | 0.9452 | | 0.1541 | 2.99 | 2100 | 0.1381 | 0.9466 | | 0.1297 | 3.06 | 2150 | 0.1320 | 0.9479 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "ai", "real" ]

jordyvl/vit-small_tobacco3482_hint_

# vit-small_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 56.9670 - Accuracy: 0.835 - Brier Loss: 0.2969 - Nll: 1.1900 - F1 Micro: 0.835 - F1 Macro: 0.8377 - Ece: 0.1545 - Aurc: 0.0499 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 59.9015 | 0.37 | 0.7765 | 4.3056 | 0.37 | 0.2185 | 0.2975 | 0.4588 | | No log | 2.0 | 50 | 58.9173 | 0.66 | 0.4866 | 2.0758 | 0.66 | 0.5717 | 0.2732 | 0.1578 | | No log | 3.0 | 75 | 58.3604 | 0.745 | 0.3466 | 1.5077 | 0.745 | 0.7135 | 0.1846 | 0.0854 | | No log | 4.0 | 100 | 58.0585 | 0.75 | 0.3628 | 1.5044 | 0.75 | 0.7674 | 0.2058 | 0.1052 | | No log | 5.0 | 125 | 57.8363 | 0.76 | 0.3782 | 1.7066 | 0.76 | 0.7657 | 0.2174 | 0.1039 | | No log | 6.0 | 150 | 57.4894 | 0.75 | 0.3593 | 1.5137 | 0.75 | 0.7377 | 0.1724 | 0.0800 | | No log | 7.0 | 175 | 57.5188 | 0.76 | 0.3631 | 1.9770 | 0.76 | 0.7514 | 0.1968 | 0.0874 | | No log | 8.0 | 200 | 57.4349 | 0.74 | 0.3947 | 1.8766 | 0.74 | 0.7412 | 0.1753 | 0.0777 | | No log | 9.0 | 225 | 57.1764 | 0.765 | 0.3481 | 1.1532 | 0.765 | 0.7411 | 0.1956 | 0.0829 | | No log | 10.0 | 250 | 57.6192 | 0.765 | 0.3943 | 1.8998 | 0.765 | 0.7755 | 0.1850 | 0.0981 | | No log | 11.0 | 275 | 57.2121 | 0.77 | 0.3531 | 1.2685 | 0.7700 | 0.7643 | 0.1739 | 0.0689 | | No log | 12.0 | 300 | 57.2250 | 0.795 | 0.3279 | 1.7553 | 0.795 | 0.7816 | 0.1596 | 0.0660 | | No log | 13.0 | 325 | 57.4911 | 0.785 | 0.3678 | 2.0499 | 0.785 | 0.7857 | 0.1788 | 0.0945 | | No log | 14.0 | 350 | 57.1481 | 0.77 | 0.3542 | 1.4834 | 0.7700 | 0.7649 | 0.1892 | 0.0636 | | No log | 15.0 | 375 | 57.1701 | 0.825 | 0.3041 | 1.6075 | 0.825 | 0.8223 | 0.1609 | 0.0621 | | No log | 16.0 | 400 | 57.4059 | 0.805 | 0.3343 | 1.7348 | 0.805 | 0.8080 | 0.1654 | 0.0822 | | No log | 17.0 | 425 | 57.9813 | 0.72 | 0.4616 | 2.6345 | 0.72 | 0.7252 | 0.2263 | 0.1101 | | No log | 18.0 | 450 | 57.2677 | 0.825 | 0.2953 | 1.5836 | 0.825 | 0.8171 | 0.1590 | 0.0572 | | No log | 19.0 | 475 | 57.6052 | 0.765 | 0.4023 | 1.7463 | 0.765 | 0.7333 | 0.2052 | 0.0822 | | 57.2084 | 20.0 | 500 | 57.4249 | 0.79 | 0.3653 | 1.5564 | 0.79 | 0.7941 | 0.1818 | 0.0845 | | 57.2084 | 21.0 | 525 | 57.2631 | 0.845 | 0.2704 | 1.7326 | 0.845 | 0.8312 | 0.1358 | 0.0628 | | 57.2084 | 22.0 | 550 | 57.1520 | 0.845 | 0.2723 | 1.2743 | 0.845 | 0.8386 | 0.1402 | 0.0551 | | 57.2084 | 23.0 | 575 | 57.2977 | 0.82 | 0.3137 | 1.3068 | 0.82 | 0.8029 | 0.1578 | 0.0621 | | 57.2084 | 24.0 | 600 | 57.2030 | 0.81 | 0.3107 | 1.5814 | 0.81 | 0.7870 | 0.1594 | 0.0688 | | 57.2084 | 25.0 | 625 | 57.1500 | 0.82 | 0.3027 | 1.4128 | 0.82 | 0.8229 | 0.1584 | 0.0436 | | 57.2084 | 26.0 | 650 | 57.1619 | 0.855 | 0.2735 | 1.5164 | 0.855 | 0.8558 | 0.1404 | 0.0530 | | 57.2084 | 27.0 | 675 | 57.1504 | 0.845 | 0.2832 | 1.5742 | 0.845 | 0.8507 | 0.1500 | 0.0516 | | 57.2084 | 28.0 | 700 | 57.1829 | 0.835 | 0.2932 | 1.4010 | 0.835 | 0.8410 | 0.1489 | 0.0496 | | 57.2084 | 29.0 | 725 | 57.1899 | 0.83 | 0.2953 | 1.4038 | 0.83 | 0.8338 | 0.1497 | 0.0511 | | 57.2084 | 30.0 | 750 | 57.1644 | 0.835 | 0.2948 | 1.3923 | 0.835 | 0.8374 | 0.1509 | 0.0507 | | 57.2084 | 31.0 | 775 | 57.1720 | 0.83 | 0.2958 | 1.4622 | 0.83 | 0.8296 | 0.1502 | 0.0509 | | 57.2084 | 32.0 | 800 | 57.1365 | 0.835 | 0.3024 | 1.2976 | 0.835 | 0.8374 | 0.1575 | 0.0509 | | 57.2084 | 33.0 | 825 | 57.1499 | 0.835 | 0.2995 | 1.3654 | 0.835 | 0.8308 | 0.1574 | 0.0523 | | 57.2084 | 34.0 | 850 | 57.1064 | 0.83 | 0.3022 | 1.3606 | 0.83 | 0.8251 | 0.1578 | 0.0526 | | 57.2084 | 35.0 | 875 | 57.0901 | 0.835 | 0.3003 | 1.2803 | 0.835 | 0.8336 | 0.1554 | 0.0516 | | 57.2084 | 36.0 | 900 | 57.0922 | 0.835 | 0.3047 | 1.2749 | 0.835 | 0.8336 | 0.1571 | 0.0517 | | 57.2084 | 37.0 | 925 | 57.0673 | 0.83 | 0.3034 | 1.2533 | 0.83 | 0.8344 | 0.1559 | 0.0509 | | 57.2084 | 38.0 | 950 | 57.0810 | 0.83 | 0.3024 | 1.2718 | 0.83 | 0.8344 | 0.1620 | 0.0526 | | 57.2084 | 39.0 | 975 | 57.1040 | 0.835 | 0.3041 | 1.2522 | 0.835 | 0.8392 | 0.1571 | 0.0506 | | 56.1387 | 40.0 | 1000 | 57.0542 | 0.835 | 0.3024 | 1.3210 | 0.835 | 0.8392 | 0.1525 | 0.0501 | | 56.1387 | 41.0 | 1025 | 57.0554 | 0.83 | 0.3037 | 1.3231 | 0.83 | 0.8344 | 0.1534 | 0.0508 | | 56.1387 | 42.0 | 1050 | 57.0724 | 0.83 | 0.2989 | 1.2517 | 0.83 | 0.8344 | 0.1485 | 0.0495 | | 56.1387 | 43.0 | 1075 | 57.0429 | 0.835 | 0.3010 | 1.3082 | 0.835 | 0.8401 | 0.1557 | 0.0506 | | 56.1387 | 44.0 | 1100 | 57.0208 | 0.835 | 0.3001 | 1.2428 | 0.835 | 0.8392 | 0.1583 | 0.0496 | | 56.1387 | 45.0 | 1125 | 57.0700 | 0.835 | 0.2996 | 1.3149 | 0.835 | 0.8454 | 0.1601 | 0.0509 | | 56.1387 | 46.0 | 1150 | 57.0054 | 0.835 | 0.2950 | 1.3019 | 0.835 | 0.8407 | 0.1476 | 0.0492 | | 56.1387 | 47.0 | 1175 | 57.0516 | 0.825 | 0.3000 | 1.2344 | 0.825 | 0.8317 | 0.1485 | 0.0511 | | 56.1387 | 48.0 | 1200 | 57.0373 | 0.835 | 0.3008 | 1.3016 | 0.835 | 0.8434 | 0.1611 | 0.0498 | | 56.1387 | 49.0 | 1225 | 57.0154 | 0.83 | 0.2982 | 1.2376 | 0.83 | 0.8329 | 0.1515 | 0.0501 | | 56.1387 | 50.0 | 1250 | 57.0000 | 0.835 | 0.2982 | 1.2196 | 0.835 | 0.8434 | 0.1535 | 0.0493 | | 56.1387 | 51.0 | 1275 | 57.0054 | 0.825 | 0.2987 | 1.2217 | 0.825 | 0.8352 | 0.1517 | 0.0505 | | 56.1387 | 52.0 | 1300 | 57.0347 | 0.835 | 0.2996 | 1.2239 | 0.835 | 0.8407 | 0.1643 | 0.0486 | | 56.1387 | 53.0 | 1325 | 57.0183 | 0.835 | 0.2989 | 1.2208 | 0.835 | 0.8411 | 0.1604 | 0.0495 | | 56.1387 | 54.0 | 1350 | 57.0094 | 0.845 | 0.2925 | 1.1545 | 0.845 | 0.8494 | 0.1515 | 0.0486 | | 56.1387 | 55.0 | 1375 | 57.0027 | 0.83 | 0.2974 | 1.2161 | 0.83 | 0.8380 | 0.1538 | 0.0491 | | 56.1387 | 56.0 | 1400 | 57.0060 | 0.835 | 0.2975 | 1.2215 | 0.835 | 0.8407 | 0.1546 | 0.0505 | | 56.1387 | 57.0 | 1425 | 56.9898 | 0.835 | 0.2959 | 1.1432 | 0.835 | 0.8411 | 0.1483 | 0.0501 | | 56.1387 | 58.0 | 1450 | 56.9907 | 0.835 | 0.2963 | 1.1437 | 0.835 | 0.8406 | 0.1527 | 0.0485 | | 56.1387 | 59.0 | 1475 | 56.9578 | 0.84 | 0.2935 | 1.1583 | 0.8400 | 0.8439 | 0.1513 | 0.0488 | | 55.9877 | 60.0 | 1500 | 57.0032 | 0.84 | 0.2957 | 1.2160 | 0.8400 | 0.8439 | 0.1460 | 0.0502 | | 55.9877 | 61.0 | 1525 | 56.9880 | 0.835 | 0.2990 | 1.2836 | 0.835 | 0.8406 | 0.1475 | 0.0489 | | 55.9877 | 62.0 | 1550 | 56.9920 | 0.83 | 0.2973 | 1.2071 | 0.83 | 0.8349 | 0.1519 | 0.0494 | | 55.9877 | 63.0 | 1575 | 56.9681 | 0.835 | 0.2978 | 1.2076 | 0.835 | 0.8406 | 0.1465 | 0.0483 | | 55.9877 | 64.0 | 1600 | 56.9772 | 0.835 | 0.3003 | 1.1997 | 0.835 | 0.8406 | 0.1567 | 0.0489 | | 55.9877 | 65.0 | 1625 | 56.9705 | 0.835 | 0.2973 | 1.2038 | 0.835 | 0.8406 | 0.1520 | 0.0495 | | 55.9877 | 66.0 | 1650 | 56.9682 | 0.835 | 0.2977 | 1.2005 | 0.835 | 0.8406 | 0.1576 | 0.0488 | | 55.9877 | 67.0 | 1675 | 56.9775 | 0.835 | 0.2981 | 1.2093 | 0.835 | 0.8406 | 0.1497 | 0.0501 | | 55.9877 | 68.0 | 1700 | 56.9762 | 0.835 | 0.2989 | 1.2061 | 0.835 | 0.8406 | 0.1626 | 0.0491 | | 55.9877 | 69.0 | 1725 | 56.9807 | 0.84 | 0.2978 | 1.2023 | 0.8400 | 0.8434 | 0.1503 | 0.0481 | | 55.9877 | 70.0 | 1750 | 56.9705 | 0.835 | 0.2988 | 1.1987 | 0.835 | 0.8406 | 0.1564 | 0.0487 | | 55.9877 | 71.0 | 1775 | 56.9752 | 0.83 | 0.2987 | 1.2027 | 0.83 | 0.8349 | 0.1593 | 0.0497 | | 55.9877 | 72.0 | 1800 | 56.9957 | 0.83 | 0.2996 | 1.2060 | 0.83 | 0.8349 | 0.1607 | 0.0496 | | 55.9877 | 73.0 | 1825 | 56.9697 | 0.84 | 0.2966 | 1.1977 | 0.8400 | 0.8434 | 0.1510 | 0.0487 | | 55.9877 | 74.0 | 1850 | 56.9644 | 0.83 | 0.2997 | 1.2055 | 0.83 | 0.8349 | 0.1528 | 0.0506 | | 55.9877 | 75.0 | 1875 | 56.9677 | 0.84 | 0.2968 | 1.1969 | 0.8400 | 0.8434 | 0.1536 | 0.0495 | | 55.9877 | 76.0 | 1900 | 56.9609 | 0.84 | 0.2958 | 1.1921 | 0.8400 | 0.8434 | 0.1531 | 0.0495 | | 55.9877 | 77.0 | 1925 | 56.9663 | 0.835 | 0.2965 | 1.1950 | 0.835 | 0.8406 | 0.1576 | 0.0494 | | 55.9877 | 78.0 | 1950 | 56.9796 | 0.83 | 0.2968 | 1.2049 | 0.83 | 0.8349 | 0.1525 | 0.0496 | | 55.9877 | 79.0 | 1975 | 56.9648 | 0.835 | 0.2966 | 1.1944 | 0.835 | 0.8406 | 0.1545 | 0.0494 | | 55.9237 | 80.0 | 2000 | 56.9596 | 0.845 | 0.2944 | 1.1912 | 0.845 | 0.8480 | 0.1543 | 0.0492 | | 55.9237 | 81.0 | 2025 | 56.9596 | 0.84 | 0.2951 | 1.1878 | 0.8400 | 0.8434 | 0.1546 | 0.0492 | | 55.9237 | 82.0 | 2050 | 56.9737 | 0.84 | 0.2958 | 1.1954 | 0.8400 | 0.8434 | 0.1521 | 0.0498 | | 55.9237 | 83.0 | 2075 | 56.9725 | 0.835 | 0.2974 | 1.1963 | 0.835 | 0.8377 | 0.1512 | 0.0500 | | 55.9237 | 84.0 | 2100 | 56.9743 | 0.835 | 0.2978 | 1.1928 | 0.835 | 0.8406 | 0.1554 | 0.0500 | | 55.9237 | 85.0 | 2125 | 56.9788 | 0.835 | 0.2971 | 1.1952 | 0.835 | 0.8377 | 0.1493 | 0.0500 | | 55.9237 | 86.0 | 2150 | 56.9705 | 0.84 | 0.2968 | 1.1933 | 0.8400 | 0.8434 | 0.1541 | 0.0499 | | 55.9237 | 87.0 | 2175 | 56.9684 | 0.835 | 0.2966 | 1.1926 | 0.835 | 0.8377 | 0.1517 | 0.0497 | | 55.9237 | 88.0 | 2200 | 56.9725 | 0.835 | 0.2979 | 1.1934 | 0.835 | 0.8377 | 0.1548 | 0.0497 | | 55.9237 | 89.0 | 2225 | 56.9704 | 0.84 | 0.2959 | 1.1934 | 0.8400 | 0.8434 | 0.1527 | 0.0495 | | 55.9237 | 90.0 | 2250 | 56.9681 | 0.84 | 0.2950 | 1.1907 | 0.8400 | 0.8434 | 0.1503 | 0.0498 | | 55.9237 | 91.0 | 2275 | 56.9763 | 0.835 | 0.2979 | 1.1934 | 0.835 | 0.8377 | 0.1516 | 0.0501 | | 55.9237 | 92.0 | 2300 | 56.9649 | 0.835 | 0.2959 | 1.1889 | 0.835 | 0.8377 | 0.1501 | 0.0495 | | 55.9237 | 93.0 | 2325 | 56.9687 | 0.835 | 0.2959 | 1.1871 | 0.835 | 0.8377 | 0.1519 | 0.0501 | | 55.9237 | 94.0 | 2350 | 56.9663 | 0.835 | 0.2963 | 1.1901 | 0.835 | 0.8377 | 0.1533 | 0.0496 | | 55.9237 | 95.0 | 2375 | 56.9674 | 0.84 | 0.2955 | 1.1895 | 0.8400 | 0.8434 | 0.1534 | 0.0498 | | 55.9237 | 96.0 | 2400 | 56.9661 | 0.835 | 0.2966 | 1.1907 | 0.835 | 0.8377 | 0.1520 | 0.0496 | | 55.9237 | 97.0 | 2425 | 56.9623 | 0.84 | 0.2958 | 1.1871 | 0.8400 | 0.8434 | 0.1532 | 0.0499 | | 55.9237 | 98.0 | 2450 | 56.9694 | 0.835 | 0.2969 | 1.1897 | 0.835 | 0.8377 | 0.1543 | 0.0499 | | 55.9237 | 99.0 | 2475 | 56.9698 | 0.835 | 0.2967 | 1.1906 | 0.835 | 0.8377 | 0.1543 | 0.0499 | | 55.8955 | 100.0 | 2500 | 56.9670 | 0.835 | 0.2969 | 1.1900 | 0.835 | 0.8377 | 0.1545 | 0.0499 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

dyvapandhu/vit-base-molecul

# vit-base-molecul This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6052 - Accuracy: 0.69 - F1: 0.6900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4633 | 3.85 | 50 | 0.6052 | 0.69 | 0.6900 | | 0.0341 | 7.69 | 100 | 0.8705 | 0.71 | 0.7100 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.11.0

[ "a", "c" ]

jordyvl/vit-tiny_tobacco3482_hint_

# vit-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 56.8340 - Accuracy: 0.85 - Brier Loss: 0.2391 - Nll: 1.2700 - F1 Micro: 0.85 - F1 Macro: 0.8414 - Ece: 0.1228 - Aurc: 0.0436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 59.9929 | 0.25 | 0.8706 | 4.9000 | 0.25 | 0.1856 | 0.3000 | 0.7819 | | No log | 2.0 | 50 | 59.2119 | 0.54 | 0.5951 | 2.8481 | 0.54 | 0.4540 | 0.2868 | 0.2585 | | No log | 3.0 | 75 | 58.7608 | 0.675 | 0.4417 | 1.6161 | 0.675 | 0.6086 | 0.2356 | 0.1335 | | No log | 4.0 | 100 | 58.5280 | 0.75 | 0.3897 | 1.7733 | 0.75 | 0.7378 | 0.2211 | 0.1173 | | No log | 5.0 | 125 | 58.2236 | 0.81 | 0.3119 | 1.5196 | 0.81 | 0.7653 | 0.1573 | 0.0769 | | No log | 6.0 | 150 | 58.0804 | 0.805 | 0.3048 | 1.6467 | 0.805 | 0.7766 | 0.1355 | 0.0599 | | No log | 7.0 | 175 | 58.3912 | 0.645 | 0.5320 | 2.1424 | 0.645 | 0.6017 | 0.2645 | 0.1497 | | No log | 8.0 | 200 | 57.7380 | 0.755 | 0.3634 | 1.4581 | 0.755 | 0.7173 | 0.1913 | 0.0920 | | No log | 9.0 | 225 | 57.5060 | 0.805 | 0.2939 | 1.4346 | 0.805 | 0.7860 | 0.1603 | 0.0636 | | No log | 10.0 | 250 | 57.5367 | 0.805 | 0.3345 | 1.7441 | 0.805 | 0.7872 | 0.1748 | 0.0727 | | No log | 11.0 | 275 | 57.7193 | 0.725 | 0.4195 | 1.7223 | 0.7250 | 0.6706 | 0.2121 | 0.0984 | | No log | 12.0 | 300 | 57.5447 | 0.785 | 0.3435 | 1.7574 | 0.785 | 0.7772 | 0.1667 | 0.0678 | | No log | 13.0 | 325 | 57.4991 | 0.795 | 0.3335 | 1.5337 | 0.795 | 0.7724 | 0.1685 | 0.0895 | | No log | 14.0 | 350 | 57.3536 | 0.835 | 0.2942 | 1.5660 | 0.835 | 0.8089 | 0.1451 | 0.0594 | | No log | 15.0 | 375 | 57.5013 | 0.815 | 0.3096 | 1.7294 | 0.815 | 0.7978 | 0.1579 | 0.0696 | | No log | 16.0 | 400 | 57.3610 | 0.835 | 0.2763 | 1.7078 | 0.835 | 0.8182 | 0.1354 | 0.0526 | | No log | 17.0 | 425 | 57.3415 | 0.825 | 0.3002 | 1.3895 | 0.825 | 0.8249 | 0.1455 | 0.0651 | | No log | 18.0 | 450 | 57.4573 | 0.79 | 0.3649 | 1.6263 | 0.79 | 0.7688 | 0.1936 | 0.0943 | | No log | 19.0 | 475 | 57.5728 | 0.8 | 0.3261 | 1.7616 | 0.8000 | 0.7461 | 0.1669 | 0.0659 | | 57.5035 | 20.0 | 500 | 57.3014 | 0.835 | 0.2982 | 1.3501 | 0.835 | 0.8175 | 0.1442 | 0.0549 | | 57.5035 | 21.0 | 525 | 57.3792 | 0.82 | 0.3206 | 1.4779 | 0.82 | 0.7957 | 0.1698 | 0.0568 | | 57.5035 | 22.0 | 550 | 57.6968 | 0.795 | 0.3627 | 1.8539 | 0.795 | 0.7468 | 0.1809 | 0.0698 | | 57.5035 | 23.0 | 575 | 57.4539 | 0.8 | 0.3465 | 1.4210 | 0.8000 | 0.7787 | 0.1736 | 0.0776 | | 57.5035 | 24.0 | 600 | 57.1479 | 0.815 | 0.2998 | 1.3434 | 0.815 | 0.7989 | 0.1513 | 0.0607 | | 57.5035 | 25.0 | 625 | 57.3390 | 0.79 | 0.3482 | 1.2595 | 0.79 | 0.7803 | 0.1635 | 0.0652 | | 57.5035 | 26.0 | 650 | 57.4046 | 0.82 | 0.3130 | 1.5825 | 0.82 | 0.8060 | 0.1495 | 0.0692 | | 57.5035 | 27.0 | 675 | 57.1398 | 0.835 | 0.2643 | 1.5441 | 0.835 | 0.8185 | 0.1317 | 0.0485 | | 57.5035 | 28.0 | 700 | 57.4322 | 0.82 | 0.3217 | 1.5260 | 0.82 | 0.7912 | 0.1576 | 0.0643 | | 57.5035 | 29.0 | 725 | 57.1362 | 0.87 | 0.2291 | 1.3903 | 0.87 | 0.8621 | 0.1104 | 0.0539 | | 57.5035 | 30.0 | 750 | 57.1131 | 0.855 | 0.2511 | 1.1423 | 0.855 | 0.8451 | 0.1292 | 0.0492 | | 57.5035 | 31.0 | 775 | 56.8690 | 0.845 | 0.2454 | 1.2822 | 0.845 | 0.8265 | 0.1254 | 0.0473 | | 57.5035 | 32.0 | 800 | 56.8384 | 0.855 | 0.2220 | 1.2243 | 0.855 | 0.8453 | 0.1119 | 0.0382 | | 57.5035 | 33.0 | 825 | 56.9461 | 0.855 | 0.2450 | 1.2192 | 0.855 | 0.8537 | 0.1276 | 0.0395 | | 57.5035 | 34.0 | 850 | 56.9061 | 0.85 | 0.2450 | 1.2097 | 0.85 | 0.8337 | 0.1216 | 0.0408 | | 57.5035 | 35.0 | 875 | 56.9100 | 0.86 | 0.2413 | 1.2197 | 0.8600 | 0.8546 | 0.1189 | 0.0387 | | 57.5035 | 36.0 | 900 | 56.9087 | 0.855 | 0.2444 | 1.2098 | 0.855 | 0.8460 | 0.1281 | 0.0379 | | 57.5035 | 37.0 | 925 | 56.8923 | 0.86 | 0.2438 | 1.2156 | 0.8600 | 0.8509 | 0.1259 | 0.0378 | | 57.5035 | 38.0 | 950 | 56.8908 | 0.85 | 0.2450 | 1.2187 | 0.85 | 0.8397 | 0.1236 | 0.0390 | | 57.5035 | 39.0 | 975 | 56.8591 | 0.855 | 0.2404 | 1.2063 | 0.855 | 0.8411 | 0.1326 | 0.0383 | | 56.2493 | 40.0 | 1000 | 56.8479 | 0.86 | 0.2352 | 1.2142 | 0.8600 | 0.8539 | 0.1257 | 0.0386 | | 56.2493 | 41.0 | 1025 | 56.8762 | 0.86 | 0.2365 | 1.2166 | 0.8600 | 0.8492 | 0.1171 | 0.0383 | | 56.2493 | 42.0 | 1050 | 56.8551 | 0.865 | 0.2321 | 1.2078 | 0.865 | 0.8547 | 0.1183 | 0.0383 | | 56.2493 | 43.0 | 1075 | 56.8913 | 0.87 | 0.2360 | 1.2013 | 0.87 | 0.8617 | 0.1187 | 0.0410 | | 56.2493 | 44.0 | 1100 | 56.8523 | 0.86 | 0.2359 | 1.2094 | 0.8600 | 0.8506 | 0.1214 | 0.0399 | | 56.2493 | 45.0 | 1125 | 56.8546 | 0.87 | 0.2330 | 1.2028 | 0.87 | 0.8650 | 0.1233 | 0.0384 | | 56.2493 | 46.0 | 1150 | 56.8536 | 0.865 | 0.2323 | 1.2099 | 0.865 | 0.8579 | 0.1230 | 0.0389 | | 56.2493 | 47.0 | 1175 | 56.8490 | 0.865 | 0.2346 | 1.2095 | 0.865 | 0.8580 | 0.1139 | 0.0411 | | 56.2493 | 48.0 | 1200 | 56.8569 | 0.88 | 0.2295 | 1.1277 | 0.88 | 0.8693 | 0.1238 | 0.0381 | | 56.2493 | 49.0 | 1225 | 56.8528 | 0.875 | 0.2292 | 1.1966 | 0.875 | 0.8681 | 0.1264 | 0.0394 | | 56.2493 | 50.0 | 1250 | 56.8462 | 0.875 | 0.2309 | 1.1215 | 0.875 | 0.8690 | 0.1178 | 0.0424 | | 56.2493 | 51.0 | 1275 | 56.8438 | 0.87 | 0.2285 | 1.1259 | 0.87 | 0.8631 | 0.1304 | 0.0404 | | 56.2493 | 52.0 | 1300 | 56.8660 | 0.865 | 0.2334 | 1.1231 | 0.865 | 0.8623 | 0.1343 | 0.0416 | | 56.2493 | 53.0 | 1325 | 56.8802 | 0.885 | 0.2273 | 1.1220 | 0.885 | 0.8784 | 0.1202 | 0.0415 | | 56.2493 | 54.0 | 1350 | 56.8581 | 0.885 | 0.2261 | 1.1938 | 0.885 | 0.8772 | 0.1286 | 0.0408 | | 56.2493 | 55.0 | 1375 | 56.8348 | 0.875 | 0.2301 | 1.1953 | 0.875 | 0.8675 | 0.1267 | 0.0404 | | 56.2493 | 56.0 | 1400 | 56.8489 | 0.87 | 0.2336 | 1.1974 | 0.87 | 0.8589 | 0.1079 | 0.0393 | | 56.2493 | 57.0 | 1425 | 56.8508 | 0.87 | 0.2307 | 1.1928 | 0.87 | 0.8648 | 0.1143 | 0.0399 | | 56.2493 | 58.0 | 1450 | 56.8172 | 0.875 | 0.2198 | 1.2030 | 0.875 | 0.8675 | 0.1062 | 0.0378 | | 56.2493 | 59.0 | 1475 | 56.8662 | 0.87 | 0.2287 | 1.1354 | 0.87 | 0.8659 | 0.1198 | 0.0410 | | 56.0552 | 60.0 | 1500 | 56.8437 | 0.88 | 0.2241 | 1.1968 | 0.88 | 0.8735 | 0.1127 | 0.0407 | | 56.0552 | 61.0 | 1525 | 56.8468 | 0.87 | 0.2303 | 1.1998 | 0.87 | 0.8648 | 0.1170 | 0.0428 | | 56.0552 | 62.0 | 1550 | 56.8575 | 0.865 | 0.2299 | 1.1940 | 0.865 | 0.8599 | 0.1171 | 0.0411 | | 56.0552 | 63.0 | 1575 | 56.8451 | 0.875 | 0.2291 | 1.1981 | 0.875 | 0.8697 | 0.1225 | 0.0395 | | 56.0552 | 64.0 | 1600 | 56.8355 | 0.875 | 0.2255 | 1.2009 | 0.875 | 0.8675 | 0.1137 | 0.0394 | | 56.0552 | 65.0 | 1625 | 56.8490 | 0.875 | 0.2338 | 1.2653 | 0.875 | 0.8697 | 0.1259 | 0.0413 | | 56.0552 | 66.0 | 1650 | 56.8206 | 0.87 | 0.2271 | 1.2860 | 0.87 | 0.8603 | 0.1197 | 0.0379 | | 56.0552 | 67.0 | 1675 | 56.8592 | 0.87 | 0.2307 | 1.1992 | 0.87 | 0.8636 | 0.1210 | 0.0408 | | 56.0552 | 68.0 | 1700 | 56.8339 | 0.865 | 0.2290 | 1.1968 | 0.865 | 0.8599 | 0.1101 | 0.0386 | | 56.0552 | 69.0 | 1725 | 56.8533 | 0.87 | 0.2336 | 1.2625 | 0.87 | 0.8636 | 0.1154 | 0.0406 | | 56.0552 | 70.0 | 1750 | 56.8281 | 0.87 | 0.2328 | 1.2012 | 0.87 | 0.8659 | 0.1229 | 0.0406 | | 56.0552 | 71.0 | 1775 | 56.8431 | 0.875 | 0.2335 | 1.2557 | 0.875 | 0.8697 | 0.1318 | 0.0398 | | 56.0552 | 72.0 | 1800 | 56.8396 | 0.865 | 0.2321 | 1.2659 | 0.865 | 0.8599 | 0.1165 | 0.0397 | | 56.0552 | 73.0 | 1825 | 56.8229 | 0.86 | 0.2302 | 1.2615 | 0.8600 | 0.8524 | 0.1258 | 0.0402 | | 56.0552 | 74.0 | 1850 | 56.8445 | 0.87 | 0.2344 | 1.2371 | 0.87 | 0.8659 | 0.1202 | 0.0393 | | 56.0552 | 75.0 | 1875 | 56.8475 | 0.865 | 0.2341 | 1.2660 | 0.865 | 0.8599 | 0.1202 | 0.0423 | | 56.0552 | 76.0 | 1900 | 56.8338 | 0.86 | 0.2320 | 1.2643 | 0.8600 | 0.8524 | 0.1296 | 0.0422 | | 56.0552 | 77.0 | 1925 | 56.8481 | 0.87 | 0.2353 | 1.2665 | 0.87 | 0.8659 | 0.1266 | 0.0426 | | 56.0552 | 78.0 | 1950 | 56.8328 | 0.865 | 0.2323 | 1.2584 | 0.865 | 0.8599 | 0.1128 | 0.0424 | | 56.0552 | 79.0 | 1975 | 56.8382 | 0.86 | 0.2363 | 1.2658 | 0.8600 | 0.8553 | 0.1273 | 0.0425 | | 55.9822 | 80.0 | 2000 | 56.8260 | 0.86 | 0.2354 | 1.2710 | 0.8600 | 0.8553 | 0.1129 | 0.0430 | | 55.9822 | 81.0 | 2025 | 56.8474 | 0.86 | 0.2398 | 1.2679 | 0.8600 | 0.8553 | 0.1212 | 0.0433 | | 55.9822 | 82.0 | 2050 | 56.8105 | 0.855 | 0.2320 | 1.2655 | 0.855 | 0.8478 | 0.1269 | 0.0423 | | 55.9822 | 83.0 | 2075 | 56.8240 | 0.86 | 0.2347 | 1.2651 | 0.8600 | 0.8524 | 0.1312 | 0.0425 | | 55.9822 | 84.0 | 2100 | 56.8350 | 0.86 | 0.2353 | 1.2690 | 0.8600 | 0.8553 | 0.1225 | 0.0435 | | 55.9822 | 85.0 | 2125 | 56.8317 | 0.855 | 0.2371 | 1.2674 | 0.855 | 0.8478 | 0.1211 | 0.0433 | | 55.9822 | 86.0 | 2150 | 56.8270 | 0.855 | 0.2364 | 1.2646 | 0.855 | 0.8478 | 0.1270 | 0.0433 | | 55.9822 | 87.0 | 2175 | 56.8275 | 0.855 | 0.2359 | 1.2660 | 0.855 | 0.8478 | 0.1167 | 0.0423 | | 55.9822 | 88.0 | 2200 | 56.8426 | 0.855 | 0.2385 | 1.2683 | 0.855 | 0.8478 | 0.1239 | 0.0428 | | 55.9822 | 89.0 | 2225 | 56.8376 | 0.855 | 0.2368 | 1.2676 | 0.855 | 0.8478 | 0.1239 | 0.0426 | | 55.9822 | 90.0 | 2250 | 56.8358 | 0.855 | 0.2382 | 1.2670 | 0.855 | 0.8451 | 0.1213 | 0.0435 | | 55.9822 | 91.0 | 2275 | 56.8254 | 0.86 | 0.2374 | 1.2687 | 0.8600 | 0.8536 | 0.1308 | 0.0432 | | 55.9822 | 92.0 | 2300 | 56.8269 | 0.855 | 0.2359 | 1.2684 | 0.855 | 0.8476 | 0.1223 | 0.0425 | | 55.9822 | 93.0 | 2325 | 56.8324 | 0.85 | 0.2381 | 1.2708 | 0.85 | 0.8414 | 0.1224 | 0.0432 | | 55.9822 | 94.0 | 2350 | 56.8344 | 0.85 | 0.2384 | 1.2682 | 0.85 | 0.8414 | 0.1222 | 0.0433 | | 55.9822 | 95.0 | 2375 | 56.8344 | 0.85 | 0.2387 | 1.2708 | 0.85 | 0.8414 | 0.1228 | 0.0434 | | 55.9822 | 96.0 | 2400 | 56.8342 | 0.85 | 0.2389 | 1.2687 | 0.85 | 0.8414 | 0.1222 | 0.0432 | | 55.9822 | 97.0 | 2425 | 56.8339 | 0.85 | 0.2387 | 1.2713 | 0.85 | 0.8414 | 0.1222 | 0.0433 | | 55.9822 | 98.0 | 2450 | 56.8342 | 0.85 | 0.2393 | 1.2708 | 0.85 | 0.8414 | 0.1228 | 0.0437 | | 55.9822 | 99.0 | 2475 | 56.8325 | 0.85 | 0.2390 | 1.2703 | 0.85 | 0.8414 | 0.1228 | 0.0436 | | 55.9517 | 100.0 | 2500 | 56.8340 | 0.85 | 0.2391 | 1.2700 | 0.85 | 0.8414 | 0.1228 | 0.0436 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/81-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 81-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4383 - Accuracy: 0.815 - Brier Loss: 0.3137 - Nll: 0.9180 - F1 Micro: 0.815 - F1 Macro: 0.7935 - Ece: 0.2739 - Aurc: 0.0546 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.1414 | 0.1 | 1.0055 | 8.0967 | 0.1000 | 0.0761 | 0.3253 | 0.8939 | | No log | 2.0 | 14 | 1.4539 | 0.215 | 0.8614 | 5.0189 | 0.2150 | 0.1812 | 0.2795 | 0.7550 | | No log | 3.0 | 21 | 1.1486 | 0.39 | 0.7467 | 4.3499 | 0.39 | 0.3077 | 0.2755 | 0.4066 | | No log | 4.0 | 28 | 0.9182 | 0.575 | 0.5990 | 2.9424 | 0.575 | 0.4619 | 0.2988 | 0.2367 | | No log | 5.0 | 35 | 0.8134 | 0.645 | 0.5398 | 2.1261 | 0.645 | 0.5549 | 0.2689 | 0.1878 | | No log | 6.0 | 42 | 0.7149 | 0.7 | 0.4612 | 2.2189 | 0.7 | 0.6006 | 0.2518 | 0.1316 | | No log | 7.0 | 49 | 0.6607 | 0.73 | 0.4303 | 1.7076 | 0.7300 | 0.6689 | 0.2976 | 0.1200 | | No log | 8.0 | 56 | 0.6109 | 0.755 | 0.3922 | 1.7703 | 0.755 | 0.6743 | 0.2638 | 0.0861 | | No log | 9.0 | 63 | 0.6119 | 0.715 | 0.3943 | 1.4194 | 0.715 | 0.6534 | 0.2276 | 0.0840 | | No log | 10.0 | 70 | 0.5462 | 0.8 | 0.3535 | 1.4026 | 0.8000 | 0.7760 | 0.2888 | 0.0660 | | No log | 11.0 | 77 | 0.5376 | 0.785 | 0.3481 | 1.2329 | 0.785 | 0.7600 | 0.2660 | 0.0757 | | No log | 12.0 | 84 | 0.5250 | 0.785 | 0.3442 | 1.1226 | 0.785 | 0.7669 | 0.2476 | 0.0802 | | No log | 13.0 | 91 | 0.5053 | 0.81 | 0.3313 | 1.1038 | 0.81 | 0.7824 | 0.2543 | 0.0628 | | No log | 14.0 | 98 | 0.5188 | 0.79 | 0.3497 | 1.0872 | 0.79 | 0.7678 | 0.2495 | 0.0759 | | No log | 15.0 | 105 | 0.5020 | 0.805 | 0.3412 | 1.4342 | 0.805 | 0.7868 | 0.2669 | 0.0652 | | No log | 16.0 | 112 | 0.5221 | 0.795 | 0.3496 | 1.3609 | 0.795 | 0.7473 | 0.2682 | 0.0621 | | No log | 17.0 | 119 | 0.5046 | 0.8 | 0.3372 | 1.1543 | 0.8000 | 0.7766 | 0.2604 | 0.0689 | | No log | 18.0 | 126 | 0.4733 | 0.805 | 0.3248 | 1.1335 | 0.805 | 0.7800 | 0.2599 | 0.0582 | | No log | 19.0 | 133 | 0.4725 | 0.815 | 0.3242 | 1.1607 | 0.815 | 0.7855 | 0.2733 | 0.0573 | | No log | 20.0 | 140 | 0.4887 | 0.82 | 0.3325 | 1.0316 | 0.82 | 0.7998 | 0.2684 | 0.0568 | | No log | 21.0 | 147 | 0.4708 | 0.815 | 0.3205 | 1.1275 | 0.815 | 0.8033 | 0.2412 | 0.0663 | | No log | 22.0 | 154 | 0.4773 | 0.83 | 0.3309 | 1.1147 | 0.83 | 0.8101 | 0.2739 | 0.0518 | | No log | 23.0 | 161 | 0.4957 | 0.815 | 0.3402 | 1.0884 | 0.815 | 0.8012 | 0.2582 | 0.0726 | | No log | 24.0 | 168 | 0.4666 | 0.805 | 0.3305 | 1.0784 | 0.805 | 0.7858 | 0.2792 | 0.0560 | | No log | 25.0 | 175 | 0.4830 | 0.795 | 0.3324 | 1.1757 | 0.795 | 0.7595 | 0.2505 | 0.0715 | | No log | 26.0 | 182 | 0.4622 | 0.8 | 0.3272 | 1.0698 | 0.8000 | 0.7873 | 0.2795 | 0.0590 | | No log | 27.0 | 189 | 0.4604 | 0.8 | 0.3200 | 1.1104 | 0.8000 | 0.7717 | 0.2561 | 0.0630 | | No log | 28.0 | 196 | 0.4635 | 0.82 | 0.3253 | 1.1271 | 0.82 | 0.7903 | 0.2756 | 0.0571 | | No log | 29.0 | 203 | 0.4590 | 0.815 | 0.3211 | 1.1048 | 0.815 | 0.7952 | 0.2881 | 0.0567 | | No log | 30.0 | 210 | 0.4575 | 0.795 | 0.3210 | 0.9174 | 0.795 | 0.7786 | 0.2833 | 0.0625 | | No log | 31.0 | 217 | 0.4684 | 0.83 | 0.3337 | 0.9485 | 0.83 | 0.8093 | 0.2892 | 0.0557 | | No log | 32.0 | 224 | 0.4520 | 0.81 | 0.3208 | 1.0186 | 0.81 | 0.7955 | 0.2438 | 0.0577 | | No log | 33.0 | 231 | 0.4567 | 0.825 | 0.3233 | 0.9246 | 0.825 | 0.7928 | 0.2665 | 0.0592 | | No log | 34.0 | 238 | 0.4468 | 0.82 | 0.3152 | 1.0065 | 0.82 | 0.8000 | 0.2710 | 0.0563 | | No log | 35.0 | 245 | 0.4562 | 0.78 | 0.3244 | 1.0626 | 0.78 | 0.7614 | 0.2602 | 0.0624 | | No log | 36.0 | 252 | 0.4542 | 0.815 | 0.3223 | 1.1362 | 0.815 | 0.7852 | 0.2584 | 0.0579 | | No log | 37.0 | 259 | 0.4441 | 0.82 | 0.3136 | 1.0419 | 0.82 | 0.7901 | 0.2790 | 0.0529 | | No log | 38.0 | 266 | 0.4408 | 0.825 | 0.3125 | 0.9860 | 0.825 | 0.8023 | 0.2766 | 0.0553 | | No log | 39.0 | 273 | 0.4354 | 0.83 | 0.3082 | 0.8958 | 0.83 | 0.8116 | 0.2713 | 0.0504 | | No log | 40.0 | 280 | 0.4465 | 0.79 | 0.3164 | 1.1111 | 0.79 | 0.7715 | 0.2668 | 0.0628 | | No log | 41.0 | 287 | 0.4416 | 0.845 | 0.3128 | 1.0103 | 0.845 | 0.8162 | 0.3044 | 0.0527 | | No log | 42.0 | 294 | 0.4463 | 0.83 | 0.3165 | 1.0849 | 0.83 | 0.8106 | 0.2683 | 0.0580 | | No log | 43.0 | 301 | 0.4405 | 0.845 | 0.3132 | 1.0312 | 0.845 | 0.8247 | 0.2792 | 0.0509 | | No log | 44.0 | 308 | 0.4443 | 0.83 | 0.3174 | 0.9196 | 0.83 | 0.8094 | 0.2687 | 0.0524 | | No log | 45.0 | 315 | 0.4445 | 0.82 | 0.3194 | 1.0665 | 0.82 | 0.7897 | 0.2651 | 0.0560 | | No log | 46.0 | 322 | 0.4405 | 0.81 | 0.3133 | 1.1805 | 0.81 | 0.7770 | 0.2771 | 0.0550 | | No log | 47.0 | 329 | 0.4380 | 0.84 | 0.3132 | 0.9508 | 0.8400 | 0.8104 | 0.2916 | 0.0535 | | No log | 48.0 | 336 | 0.4407 | 0.825 | 0.3139 | 0.9044 | 0.825 | 0.7978 | 0.2702 | 0.0542 | | No log | 49.0 | 343 | 0.4418 | 0.835 | 0.3154 | 0.8965 | 0.835 | 0.8178 | 0.2877 | 0.0569 | | No log | 50.0 | 350 | 0.4368 | 0.825 | 0.3123 | 0.9774 | 0.825 | 0.8073 | 0.2607 | 0.0531 | | No log | 51.0 | 357 | 0.4402 | 0.825 | 0.3140 | 0.9170 | 0.825 | 0.8052 | 0.2810 | 0.0550 | | No log | 52.0 | 364 | 0.4374 | 0.82 | 0.3107 | 0.9873 | 0.82 | 0.7952 | 0.2602 | 0.0542 | | No log | 53.0 | 371 | 0.4368 | 0.83 | 0.3120 | 0.9832 | 0.83 | 0.8084 | 0.2709 | 0.0541 | | No log | 54.0 | 378 | 0.4375 | 0.82 | 0.3131 | 0.9094 | 0.82 | 0.7943 | 0.2633 | 0.0538 | | No log | 55.0 | 385 | 0.4379 | 0.815 | 0.3134 | 0.9927 | 0.815 | 0.7856 | 0.2960 | 0.0552 | | No log | 56.0 | 392 | 0.4370 | 0.83 | 0.3125 | 0.9746 | 0.83 | 0.8100 | 0.2744 | 0.0535 | | No log | 57.0 | 399 | 0.4366 | 0.825 | 0.3123 | 1.0392 | 0.825 | 0.8021 | 0.2730 | 0.0536 | | No log | 58.0 | 406 | 0.4372 | 0.825 | 0.3129 | 0.9174 | 0.825 | 0.8026 | 0.2800 | 0.0542 | | No log | 59.0 | 413 | 0.4380 | 0.81 | 0.3134 | 0.9770 | 0.81 | 0.7831 | 0.2612 | 0.0557 | | No log | 60.0 | 420 | 0.4374 | 0.82 | 0.3130 | 0.9124 | 0.82 | 0.7961 | 0.2589 | 0.0541 | | No log | 61.0 | 427 | 0.4366 | 0.825 | 0.3121 | 0.9038 | 0.825 | 0.8061 | 0.2641 | 0.0538 | | No log | 62.0 | 434 | 0.4372 | 0.825 | 0.3126 | 0.9105 | 0.825 | 0.8042 | 0.2684 | 0.0547 | | No log | 63.0 | 441 | 0.4381 | 0.82 | 0.3135 | 0.9160 | 0.82 | 0.7961 | 0.2810 | 0.0545 | | No log | 64.0 | 448 | 0.4376 | 0.83 | 0.3133 | 0.9134 | 0.83 | 0.8100 | 0.2757 | 0.0539 | | No log | 65.0 | 455 | 0.4376 | 0.825 | 0.3130 | 0.9133 | 0.825 | 0.8061 | 0.2977 | 0.0541 | | No log | 66.0 | 462 | 0.4378 | 0.825 | 0.3133 | 0.9153 | 0.825 | 0.8061 | 0.2767 | 0.0543 | | No log | 67.0 | 469 | 0.4373 | 0.825 | 0.3129 | 0.9139 | 0.825 | 0.8042 | 0.2905 | 0.0541 | | No log | 68.0 | 476 | 0.4375 | 0.82 | 0.3129 | 0.9128 | 0.82 | 0.7961 | 0.2739 | 0.0543 | | No log | 69.0 | 483 | 0.4376 | 0.82 | 0.3131 | 0.9125 | 0.82 | 0.7961 | 0.2757 | 0.0542 | | No log | 70.0 | 490 | 0.4377 | 0.825 | 0.3133 | 0.9174 | 0.825 | 0.8061 | 0.2924 | 0.0538 | | No log | 71.0 | 497 | 0.4380 | 0.82 | 0.3134 | 0.9179 | 0.82 | 0.7961 | 0.2896 | 0.0541 | | 0.2684 | 72.0 | 504 | 0.4378 | 0.82 | 0.3133 | 0.9148 | 0.82 | 0.8035 | 0.2912 | 0.0543 | | 0.2684 | 73.0 | 511 | 0.4375 | 0.82 | 0.3131 | 0.9169 | 0.82 | 0.7961 | 0.2731 | 0.0542 | | 0.2684 | 74.0 | 518 | 0.4379 | 0.82 | 0.3133 | 0.9177 | 0.82 | 0.7961 | 0.2732 | 0.0540 | | 0.2684 | 75.0 | 525 | 0.4383 | 0.82 | 0.3138 | 0.9194 | 0.82 | 0.8035 | 0.2835 | 0.0545 | | 0.2684 | 76.0 | 532 | 0.4378 | 0.815 | 0.3133 | 0.9133 | 0.815 | 0.7935 | 0.2824 | 0.0543 | | 0.2684 | 77.0 | 539 | 0.4378 | 0.815 | 0.3133 | 0.9146 | 0.815 | 0.7935 | 0.2735 | 0.0542 | | 0.2684 | 78.0 | 546 | 0.4379 | 0.815 | 0.3134 | 0.9139 | 0.815 | 0.7935 | 0.2828 | 0.0547 | | 0.2684 | 79.0 | 553 | 0.4382 | 0.815 | 0.3136 | 0.9179 | 0.815 | 0.7935 | 0.2817 | 0.0547 | | 0.2684 | 80.0 | 560 | 0.4380 | 0.815 | 0.3134 | 0.9168 | 0.815 | 0.7935 | 0.2818 | 0.0545 | | 0.2684 | 81.0 | 567 | 0.4381 | 0.815 | 0.3135 | 0.9183 | 0.815 | 0.7935 | 0.2736 | 0.0544 | | 0.2684 | 82.0 | 574 | 0.4379 | 0.815 | 0.3134 | 0.9164 | 0.815 | 0.7935 | 0.2736 | 0.0544 | | 0.2684 | 83.0 | 581 | 0.4382 | 0.815 | 0.3136 | 0.9168 | 0.815 | 0.7935 | 0.2736 | 0.0541 | | 0.2684 | 84.0 | 588 | 0.4381 | 0.815 | 0.3136 | 0.9199 | 0.815 | 0.7935 | 0.2737 | 0.0541 | | 0.2684 | 85.0 | 595 | 0.4380 | 0.815 | 0.3134 | 0.9175 | 0.815 | 0.7935 | 0.2735 | 0.0543 | | 0.2684 | 86.0 | 602 | 0.4383 | 0.815 | 0.3137 | 0.9197 | 0.815 | 0.7935 | 0.2674 | 0.0545 | | 0.2684 | 87.0 | 609 | 0.4381 | 0.815 | 0.3135 | 0.9176 | 0.815 | 0.7935 | 0.2738 | 0.0547 | | 0.2684 | 88.0 | 616 | 0.4381 | 0.815 | 0.3135 | 0.9179 | 0.815 | 0.7935 | 0.2736 | 0.0541 | | 0.2684 | 89.0 | 623 | 0.4381 | 0.815 | 0.3135 | 0.9177 | 0.815 | 0.7935 | 0.2737 | 0.0543 | | 0.2684 | 90.0 | 630 | 0.4382 | 0.815 | 0.3136 | 0.9173 | 0.815 | 0.7935 | 0.2736 | 0.0544 | | 0.2684 | 91.0 | 637 | 0.4383 | 0.815 | 0.3136 | 0.9187 | 0.815 | 0.7935 | 0.2738 | 0.0546 | | 0.2684 | 92.0 | 644 | 0.4382 | 0.815 | 0.3136 | 0.9171 | 0.815 | 0.7935 | 0.2738 | 0.0544 | | 0.2684 | 93.0 | 651 | 0.4383 | 0.815 | 0.3137 | 0.9190 | 0.815 | 0.7935 | 0.2738 | 0.0546 | | 0.2684 | 94.0 | 658 | 0.4382 | 0.815 | 0.3136 | 0.9187 | 0.815 | 0.7935 | 0.2737 | 0.0543 | | 0.2684 | 95.0 | 665 | 0.4383 | 0.815 | 0.3136 | 0.9184 | 0.815 | 0.7935 | 0.2739 | 0.0545 | | 0.2684 | 96.0 | 672 | 0.4382 | 0.815 | 0.3136 | 0.9184 | 0.815 | 0.7935 | 0.2737 | 0.0545 | | 0.2684 | 97.0 | 679 | 0.4382 | 0.815 | 0.3136 | 0.9179 | 0.815 | 0.7935 | 0.2739 | 0.0545 | | 0.2684 | 98.0 | 686 | 0.4383 | 0.815 | 0.3137 | 0.9185 | 0.815 | 0.7935 | 0.2738 | 0.0544 | | 0.2684 | 99.0 | 693 | 0.4383 | 0.815 | 0.3137 | 0.9182 | 0.815 | 0.7935 | 0.2739 | 0.0546 | | 0.2684 | 100.0 | 700 | 0.4383 | 0.815 | 0.3137 | 0.9180 | 0.815 | 0.7935 | 0.2739 | 0.0546 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/81-tiny_tobacco3482

# 81-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1635 - Accuracy: 0.815 - Brier Loss: 0.3719 - Nll: 0.7360 - F1 Micro: 0.815 - F1 Macro: 0.7973 - Ece: 0.3345 - Aurc: 0.0542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.2942 | 0.16 | 1.0273 | 8.4554 | 0.16 | 0.0543 | 0.3538 | 0.8479 | | No log | 2.0 | 14 | 1.1952 | 0.095 | 0.9063 | 7.1126 | 0.095 | 0.0819 | 0.2327 | 0.8615 | | No log | 3.0 | 21 | 0.7862 | 0.305 | 0.8434 | 5.1628 | 0.305 | 0.1452 | 0.2965 | 0.6092 | | No log | 4.0 | 28 | 0.6144 | 0.385 | 0.7594 | 3.3452 | 0.3850 | 0.2942 | 0.2978 | 0.4140 | | No log | 5.0 | 35 | 0.5040 | 0.54 | 0.6858 | 2.6433 | 0.54 | 0.4419 | 0.3200 | 0.2853 | | No log | 6.0 | 42 | 0.4291 | 0.625 | 0.6353 | 2.2564 | 0.625 | 0.5273 | 0.3772 | 0.1975 | | No log | 7.0 | 49 | 0.3839 | 0.63 | 0.5815 | 2.0215 | 0.63 | 0.4970 | 0.3469 | 0.1795 | | No log | 8.0 | 56 | 0.3390 | 0.705 | 0.5512 | 1.5239 | 0.705 | 0.5983 | 0.3704 | 0.1337 | | No log | 9.0 | 63 | 0.3128 | 0.705 | 0.5015 | 1.7837 | 0.705 | 0.6172 | 0.3403 | 0.1138 | | No log | 10.0 | 70 | 0.2868 | 0.705 | 0.4532 | 1.1243 | 0.705 | 0.6108 | 0.3221 | 0.1015 | | No log | 11.0 | 77 | 0.2866 | 0.74 | 0.4532 | 1.0967 | 0.74 | 0.6909 | 0.3422 | 0.0932 | | No log | 12.0 | 84 | 0.2625 | 0.765 | 0.4235 | 1.4646 | 0.765 | 0.6788 | 0.3240 | 0.0805 | | No log | 13.0 | 91 | 0.2516 | 0.73 | 0.4141 | 1.4855 | 0.7300 | 0.6495 | 0.2770 | 0.0869 | | No log | 14.0 | 98 | 0.2487 | 0.805 | 0.4440 | 1.2873 | 0.805 | 0.7319 | 0.3699 | 0.0670 | | No log | 15.0 | 105 | 0.2103 | 0.77 | 0.4029 | 1.0834 | 0.7700 | 0.6891 | 0.3007 | 0.0677 | | No log | 16.0 | 112 | 0.2216 | 0.8 | 0.4029 | 1.0621 | 0.8000 | 0.7247 | 0.3513 | 0.0611 | | No log | 17.0 | 119 | 0.2230 | 0.745 | 0.3999 | 1.0367 | 0.745 | 0.6710 | 0.2992 | 0.0809 | | No log | 18.0 | 126 | 0.2301 | 0.805 | 0.4044 | 0.9650 | 0.805 | 0.7473 | 0.3406 | 0.0538 | | No log | 19.0 | 133 | 0.2024 | 0.795 | 0.3935 | 0.9429 | 0.795 | 0.7562 | 0.3327 | 0.0669 | | No log | 20.0 | 140 | 0.1959 | 0.82 | 0.3935 | 0.8984 | 0.82 | 0.8000 | 0.3453 | 0.0571 | | No log | 21.0 | 147 | 0.2020 | 0.815 | 0.3946 | 0.8507 | 0.815 | 0.7949 | 0.3669 | 0.0588 | | No log | 22.0 | 154 | 0.2100 | 0.805 | 0.3758 | 0.8083 | 0.805 | 0.7743 | 0.3087 | 0.0625 | | No log | 23.0 | 161 | 0.2002 | 0.81 | 0.3881 | 1.0046 | 0.81 | 0.7953 | 0.3276 | 0.0617 | | No log | 24.0 | 168 | 0.2004 | 0.83 | 0.3987 | 0.8922 | 0.83 | 0.8034 | 0.3565 | 0.0531 | | No log | 25.0 | 175 | 0.1914 | 0.785 | 0.3699 | 0.8571 | 0.785 | 0.7484 | 0.3117 | 0.0601 | | No log | 26.0 | 182 | 0.1845 | 0.815 | 0.3764 | 0.8153 | 0.815 | 0.7970 | 0.3215 | 0.0539 | | No log | 27.0 | 189 | 0.1821 | 0.835 | 0.3815 | 0.8488 | 0.835 | 0.8175 | 0.3441 | 0.0497 | | No log | 28.0 | 196 | 0.1869 | 0.84 | 0.3808 | 0.8654 | 0.8400 | 0.8236 | 0.3639 | 0.0504 | | No log | 29.0 | 203 | 0.1859 | 0.79 | 0.3752 | 0.7067 | 0.79 | 0.7661 | 0.3019 | 0.0618 | | No log | 30.0 | 210 | 0.1842 | 0.795 | 0.3826 | 0.9031 | 0.795 | 0.7646 | 0.3170 | 0.0599 | | No log | 31.0 | 217 | 0.1797 | 0.815 | 0.3714 | 0.8572 | 0.815 | 0.8038 | 0.3214 | 0.0588 | | No log | 32.0 | 224 | 0.1754 | 0.805 | 0.3679 | 0.7412 | 0.805 | 0.7883 | 0.3063 | 0.0563 | | No log | 33.0 | 231 | 0.1790 | 0.835 | 0.3736 | 0.8357 | 0.835 | 0.8128 | 0.3431 | 0.0497 | | No log | 34.0 | 238 | 0.1761 | 0.81 | 0.3744 | 0.7360 | 0.81 | 0.7907 | 0.3292 | 0.0526 | | No log | 35.0 | 245 | 0.1731 | 0.83 | 0.3744 | 0.7494 | 0.83 | 0.8202 | 0.3351 | 0.0540 | | No log | 36.0 | 252 | 0.1758 | 0.79 | 0.3738 | 0.9441 | 0.79 | 0.7710 | 0.3099 | 0.0625 | | No log | 37.0 | 259 | 0.1730 | 0.81 | 0.3785 | 0.9418 | 0.81 | 0.7939 | 0.3425 | 0.0577 | | No log | 38.0 | 266 | 0.1727 | 0.815 | 0.3752 | 0.7539 | 0.815 | 0.8001 | 0.3290 | 0.0540 | | No log | 39.0 | 273 | 0.1769 | 0.81 | 0.3754 | 0.9013 | 0.81 | 0.7888 | 0.3296 | 0.0563 | | No log | 40.0 | 280 | 0.1770 | 0.805 | 0.3809 | 0.7637 | 0.805 | 0.7809 | 0.3291 | 0.0519 | | No log | 41.0 | 287 | 0.1771 | 0.815 | 0.3814 | 0.7495 | 0.815 | 0.7993 | 0.3352 | 0.0565 | | No log | 42.0 | 294 | 0.1745 | 0.8 | 0.3807 | 0.8386 | 0.8000 | 0.7865 | 0.3212 | 0.0601 | | No log | 43.0 | 301 | 0.1700 | 0.825 | 0.3762 | 0.8216 | 0.825 | 0.8067 | 0.3420 | 0.0517 | | No log | 44.0 | 308 | 0.1686 | 0.825 | 0.3706 | 0.7310 | 0.825 | 0.8024 | 0.3448 | 0.0509 | | No log | 45.0 | 315 | 0.1692 | 0.81 | 0.3739 | 0.6805 | 0.81 | 0.7914 | 0.3397 | 0.0508 | | No log | 46.0 | 322 | 0.1697 | 0.825 | 0.3709 | 0.7482 | 0.825 | 0.7995 | 0.3364 | 0.0531 | | No log | 47.0 | 329 | 0.1681 | 0.805 | 0.3735 | 0.8810 | 0.805 | 0.7872 | 0.3388 | 0.0585 | | No log | 48.0 | 336 | 0.1667 | 0.815 | 0.3731 | 0.6788 | 0.815 | 0.7968 | 0.3427 | 0.0567 | | No log | 49.0 | 343 | 0.1690 | 0.82 | 0.3786 | 0.8693 | 0.82 | 0.7967 | 0.3507 | 0.0502 | | No log | 50.0 | 350 | 0.1668 | 0.82 | 0.3761 | 0.7854 | 0.82 | 0.8032 | 0.3509 | 0.0526 | | No log | 51.0 | 357 | 0.1659 | 0.82 | 0.3760 | 0.7437 | 0.82 | 0.8045 | 0.3374 | 0.0557 | | No log | 52.0 | 364 | 0.1663 | 0.805 | 0.3682 | 0.7932 | 0.805 | 0.7863 | 0.3171 | 0.0560 | | No log | 53.0 | 371 | 0.1649 | 0.805 | 0.3649 | 0.7249 | 0.805 | 0.7863 | 0.3139 | 0.0562 | | No log | 54.0 | 378 | 0.1654 | 0.815 | 0.3725 | 0.7390 | 0.815 | 0.7935 | 0.3273 | 0.0560 | | No log | 55.0 | 385 | 0.1667 | 0.84 | 0.3741 | 0.7324 | 0.8400 | 0.8279 | 0.3501 | 0.0513 | | No log | 56.0 | 392 | 0.1659 | 0.825 | 0.3718 | 0.7278 | 0.825 | 0.8058 | 0.3288 | 0.0522 | | No log | 57.0 | 399 | 0.1659 | 0.825 | 0.3731 | 0.7351 | 0.825 | 0.8084 | 0.3204 | 0.0537 | | No log | 58.0 | 406 | 0.1645 | 0.825 | 0.3730 | 0.7356 | 0.825 | 0.8068 | 0.3468 | 0.0524 | | No log | 59.0 | 413 | 0.1626 | 0.825 | 0.3715 | 0.7282 | 0.825 | 0.8040 | 0.3391 | 0.0527 | | No log | 60.0 | 420 | 0.1628 | 0.825 | 0.3698 | 0.7297 | 0.825 | 0.8056 | 0.3212 | 0.0534 | | No log | 61.0 | 427 | 0.1630 | 0.825 | 0.3719 | 0.7312 | 0.825 | 0.8065 | 0.3260 | 0.0533 | | No log | 62.0 | 434 | 0.1642 | 0.82 | 0.3714 | 0.7335 | 0.82 | 0.8040 | 0.3388 | 0.0549 | | No log | 63.0 | 441 | 0.1634 | 0.82 | 0.3733 | 0.7349 | 0.82 | 0.8011 | 0.3399 | 0.0553 | | No log | 64.0 | 448 | 0.1628 | 0.815 | 0.3710 | 0.7301 | 0.815 | 0.7974 | 0.3249 | 0.0532 | | No log | 65.0 | 455 | 0.1629 | 0.815 | 0.3704 | 0.7362 | 0.815 | 0.7981 | 0.3362 | 0.0541 | | No log | 66.0 | 462 | 0.1630 | 0.82 | 0.3727 | 0.7354 | 0.82 | 0.8024 | 0.3559 | 0.0526 | | No log | 67.0 | 469 | 0.1633 | 0.825 | 0.3733 | 0.7355 | 0.825 | 0.8038 | 0.3492 | 0.0526 | | No log | 68.0 | 476 | 0.1638 | 0.82 | 0.3724 | 0.7369 | 0.82 | 0.8040 | 0.3468 | 0.0550 | | No log | 69.0 | 483 | 0.1629 | 0.82 | 0.3717 | 0.7329 | 0.82 | 0.8040 | 0.3251 | 0.0536 | | No log | 70.0 | 490 | 0.1629 | 0.815 | 0.3709 | 0.7312 | 0.815 | 0.7947 | 0.3221 | 0.0541 | | No log | 71.0 | 497 | 0.1630 | 0.815 | 0.3718 | 0.7371 | 0.815 | 0.7973 | 0.3436 | 0.0545 | | 0.1743 | 72.0 | 504 | 0.1631 | 0.815 | 0.3712 | 0.7350 | 0.815 | 0.7973 | 0.3264 | 0.0536 | | 0.1743 | 73.0 | 511 | 0.1634 | 0.815 | 0.3721 | 0.7348 | 0.815 | 0.7981 | 0.3340 | 0.0543 | | 0.1743 | 74.0 | 518 | 0.1631 | 0.815 | 0.3716 | 0.7332 | 0.815 | 0.7973 | 0.3279 | 0.0541 | | 0.1743 | 75.0 | 525 | 0.1633 | 0.82 | 0.3720 | 0.7346 | 0.82 | 0.8008 | 0.3186 | 0.0542 | | 0.1743 | 76.0 | 532 | 0.1631 | 0.815 | 0.3716 | 0.7342 | 0.815 | 0.7973 | 0.3189 | 0.0542 | | 0.1743 | 77.0 | 539 | 0.1633 | 0.815 | 0.3718 | 0.7358 | 0.815 | 0.7973 | 0.3344 | 0.0542 | | 0.1743 | 78.0 | 546 | 0.1633 | 0.815 | 0.3718 | 0.7363 | 0.815 | 0.7991 | 0.3342 | 0.0542 | | 0.1743 | 79.0 | 553 | 0.1633 | 0.815 | 0.3718 | 0.7350 | 0.815 | 0.7973 | 0.3344 | 0.0543 | | 0.1743 | 80.0 | 560 | 0.1634 | 0.815 | 0.3718 | 0.7354 | 0.815 | 0.7973 | 0.3136 | 0.0542 | | 0.1743 | 81.0 | 567 | 0.1633 | 0.815 | 0.3721 | 0.7360 | 0.815 | 0.7973 | 0.3279 | 0.0543 | | 0.1743 | 82.0 | 574 | 0.1634 | 0.815 | 0.3719 | 0.7358 | 0.815 | 0.7973 | 0.3276 | 0.0542 | | 0.1743 | 83.0 | 581 | 0.1634 | 0.815 | 0.3720 | 0.7358 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 84.0 | 588 | 0.1633 | 0.815 | 0.3717 | 0.7352 | 0.815 | 0.7973 | 0.3275 | 0.0542 | | 0.1743 | 85.0 | 595 | 0.1633 | 0.815 | 0.3720 | 0.7357 | 0.815 | 0.7973 | 0.3194 | 0.0541 | | 0.1743 | 86.0 | 602 | 0.1634 | 0.815 | 0.3717 | 0.7361 | 0.815 | 0.7973 | 0.3276 | 0.0542 | | 0.1743 | 87.0 | 609 | 0.1634 | 0.815 | 0.3719 | 0.7359 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 88.0 | 616 | 0.1634 | 0.815 | 0.3720 | 0.7359 | 0.815 | 0.7973 | 0.3278 | 0.0542 | | 0.1743 | 89.0 | 623 | 0.1634 | 0.815 | 0.3718 | 0.7359 | 0.815 | 0.7973 | 0.3192 | 0.0543 | | 0.1743 | 90.0 | 630 | 0.1635 | 0.815 | 0.3719 | 0.7359 | 0.815 | 0.7973 | 0.3278 | 0.0543 | | 0.1743 | 91.0 | 637 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3344 | 0.0541 | | 0.1743 | 92.0 | 644 | 0.1635 | 0.815 | 0.3719 | 0.7361 | 0.815 | 0.7973 | 0.3278 | 0.0542 | | 0.1743 | 93.0 | 651 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 94.0 | 658 | 0.1635 | 0.815 | 0.3719 | 0.7356 | 0.815 | 0.7973 | 0.3261 | 0.0543 | | 0.1743 | 95.0 | 665 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 96.0 | 672 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3278 | 0.0542 | | 0.1743 | 97.0 | 679 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 98.0 | 686 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3278 | 0.0543 | | 0.1743 | 99.0 | 693 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 | | 0.1743 | 100.0 | 700 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/81-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# 81-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0317 - Accuracy: 0.86 - Brier Loss: 0.2316 - Nll: 0.9681 - F1 Micro: 0.8600 - F1 Macro: 0.8444 - Ece: 0.1162 - Aurc: 0.0505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 6.0999 | 0.095 | 1.0191 | 8.7249 | 0.095 | 0.0767 | 0.3174 | 0.8988 | | No log | 2.0 | 14 | 5.0037 | 0.145 | 0.9023 | 8.1615 | 0.145 | 0.1212 | 0.2431 | 0.8041 | | No log | 3.0 | 21 | 4.5740 | 0.265 | 0.8363 | 5.9714 | 0.265 | 0.2003 | 0.2732 | 0.5711 | | No log | 4.0 | 28 | 4.3460 | 0.435 | 0.7292 | 3.9720 | 0.435 | 0.3780 | 0.3029 | 0.3535 | | No log | 5.0 | 35 | 4.1953 | 0.565 | 0.6344 | 2.7772 | 0.565 | 0.4465 | 0.3056 | 0.2433 | | No log | 6.0 | 42 | 4.0769 | 0.665 | 0.5852 | 2.3975 | 0.665 | 0.5345 | 0.3469 | 0.1662 | | No log | 7.0 | 49 | 3.9670 | 0.685 | 0.5103 | 2.1879 | 0.685 | 0.5645 | 0.3335 | 0.1339 | | No log | 8.0 | 56 | 3.9085 | 0.74 | 0.4625 | 1.7988 | 0.74 | 0.6444 | 0.3276 | 0.1028 | | No log | 9.0 | 63 | 3.8671 | 0.765 | 0.4073 | 1.5650 | 0.765 | 0.6567 | 0.2800 | 0.0878 | | No log | 10.0 | 70 | 3.8008 | 0.77 | 0.3617 | 1.4468 | 0.7700 | 0.6730 | 0.2443 | 0.0692 | | No log | 11.0 | 77 | 3.8924 | 0.76 | 0.3685 | 1.4401 | 0.76 | 0.7045 | 0.2129 | 0.0859 | | No log | 12.0 | 84 | 3.8523 | 0.78 | 0.3275 | 1.3333 | 0.78 | 0.7165 | 0.2191 | 0.0691 | | No log | 13.0 | 91 | 3.7745 | 0.78 | 0.3229 | 1.4554 | 0.78 | 0.6820 | 0.1980 | 0.0653 | | No log | 14.0 | 98 | 3.8155 | 0.805 | 0.3070 | 1.1638 | 0.805 | 0.7564 | 0.1993 | 0.0621 | | No log | 15.0 | 105 | 3.8060 | 0.815 | 0.3038 | 1.2009 | 0.815 | 0.7624 | 0.2051 | 0.0607 | | No log | 16.0 | 112 | 3.8269 | 0.81 | 0.3044 | 1.2937 | 0.81 | 0.7751 | 0.1978 | 0.0643 | | No log | 17.0 | 119 | 3.8191 | 0.83 | 0.2841 | 0.9571 | 0.83 | 0.7978 | 0.1773 | 0.0593 | | No log | 18.0 | 126 | 3.8986 | 0.81 | 0.3109 | 1.1933 | 0.81 | 0.7858 | 0.1728 | 0.0692 | | No log | 19.0 | 133 | 3.8134 | 0.845 | 0.2694 | 0.9314 | 0.845 | 0.8175 | 0.1791 | 0.0549 | | No log | 20.0 | 140 | 3.8148 | 0.825 | 0.2768 | 0.8896 | 0.825 | 0.8108 | 0.1495 | 0.0619 | | No log | 21.0 | 147 | 3.7769 | 0.83 | 0.2639 | 0.9579 | 0.83 | 0.8064 | 0.1521 | 0.0521 | | No log | 22.0 | 154 | 3.7941 | 0.84 | 0.2596 | 0.9457 | 0.8400 | 0.8176 | 0.1644 | 0.0534 | | No log | 23.0 | 161 | 3.9296 | 0.79 | 0.2938 | 1.0757 | 0.79 | 0.7732 | 0.1612 | 0.0706 | | No log | 24.0 | 168 | 3.7899 | 0.815 | 0.2734 | 1.0937 | 0.815 | 0.7910 | 0.1570 | 0.0531 | | No log | 25.0 | 175 | 3.8261 | 0.855 | 0.2554 | 0.9640 | 0.855 | 0.8370 | 0.1378 | 0.0559 | | No log | 26.0 | 182 | 3.8596 | 0.805 | 0.2626 | 1.0429 | 0.805 | 0.7814 | 0.1548 | 0.0597 | | No log | 27.0 | 189 | 3.7974 | 0.825 | 0.2637 | 1.0256 | 0.825 | 0.8107 | 0.1251 | 0.0522 | | No log | 28.0 | 196 | 3.8304 | 0.83 | 0.2555 | 1.1640 | 0.83 | 0.8081 | 0.1337 | 0.0561 | | No log | 29.0 | 203 | 3.8346 | 0.835 | 0.2631 | 0.8774 | 0.835 | 0.8110 | 0.1473 | 0.0564 | | No log | 30.0 | 210 | 3.8251 | 0.825 | 0.2549 | 0.9254 | 0.825 | 0.7979 | 0.1298 | 0.0541 | | No log | 31.0 | 217 | 3.8759 | 0.825 | 0.2671 | 0.9357 | 0.825 | 0.8014 | 0.1672 | 0.0609 | | No log | 32.0 | 224 | 3.8466 | 0.835 | 0.2567 | 1.0822 | 0.835 | 0.8168 | 0.1454 | 0.0553 | | No log | 33.0 | 231 | 3.8600 | 0.835 | 0.2578 | 1.0196 | 0.835 | 0.8103 | 0.1363 | 0.0577 | | No log | 34.0 | 238 | 3.8364 | 0.84 | 0.2615 | 0.9060 | 0.8400 | 0.8153 | 0.1556 | 0.0550 | | No log | 35.0 | 245 | 3.8615 | 0.84 | 0.2741 | 0.9777 | 0.8400 | 0.8175 | 0.1553 | 0.0609 | | No log | 36.0 | 252 | 3.8354 | 0.815 | 0.2672 | 1.0578 | 0.815 | 0.7829 | 0.1412 | 0.0571 | | No log | 37.0 | 259 | 3.8214 | 0.825 | 0.2586 | 1.1244 | 0.825 | 0.8005 | 0.1567 | 0.0555 | | No log | 38.0 | 266 | 3.8379 | 0.84 | 0.2557 | 0.9827 | 0.8400 | 0.8159 | 0.1623 | 0.0569 | | No log | 39.0 | 273 | 3.8269 | 0.82 | 0.2590 | 1.0025 | 0.82 | 0.7983 | 0.1300 | 0.0552 | | No log | 40.0 | 280 | 3.8326 | 0.835 | 0.2576 | 0.9914 | 0.835 | 0.8145 | 0.1339 | 0.0549 | | No log | 41.0 | 287 | 3.8171 | 0.845 | 0.2446 | 0.9369 | 0.845 | 0.8234 | 0.1345 | 0.0555 | | No log | 42.0 | 294 | 3.8197 | 0.835 | 0.2421 | 0.9189 | 0.835 | 0.8127 | 0.1263 | 0.0551 | | No log | 43.0 | 301 | 3.8182 | 0.835 | 0.2421 | 1.0651 | 0.835 | 0.8181 | 0.1348 | 0.0528 | | No log | 44.0 | 308 | 3.8461 | 0.85 | 0.2482 | 0.9057 | 0.85 | 0.8353 | 0.1580 | 0.0530 | | No log | 45.0 | 315 | 3.8203 | 0.845 | 0.2405 | 0.8504 | 0.845 | 0.8241 | 0.1461 | 0.0512 | | No log | 46.0 | 322 | 3.8468 | 0.845 | 0.2431 | 0.7909 | 0.845 | 0.8252 | 0.1273 | 0.0534 | | No log | 47.0 | 329 | 3.8798 | 0.84 | 0.2486 | 0.9810 | 0.8400 | 0.8219 | 0.1412 | 0.0565 | | No log | 48.0 | 336 | 3.8650 | 0.855 | 0.2447 | 0.9117 | 0.855 | 0.8372 | 0.1465 | 0.0525 | | No log | 49.0 | 343 | 3.8425 | 0.845 | 0.2414 | 1.0040 | 0.845 | 0.8231 | 0.1251 | 0.0536 | | No log | 50.0 | 350 | 3.8350 | 0.86 | 0.2351 | 0.8541 | 0.8600 | 0.8454 | 0.1403 | 0.0511 | | No log | 51.0 | 357 | 3.8572 | 0.84 | 0.2427 | 0.9366 | 0.8400 | 0.8197 | 0.1249 | 0.0527 | | No log | 52.0 | 364 | 3.8461 | 0.85 | 0.2374 | 0.8631 | 0.85 | 0.8352 | 0.1269 | 0.0506 | | No log | 53.0 | 371 | 3.8692 | 0.835 | 0.2431 | 0.9839 | 0.835 | 0.8110 | 0.1356 | 0.0546 | | No log | 54.0 | 378 | 3.8806 | 0.855 | 0.2403 | 1.0493 | 0.855 | 0.8343 | 0.1287 | 0.0530 | | No log | 55.0 | 385 | 3.8875 | 0.86 | 0.2389 | 0.9404 | 0.8600 | 0.8435 | 0.1416 | 0.0532 | | No log | 56.0 | 392 | 3.8893 | 0.84 | 0.2407 | 0.9410 | 0.8400 | 0.8189 | 0.1256 | 0.0539 | | No log | 57.0 | 399 | 3.8923 | 0.84 | 0.2408 | 0.9917 | 0.8400 | 0.8176 | 0.1274 | 0.0540 | | No log | 58.0 | 406 | 3.8865 | 0.86 | 0.2323 | 0.9745 | 0.8600 | 0.8444 | 0.1372 | 0.0517 | | No log | 59.0 | 413 | 3.9037 | 0.845 | 0.2363 | 0.9451 | 0.845 | 0.8230 | 0.1422 | 0.0533 | | No log | 60.0 | 420 | 3.9040 | 0.845 | 0.2396 | 1.0699 | 0.845 | 0.8233 | 0.1272 | 0.0522 | | No log | 61.0 | 427 | 3.9136 | 0.85 | 0.2378 | 1.0664 | 0.85 | 0.8289 | 0.1283 | 0.0527 | | No log | 62.0 | 434 | 3.8877 | 0.85 | 0.2355 | 0.8483 | 0.85 | 0.8315 | 0.1445 | 0.0519 | | No log | 63.0 | 441 | 3.8991 | 0.855 | 0.2342 | 0.9246 | 0.855 | 0.8374 | 0.1124 | 0.0509 | | No log | 64.0 | 448 | 3.9207 | 0.85 | 0.2383 | 1.0477 | 0.85 | 0.8330 | 0.1235 | 0.0514 | | No log | 65.0 | 455 | 3.9000 | 0.855 | 0.2362 | 1.0504 | 0.855 | 0.8384 | 0.1375 | 0.0529 | | No log | 66.0 | 462 | 3.9542 | 0.84 | 0.2463 | 0.9763 | 0.8400 | 0.8255 | 0.1405 | 0.0540 | | No log | 67.0 | 469 | 3.9153 | 0.855 | 0.2374 | 0.9954 | 0.855 | 0.8376 | 0.1353 | 0.0523 | | No log | 68.0 | 476 | 3.9264 | 0.845 | 0.2410 | 0.9917 | 0.845 | 0.8295 | 0.1081 | 0.0515 | | No log | 69.0 | 483 | 3.8989 | 0.86 | 0.2272 | 0.9322 | 0.8600 | 0.8438 | 0.1260 | 0.0492 | | No log | 70.0 | 490 | 3.9224 | 0.86 | 0.2329 | 0.9317 | 0.8600 | 0.8443 | 0.1091 | 0.0515 | | No log | 71.0 | 497 | 3.9313 | 0.85 | 0.2360 | 1.0424 | 0.85 | 0.8310 | 0.1259 | 0.0511 | | 3.5118 | 72.0 | 504 | 3.9407 | 0.85 | 0.2343 | 0.9333 | 0.85 | 0.8331 | 0.1156 | 0.0529 | | 3.5118 | 73.0 | 511 | 3.9407 | 0.865 | 0.2318 | 0.9791 | 0.865 | 0.8523 | 0.1245 | 0.0497 | | 3.5118 | 74.0 | 518 | 3.9461 | 0.855 | 0.2347 | 1.0488 | 0.855 | 0.8385 | 0.1298 | 0.0508 | | 3.5118 | 75.0 | 525 | 3.9560 | 0.86 | 0.2319 | 0.9924 | 0.8600 | 0.8444 | 0.1410 | 0.0504 | | 3.5118 | 76.0 | 532 | 3.9608 | 0.855 | 0.2317 | 0.9253 | 0.855 | 0.8390 | 0.1380 | 0.0517 | | 3.5118 | 77.0 | 539 | 3.9638 | 0.865 | 0.2319 | 0.9210 | 0.865 | 0.8528 | 0.1202 | 0.0504 | | 3.5118 | 78.0 | 546 | 3.9718 | 0.86 | 0.2323 | 0.9413 | 0.8600 | 0.8444 | 0.1255 | 0.0505 | | 3.5118 | 79.0 | 553 | 3.9778 | 0.86 | 0.2324 | 0.9916 | 0.8600 | 0.8444 | 0.1270 | 0.0506 | | 3.5118 | 80.0 | 560 | 3.9813 | 0.855 | 0.2323 | 0.9919 | 0.855 | 0.8390 | 0.1246 | 0.0509 | | 3.5118 | 81.0 | 567 | 3.9876 | 0.86 | 0.2319 | 0.9330 | 0.8600 | 0.8444 | 0.1318 | 0.0506 | | 3.5118 | 82.0 | 574 | 3.9939 | 0.855 | 0.2324 | 0.9328 | 0.855 | 0.8390 | 0.1280 | 0.0510 | | 3.5118 | 83.0 | 581 | 3.9971 | 0.86 | 0.2319 | 0.9321 | 0.8600 | 0.8444 | 0.1303 | 0.0503 | | 3.5118 | 84.0 | 588 | 4.0003 | 0.855 | 0.2316 | 0.9348 | 0.855 | 0.8390 | 0.1284 | 0.0508 | | 3.5118 | 85.0 | 595 | 4.0054 | 0.86 | 0.2319 | 0.9909 | 0.8600 | 0.8444 | 0.1348 | 0.0503 | | 3.5118 | 86.0 | 602 | 4.0086 | 0.86 | 0.2315 | 0.9338 | 0.8600 | 0.8444 | 0.1340 | 0.0504 | | 3.5118 | 87.0 | 609 | 4.0125 | 0.86 | 0.2318 | 0.9522 | 0.8600 | 0.8444 | 0.1348 | 0.0504 | | 3.5118 | 88.0 | 616 | 4.0148 | 0.86 | 0.2316 | 0.9396 | 0.8600 | 0.8444 | 0.1323 | 0.0504 | | 3.5118 | 89.0 | 623 | 4.0185 | 0.86 | 0.2318 | 0.9378 | 0.8600 | 0.8444 | 0.1326 | 0.0505 | | 3.5118 | 90.0 | 630 | 4.0197 | 0.86 | 0.2316 | 0.9412 | 0.8600 | 0.8444 | 0.1253 | 0.0506 | | 3.5118 | 91.0 | 637 | 4.0231 | 0.86 | 0.2318 | 0.9395 | 0.8600 | 0.8444 | 0.1165 | 0.0506 | | 3.5118 | 92.0 | 644 | 4.0249 | 0.86 | 0.2316 | 0.9921 | 0.8600 | 0.8444 | 0.1159 | 0.0504 | | 3.5118 | 93.0 | 651 | 4.0266 | 0.86 | 0.2316 | 0.9441 | 0.8600 | 0.8444 | 0.1161 | 0.0505 | | 3.5118 | 94.0 | 658 | 4.0275 | 0.86 | 0.2317 | 0.9934 | 0.8600 | 0.8444 | 0.1159 | 0.0504 | | 3.5118 | 95.0 | 665 | 4.0289 | 0.86 | 0.2315 | 0.9429 | 0.8600 | 0.8444 | 0.1160 | 0.0505 | | 3.5118 | 96.0 | 672 | 4.0301 | 0.86 | 0.2316 | 0.9932 | 0.8600 | 0.8444 | 0.1163 | 0.0505 | | 3.5118 | 97.0 | 679 | 4.0304 | 0.86 | 0.2315 | 0.9936 | 0.8600 | 0.8444 | 0.1163 | 0.0505 | | 3.5118 | 98.0 | 686 | 4.0313 | 0.86 | 0.2316 | 0.9935 | 0.8600 | 0.8444 | 0.1163 | 0.0504 | | 3.5118 | 99.0 | 693 | 4.0317 | 0.86 | 0.2316 | 0.9601 | 0.8600 | 0.8444 | 0.1162 | 0.0505 | | 3.5118 | 100.0 | 700 | 4.0317 | 0.86 | 0.2316 | 0.9681 | 0.8600 | 0.8444 | 0.1162 | 0.0505 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/81-tiny_tobacco3482_hint_

# 81-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 63.0326 - Accuracy: 0.85 - Brier Loss: 0.2647 - Nll: 1.1178 - F1 Micro: 0.85 - F1 Macro: 0.8409 - Ece: 0.1296 - Aurc: 0.0380 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 66.3150 | 0.26 | 0.8706 | 4.9002 | 0.26 | 0.1920 | 0.2904 | 0.7808 | | No log | 2.0 | 50 | 65.5312 | 0.54 | 0.5948 | 2.8544 | 0.54 | 0.4535 | 0.2938 | 0.2564 | | No log | 3.0 | 75 | 65.0613 | 0.675 | 0.4403 | 1.6101 | 0.675 | 0.6134 | 0.2276 | 0.1328 | | No log | 4.0 | 100 | 64.7775 | 0.765 | 0.3800 | 1.5512 | 0.765 | 0.7486 | 0.2394 | 0.1111 | | No log | 5.0 | 125 | 64.4695 | 0.8 | 0.3065 | 1.7477 | 0.8000 | 0.7658 | 0.1814 | 0.0713 | | No log | 6.0 | 150 | 64.3179 | 0.78 | 0.3286 | 1.7786 | 0.78 | 0.7409 | 0.1555 | 0.0636 | | No log | 7.0 | 175 | 64.0707 | 0.77 | 0.3516 | 1.6165 | 0.7700 | 0.7179 | 0.1677 | 0.0927 | | No log | 8.0 | 200 | 63.8878 | 0.775 | 0.3677 | 1.5868 | 0.775 | 0.7675 | 0.1922 | 0.0867 | | No log | 9.0 | 225 | 64.2141 | 0.715 | 0.4341 | 2.1772 | 0.715 | 0.7074 | 0.2273 | 0.1130 | | No log | 10.0 | 250 | 63.8037 | 0.775 | 0.3649 | 1.4471 | 0.775 | 0.7221 | 0.1744 | 0.0766 | | No log | 11.0 | 275 | 64.2412 | 0.655 | 0.5396 | 2.0448 | 0.655 | 0.6532 | 0.2692 | 0.1670 | | No log | 12.0 | 300 | 63.8514 | 0.73 | 0.4372 | 1.9358 | 0.7300 | 0.6997 | 0.2194 | 0.1008 | | No log | 13.0 | 325 | 63.4352 | 0.815 | 0.2888 | 1.6462 | 0.815 | 0.7858 | 0.1490 | 0.0469 | | No log | 14.0 | 350 | 63.5101 | 0.795 | 0.3262 | 1.3794 | 0.795 | 0.7816 | 0.1599 | 0.0648 | | No log | 15.0 | 375 | 63.6365 | 0.785 | 0.3704 | 1.4766 | 0.785 | 0.7627 | 0.1691 | 0.0781 | | No log | 16.0 | 400 | 63.7245 | 0.73 | 0.4145 | 1.7447 | 0.7300 | 0.7261 | 0.2087 | 0.0917 | | No log | 17.0 | 425 | 63.4312 | 0.795 | 0.3148 | 1.4363 | 0.795 | 0.7868 | 0.1665 | 0.0636 | | No log | 18.0 | 450 | 63.7070 | 0.835 | 0.2915 | 1.4455 | 0.835 | 0.8078 | 0.1542 | 0.0570 | | No log | 19.0 | 475 | 63.3600 | 0.81 | 0.2997 | 1.3326 | 0.81 | 0.7878 | 0.1602 | 0.0534 | | 62.5692 | 20.0 | 500 | 63.4339 | 0.81 | 0.3158 | 1.2051 | 0.81 | 0.7809 | 0.1541 | 0.0506 | | 62.5692 | 21.0 | 525 | 63.3477 | 0.805 | 0.3088 | 1.2575 | 0.805 | 0.7943 | 0.1570 | 0.0489 | | 62.5692 | 22.0 | 550 | 63.3362 | 0.83 | 0.2911 | 1.3409 | 0.83 | 0.8104 | 0.1440 | 0.0514 | | 62.5692 | 23.0 | 575 | 63.3897 | 0.805 | 0.3004 | 1.2505 | 0.805 | 0.7833 | 0.1595 | 0.0453 | | 62.5692 | 24.0 | 600 | 63.3475 | 0.8 | 0.3185 | 1.1190 | 0.8000 | 0.7750 | 0.1626 | 0.0486 | | 62.5692 | 25.0 | 625 | 63.4552 | 0.805 | 0.3470 | 1.2483 | 0.805 | 0.7904 | 0.1818 | 0.0652 | | 62.5692 | 26.0 | 650 | 63.4364 | 0.79 | 0.3453 | 1.1298 | 0.79 | 0.7798 | 0.1827 | 0.0651 | | 62.5692 | 27.0 | 675 | 63.3001 | 0.83 | 0.2899 | 1.4329 | 0.83 | 0.8141 | 0.1370 | 0.0466 | | 62.5692 | 28.0 | 700 | 63.1848 | 0.85 | 0.2514 | 1.2175 | 0.85 | 0.8354 | 0.1220 | 0.0396 | | 62.5692 | 29.0 | 725 | 63.2303 | 0.835 | 0.2744 | 1.4886 | 0.835 | 0.8143 | 0.1389 | 0.0532 | | 62.5692 | 30.0 | 750 | 63.2275 | 0.84 | 0.2774 | 1.0426 | 0.8400 | 0.8405 | 0.1325 | 0.0632 | | 62.5692 | 31.0 | 775 | 63.1341 | 0.835 | 0.2597 | 1.1066 | 0.835 | 0.8083 | 0.1170 | 0.0510 | | 62.5692 | 32.0 | 800 | 63.2045 | 0.81 | 0.2970 | 1.0432 | 0.81 | 0.8028 | 0.1561 | 0.0552 | | 62.5692 | 33.0 | 825 | 63.1898 | 0.82 | 0.2952 | 1.0189 | 0.82 | 0.8060 | 0.1440 | 0.0521 | | 62.5692 | 34.0 | 850 | 63.1330 | 0.835 | 0.2760 | 1.0095 | 0.835 | 0.8265 | 0.1356 | 0.0542 | | 62.5692 | 35.0 | 875 | 63.1572 | 0.84 | 0.2834 | 1.0174 | 0.8400 | 0.8234 | 0.1337 | 0.0508 | | 62.5692 | 36.0 | 900 | 63.1922 | 0.835 | 0.2894 | 1.0102 | 0.835 | 0.8233 | 0.1469 | 0.0500 | | 62.5692 | 37.0 | 925 | 63.1305 | 0.83 | 0.2818 | 1.0146 | 0.83 | 0.8172 | 0.1387 | 0.0510 | | 62.5692 | 38.0 | 950 | 63.1902 | 0.815 | 0.2865 | 1.0101 | 0.815 | 0.8016 | 0.1500 | 0.0516 | | 62.5692 | 39.0 | 975 | 63.1835 | 0.825 | 0.2851 | 1.0177 | 0.825 | 0.8162 | 0.1436 | 0.0496 | | 61.2333 | 40.0 | 1000 | 63.1741 | 0.84 | 0.2783 | 1.0160 | 0.8400 | 0.8266 | 0.1275 | 0.0510 | | 61.2333 | 41.0 | 1025 | 63.1755 | 0.835 | 0.2756 | 1.0117 | 0.835 | 0.8192 | 0.1447 | 0.0483 | | 61.2333 | 42.0 | 1050 | 63.1281 | 0.83 | 0.2820 | 1.0142 | 0.83 | 0.8169 | 0.1415 | 0.0466 | | 61.2333 | 43.0 | 1075 | 63.1697 | 0.85 | 0.2675 | 0.9929 | 0.85 | 0.8358 | 0.1423 | 0.0484 | | 61.2333 | 44.0 | 1100 | 63.1141 | 0.835 | 0.2767 | 1.0005 | 0.835 | 0.8237 | 0.1293 | 0.0481 | | 61.2333 | 45.0 | 1125 | 63.1441 | 0.85 | 0.2638 | 1.0023 | 0.85 | 0.8383 | 0.1335 | 0.0471 | | 61.2333 | 46.0 | 1150 | 63.1221 | 0.84 | 0.2745 | 0.9981 | 0.8400 | 0.8308 | 0.1271 | 0.0451 | | 61.2333 | 47.0 | 1175 | 63.1140 | 0.845 | 0.2654 | 0.9891 | 0.845 | 0.8317 | 0.1351 | 0.0458 | | 61.2333 | 48.0 | 1200 | 63.1056 | 0.845 | 0.2654 | 1.0016 | 0.845 | 0.8351 | 0.1364 | 0.0458 | | 61.2333 | 49.0 | 1225 | 63.0906 | 0.83 | 0.2713 | 1.0042 | 0.83 | 0.8221 | 0.1455 | 0.0449 | | 61.2333 | 50.0 | 1250 | 63.0942 | 0.835 | 0.2633 | 1.0003 | 0.835 | 0.8314 | 0.1397 | 0.0452 | | 61.2333 | 51.0 | 1275 | 63.0929 | 0.84 | 0.2641 | 0.9957 | 0.8400 | 0.8359 | 0.1340 | 0.0440 | | 61.2333 | 52.0 | 1300 | 63.0913 | 0.83 | 0.2646 | 1.0040 | 0.83 | 0.8242 | 0.1422 | 0.0440 | | 61.2333 | 53.0 | 1325 | 63.1152 | 0.83 | 0.2754 | 0.9985 | 0.83 | 0.8250 | 0.1416 | 0.0447 | | 61.2333 | 54.0 | 1350 | 63.0923 | 0.835 | 0.2649 | 0.9997 | 0.835 | 0.8278 | 0.1356 | 0.0426 | | 61.2333 | 55.0 | 1375 | 63.0720 | 0.83 | 0.2686 | 0.9988 | 0.83 | 0.8243 | 0.1396 | 0.0431 | | 61.2333 | 56.0 | 1400 | 63.0627 | 0.83 | 0.2636 | 1.0713 | 0.83 | 0.8243 | 0.1369 | 0.0427 | | 61.2333 | 57.0 | 1425 | 63.0742 | 0.835 | 0.2692 | 1.0572 | 0.835 | 0.8305 | 0.1391 | 0.0425 | | 61.2333 | 58.0 | 1450 | 63.0910 | 0.84 | 0.2639 | 1.0727 | 0.8400 | 0.8334 | 0.1320 | 0.0432 | | 61.2333 | 59.0 | 1475 | 63.1015 | 0.84 | 0.2648 | 1.1382 | 0.8400 | 0.8354 | 0.1331 | 0.0423 | | 61.0482 | 60.0 | 1500 | 63.0557 | 0.835 | 0.2655 | 1.0688 | 0.835 | 0.8293 | 0.1333 | 0.0420 | | 61.0482 | 61.0 | 1525 | 63.0590 | 0.835 | 0.2655 | 1.1378 | 0.835 | 0.8315 | 0.1425 | 0.0416 | | 61.0482 | 62.0 | 1550 | 63.0732 | 0.845 | 0.2661 | 1.0565 | 0.845 | 0.8381 | 0.1404 | 0.0413 | | 61.0482 | 63.0 | 1575 | 63.0972 | 0.855 | 0.2659 | 1.1274 | 0.855 | 0.8501 | 0.1424 | 0.0416 | | 61.0482 | 64.0 | 1600 | 63.0528 | 0.84 | 0.2694 | 1.1315 | 0.8400 | 0.8330 | 0.1355 | 0.0418 | | 61.0482 | 65.0 | 1625 | 63.0625 | 0.835 | 0.2683 | 1.1336 | 0.835 | 0.8281 | 0.1373 | 0.0411 | | 61.0482 | 66.0 | 1650 | 63.0512 | 0.835 | 0.2747 | 1.1250 | 0.835 | 0.8242 | 0.1436 | 0.0410 | | 61.0482 | 67.0 | 1675 | 63.0634 | 0.85 | 0.2671 | 1.1270 | 0.85 | 0.8397 | 0.1376 | 0.0419 | | 61.0482 | 68.0 | 1700 | 63.0609 | 0.835 | 0.2717 | 1.1311 | 0.835 | 0.8295 | 0.1365 | 0.0411 | | 61.0482 | 69.0 | 1725 | 63.0513 | 0.835 | 0.2707 | 1.1261 | 0.835 | 0.8223 | 0.1461 | 0.0412 | | 61.0482 | 70.0 | 1750 | 63.0510 | 0.845 | 0.2712 | 1.1219 | 0.845 | 0.8396 | 0.1369 | 0.0411 | | 61.0482 | 71.0 | 1775 | 63.0530 | 0.845 | 0.2688 | 1.1244 | 0.845 | 0.8364 | 0.1403 | 0.0412 | | 61.0482 | 72.0 | 1800 | 63.0456 | 0.84 | 0.2665 | 1.1204 | 0.8400 | 0.8293 | 0.1341 | 0.0400 | | 61.0482 | 73.0 | 1825 | 63.0459 | 0.845 | 0.2680 | 1.1244 | 0.845 | 0.8360 | 0.1430 | 0.0398 | | 61.0482 | 74.0 | 1850 | 63.0773 | 0.85 | 0.2684 | 1.1291 | 0.85 | 0.8440 | 0.1386 | 0.0410 | | 61.0482 | 75.0 | 1875 | 63.0497 | 0.85 | 0.2664 | 1.1285 | 0.85 | 0.8388 | 0.1293 | 0.0392 | | 61.0482 | 76.0 | 1900 | 63.0483 | 0.845 | 0.2695 | 1.1256 | 0.845 | 0.8352 | 0.1440 | 0.0409 | | 61.0482 | 77.0 | 1925 | 63.0352 | 0.845 | 0.2680 | 1.1229 | 0.845 | 0.8352 | 0.1420 | 0.0398 | | 61.0482 | 78.0 | 1950 | 63.0291 | 0.845 | 0.2701 | 1.1225 | 0.845 | 0.8352 | 0.1342 | 0.0394 | | 61.0482 | 79.0 | 1975 | 63.0508 | 0.85 | 0.2695 | 1.1224 | 0.85 | 0.8388 | 0.1418 | 0.0399 | | 60.9704 | 80.0 | 2000 | 63.0510 | 0.85 | 0.2708 | 1.1169 | 0.85 | 0.8388 | 0.1376 | 0.0394 | | 60.9704 | 81.0 | 2025 | 63.0460 | 0.85 | 0.2648 | 1.1205 | 0.85 | 0.8440 | 0.1357 | 0.0397 | | 60.9704 | 82.0 | 2050 | 63.0505 | 0.845 | 0.2697 | 1.1148 | 0.845 | 0.8352 | 0.1464 | 0.0392 | | 60.9704 | 83.0 | 2075 | 63.0425 | 0.845 | 0.2651 | 1.1229 | 0.845 | 0.8352 | 0.1396 | 0.0389 | | 60.9704 | 84.0 | 2100 | 63.0398 | 0.845 | 0.2664 | 1.1197 | 0.845 | 0.8337 | 0.1330 | 0.0388 | | 60.9704 | 85.0 | 2125 | 63.0355 | 0.845 | 0.2667 | 1.1192 | 0.845 | 0.8360 | 0.1307 | 0.0387 | | 60.9704 | 86.0 | 2150 | 63.0386 | 0.85 | 0.2649 | 1.1223 | 0.85 | 0.8409 | 0.1279 | 0.0379 | | 60.9704 | 87.0 | 2175 | 63.0405 | 0.85 | 0.2642 | 1.1218 | 0.85 | 0.8409 | 0.1437 | 0.0378 | | 60.9704 | 88.0 | 2200 | 63.0363 | 0.85 | 0.2667 | 1.1165 | 0.85 | 0.8388 | 0.1320 | 0.0390 | | 60.9704 | 89.0 | 2225 | 63.0456 | 0.845 | 0.2644 | 1.1180 | 0.845 | 0.8352 | 0.1354 | 0.0381 | | 60.9704 | 90.0 | 2250 | 63.0343 | 0.845 | 0.2656 | 1.1159 | 0.845 | 0.8337 | 0.1390 | 0.0385 | | 60.9704 | 91.0 | 2275 | 63.0391 | 0.85 | 0.2654 | 1.1194 | 0.85 | 0.8409 | 0.1389 | 0.0380 | | 60.9704 | 92.0 | 2300 | 63.0354 | 0.85 | 0.2665 | 1.1203 | 0.85 | 0.8409 | 0.1419 | 0.0377 | | 60.9704 | 93.0 | 2325 | 63.0272 | 0.845 | 0.2650 | 1.1166 | 0.845 | 0.8381 | 0.1370 | 0.0381 | | 60.9704 | 94.0 | 2350 | 63.0313 | 0.85 | 0.2647 | 1.1181 | 0.85 | 0.8409 | 0.1322 | 0.0380 | | 60.9704 | 95.0 | 2375 | 63.0220 | 0.845 | 0.2658 | 1.1197 | 0.845 | 0.8357 | 0.1311 | 0.0378 | | 60.9704 | 96.0 | 2400 | 63.0345 | 0.85 | 0.2639 | 1.1179 | 0.85 | 0.8409 | 0.1284 | 0.0381 | | 60.9704 | 97.0 | 2425 | 63.0330 | 0.845 | 0.2651 | 1.1163 | 0.845 | 0.8352 | 0.1391 | 0.0382 | | 60.9704 | 98.0 | 2450 | 63.0302 | 0.85 | 0.2646 | 1.1182 | 0.85 | 0.8409 | 0.1311 | 0.0380 | | 60.9704 | 99.0 | 2475 | 63.0287 | 0.85 | 0.2646 | 1.1175 | 0.85 | 0.8409 | 0.1250 | 0.0380 | | 60.9392 | 100.0 | 2500 | 63.0326 | 0.85 | 0.2647 | 1.1178 | 0.85 | 0.8409 | 0.1296 | 0.0380 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/60-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 60-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4631 - Accuracy: 0.81 - Brier Loss: 0.3427 - Nll: 0.9896 - F1 Micro: 0.81 - F1 Macro: 0.7794 - Ece: 0.3002 - Aurc: 0.0534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.0115 | 0.1 | 1.0070 | 8.1872 | 0.1000 | 0.0765 | 0.3197 | 0.8958 | | No log | 2.0 | 14 | 1.3480 | 0.19 | 0.8667 | 5.2268 | 0.19 | 0.1599 | 0.2694 | 0.7716 | | No log | 3.0 | 21 | 1.0745 | 0.38 | 0.7608 | 4.8555 | 0.38 | 0.3111 | 0.2804 | 0.4226 | | No log | 4.0 | 28 | 0.8841 | 0.57 | 0.6204 | 2.8740 | 0.57 | 0.4526 | 0.2904 | 0.2536 | | No log | 5.0 | 35 | 0.7962 | 0.63 | 0.5646 | 2.4082 | 0.63 | 0.5462 | 0.3178 | 0.2005 | | No log | 6.0 | 42 | 0.7065 | 0.68 | 0.4894 | 2.3364 | 0.68 | 0.5773 | 0.2660 | 0.1461 | | No log | 7.0 | 49 | 0.6639 | 0.7 | 0.4548 | 1.8260 | 0.7 | 0.6412 | 0.2748 | 0.1340 | | No log | 8.0 | 56 | 0.6374 | 0.745 | 0.4265 | 1.8720 | 0.745 | 0.6620 | 0.3127 | 0.1047 | | No log | 9.0 | 63 | 0.6184 | 0.73 | 0.4136 | 1.7957 | 0.7300 | 0.6650 | 0.3022 | 0.0845 | | No log | 10.0 | 70 | 0.5569 | 0.8 | 0.3838 | 1.2127 | 0.8000 | 0.7683 | 0.3047 | 0.0721 | | No log | 11.0 | 77 | 0.5225 | 0.815 | 0.3514 | 1.0539 | 0.815 | 0.7871 | 0.2818 | 0.0625 | | No log | 12.0 | 84 | 0.5330 | 0.795 | 0.3609 | 1.0544 | 0.795 | 0.7695 | 0.2723 | 0.0786 | | No log | 13.0 | 91 | 0.5241 | 0.78 | 0.3615 | 1.1338 | 0.78 | 0.7599 | 0.2681 | 0.0726 | | No log | 14.0 | 98 | 0.5345 | 0.795 | 0.3640 | 1.1229 | 0.795 | 0.7527 | 0.2950 | 0.0661 | | No log | 15.0 | 105 | 0.5042 | 0.8 | 0.3459 | 1.1690 | 0.8000 | 0.7664 | 0.2915 | 0.0664 | | No log | 16.0 | 112 | 0.5285 | 0.795 | 0.3779 | 1.1973 | 0.795 | 0.7468 | 0.3056 | 0.0647 | | No log | 17.0 | 119 | 0.5374 | 0.77 | 0.3716 | 1.5324 | 0.7700 | 0.7406 | 0.2658 | 0.0910 | | No log | 18.0 | 126 | 0.5132 | 0.79 | 0.3677 | 1.1252 | 0.79 | 0.7699 | 0.2842 | 0.0722 | | No log | 19.0 | 133 | 0.4961 | 0.815 | 0.3558 | 0.8880 | 0.815 | 0.7941 | 0.2985 | 0.0611 | | No log | 20.0 | 140 | 0.4971 | 0.82 | 0.3556 | 1.0845 | 0.82 | 0.8049 | 0.3045 | 0.0631 | | No log | 21.0 | 147 | 0.4791 | 0.835 | 0.3328 | 0.9109 | 0.835 | 0.8168 | 0.2803 | 0.0534 | | No log | 22.0 | 154 | 0.4953 | 0.805 | 0.3531 | 1.0087 | 0.805 | 0.7758 | 0.3015 | 0.0642 | | No log | 23.0 | 161 | 0.4962 | 0.825 | 0.3543 | 0.8886 | 0.825 | 0.8091 | 0.3085 | 0.0672 | | No log | 24.0 | 168 | 0.4753 | 0.82 | 0.3435 | 1.0609 | 0.82 | 0.8025 | 0.2994 | 0.0508 | | No log | 25.0 | 175 | 0.4856 | 0.825 | 0.3495 | 1.1336 | 0.825 | 0.7846 | 0.3018 | 0.0539 | | No log | 26.0 | 182 | 0.4824 | 0.805 | 0.3499 | 1.0142 | 0.805 | 0.7890 | 0.3000 | 0.0577 | | No log | 27.0 | 189 | 0.4843 | 0.83 | 0.3506 | 0.7550 | 0.83 | 0.8090 | 0.3163 | 0.0556 | | No log | 28.0 | 196 | 0.4728 | 0.825 | 0.3451 | 1.1111 | 0.825 | 0.8012 | 0.3006 | 0.0554 | | No log | 29.0 | 203 | 0.4844 | 0.8 | 0.3483 | 1.2220 | 0.8000 | 0.7750 | 0.2913 | 0.0609 | | No log | 30.0 | 210 | 0.4728 | 0.815 | 0.3478 | 1.1306 | 0.815 | 0.7840 | 0.3051 | 0.0540 | | No log | 31.0 | 217 | 0.4694 | 0.83 | 0.3411 | 0.8267 | 0.83 | 0.8156 | 0.3101 | 0.0474 | | No log | 32.0 | 224 | 0.4685 | 0.82 | 0.3406 | 1.0564 | 0.82 | 0.7909 | 0.3269 | 0.0529 | | No log | 33.0 | 231 | 0.4692 | 0.81 | 0.3407 | 0.8508 | 0.81 | 0.7900 | 0.2838 | 0.0517 | | No log | 34.0 | 238 | 0.4647 | 0.815 | 0.3404 | 1.0430 | 0.815 | 0.7894 | 0.3098 | 0.0563 | | No log | 35.0 | 245 | 0.4761 | 0.795 | 0.3503 | 1.0340 | 0.795 | 0.7687 | 0.2731 | 0.0594 | | No log | 36.0 | 252 | 0.4802 | 0.83 | 0.3571 | 0.8575 | 0.83 | 0.8046 | 0.3275 | 0.0511 | | No log | 37.0 | 259 | 0.4686 | 0.8 | 0.3414 | 0.8818 | 0.8000 | 0.7836 | 0.3020 | 0.0560 | | No log | 38.0 | 266 | 0.4612 | 0.815 | 0.3361 | 0.9901 | 0.815 | 0.7807 | 0.2988 | 0.0528 | | No log | 39.0 | 273 | 0.4721 | 0.81 | 0.3475 | 0.9803 | 0.81 | 0.7875 | 0.3019 | 0.0576 | | No log | 40.0 | 280 | 0.4646 | 0.83 | 0.3425 | 0.8495 | 0.83 | 0.8059 | 0.3063 | 0.0507 | | No log | 41.0 | 287 | 0.4622 | 0.805 | 0.3396 | 1.0087 | 0.805 | 0.7688 | 0.2903 | 0.0558 | | No log | 42.0 | 294 | 0.4599 | 0.82 | 0.3375 | 0.9166 | 0.82 | 0.7996 | 0.3319 | 0.0533 | | No log | 43.0 | 301 | 0.4688 | 0.805 | 0.3475 | 1.0195 | 0.805 | 0.7771 | 0.2994 | 0.0573 | | No log | 44.0 | 308 | 0.4652 | 0.805 | 0.3437 | 0.9095 | 0.805 | 0.7809 | 0.3134 | 0.0545 | | No log | 45.0 | 315 | 0.4641 | 0.81 | 0.3418 | 0.8922 | 0.81 | 0.7806 | 0.3014 | 0.0550 | | No log | 46.0 | 322 | 0.4579 | 0.825 | 0.3361 | 0.9516 | 0.825 | 0.7956 | 0.2971 | 0.0490 | | No log | 47.0 | 329 | 0.4638 | 0.82 | 0.3423 | 0.8830 | 0.82 | 0.7961 | 0.3259 | 0.0557 | | No log | 48.0 | 336 | 0.4643 | 0.81 | 0.3434 | 1.0046 | 0.81 | 0.7789 | 0.3042 | 0.0541 | | No log | 49.0 | 343 | 0.4596 | 0.81 | 0.3388 | 0.9862 | 0.81 | 0.7835 | 0.3170 | 0.0532 | | No log | 50.0 | 350 | 0.4603 | 0.815 | 0.3399 | 0.9288 | 0.815 | 0.7963 | 0.3031 | 0.0533 | | No log | 51.0 | 357 | 0.4610 | 0.815 | 0.3403 | 0.9900 | 0.815 | 0.7898 | 0.3306 | 0.0546 | | No log | 52.0 | 364 | 0.4617 | 0.81 | 0.3412 | 0.9834 | 0.81 | 0.7793 | 0.3079 | 0.0533 | | No log | 53.0 | 371 | 0.4627 | 0.815 | 0.3423 | 0.9901 | 0.815 | 0.7898 | 0.3023 | 0.0543 | | No log | 54.0 | 378 | 0.4612 | 0.815 | 0.3415 | 0.9868 | 0.815 | 0.7962 | 0.3178 | 0.0534 | | No log | 55.0 | 385 | 0.4617 | 0.815 | 0.3416 | 0.9904 | 0.815 | 0.7898 | 0.3117 | 0.0533 | | No log | 56.0 | 392 | 0.4605 | 0.81 | 0.3399 | 0.9845 | 0.81 | 0.7793 | 0.3069 | 0.0535 | | No log | 57.0 | 399 | 0.4606 | 0.81 | 0.3405 | 0.9818 | 0.81 | 0.7793 | 0.3045 | 0.0531 | | No log | 58.0 | 406 | 0.4614 | 0.81 | 0.3413 | 0.9853 | 0.81 | 0.7793 | 0.3114 | 0.0537 | | No log | 59.0 | 413 | 0.4623 | 0.81 | 0.3424 | 0.9848 | 0.81 | 0.7793 | 0.3045 | 0.0534 | | No log | 60.0 | 420 | 0.4621 | 0.81 | 0.3421 | 0.9898 | 0.81 | 0.7863 | 0.3150 | 0.0536 | | No log | 61.0 | 427 | 0.4620 | 0.81 | 0.3417 | 0.9868 | 0.81 | 0.7793 | 0.3060 | 0.0534 | | No log | 62.0 | 434 | 0.4618 | 0.81 | 0.3413 | 0.9843 | 0.81 | 0.7793 | 0.3029 | 0.0533 | | No log | 63.0 | 441 | 0.4622 | 0.81 | 0.3419 | 0.9868 | 0.81 | 0.7793 | 0.2969 | 0.0535 | | No log | 64.0 | 448 | 0.4621 | 0.81 | 0.3419 | 0.9881 | 0.81 | 0.7793 | 0.3070 | 0.0542 | | No log | 65.0 | 455 | 0.4625 | 0.81 | 0.3422 | 0.9871 | 0.81 | 0.7794 | 0.3131 | 0.0532 | | No log | 66.0 | 462 | 0.4626 | 0.81 | 0.3423 | 0.9880 | 0.81 | 0.7794 | 0.3066 | 0.0533 | | No log | 67.0 | 469 | 0.4621 | 0.81 | 0.3420 | 0.9872 | 0.81 | 0.7793 | 0.3066 | 0.0536 | | No log | 68.0 | 476 | 0.4624 | 0.81 | 0.3421 | 0.9882 | 0.81 | 0.7794 | 0.2966 | 0.0533 | | No log | 69.0 | 483 | 0.4627 | 0.81 | 0.3425 | 0.9891 | 0.81 | 0.7794 | 0.3160 | 0.0534 | | No log | 70.0 | 490 | 0.4628 | 0.81 | 0.3424 | 0.9899 | 0.81 | 0.7794 | 0.2970 | 0.0533 | | No log | 71.0 | 497 | 0.4627 | 0.81 | 0.3423 | 0.9890 | 0.81 | 0.7794 | 0.2968 | 0.0532 | | 0.3139 | 72.0 | 504 | 0.4625 | 0.81 | 0.3423 | 0.9886 | 0.81 | 0.7794 | 0.2971 | 0.0534 | | 0.3139 | 73.0 | 511 | 0.4625 | 0.81 | 0.3423 | 0.9892 | 0.81 | 0.7794 | 0.3043 | 0.0535 | | 0.3139 | 74.0 | 518 | 0.4626 | 0.81 | 0.3422 | 0.9881 | 0.81 | 0.7794 | 0.2969 | 0.0533 | | 0.3139 | 75.0 | 525 | 0.4631 | 0.81 | 0.3428 | 0.9896 | 0.81 | 0.7794 | 0.3142 | 0.0533 | | 0.3139 | 76.0 | 532 | 0.4628 | 0.81 | 0.3425 | 0.9893 | 0.81 | 0.7794 | 0.3138 | 0.0532 | | 0.3139 | 77.0 | 539 | 0.4627 | 0.81 | 0.3423 | 0.9889 | 0.81 | 0.7794 | 0.3040 | 0.0533 | | 0.3139 | 78.0 | 546 | 0.4628 | 0.81 | 0.3425 | 0.9888 | 0.81 | 0.7794 | 0.3138 | 0.0533 | | 0.3139 | 79.0 | 553 | 0.4629 | 0.81 | 0.3426 | 0.9898 | 0.81 | 0.7794 | 0.3002 | 0.0535 | | 0.3139 | 80.0 | 560 | 0.4630 | 0.81 | 0.3426 | 0.9892 | 0.81 | 0.7794 | 0.3041 | 0.0534 | | 0.3139 | 81.0 | 567 | 0.4631 | 0.81 | 0.3428 | 0.9899 | 0.81 | 0.7794 | 0.3042 | 0.0534 | | 0.3139 | 82.0 | 574 | 0.4628 | 0.81 | 0.3424 | 0.9889 | 0.81 | 0.7794 | 0.3039 | 0.0532 | | 0.3139 | 83.0 | 581 | 0.4630 | 0.81 | 0.3427 | 0.9893 | 0.81 | 0.7794 | 0.3068 | 0.0533 | | 0.3139 | 84.0 | 588 | 0.4629 | 0.81 | 0.3426 | 0.9894 | 0.81 | 0.7794 | 0.3069 | 0.0534 | | 0.3139 | 85.0 | 595 | 0.4629 | 0.81 | 0.3425 | 0.9893 | 0.81 | 0.7794 | 0.3138 | 0.0535 | | 0.3139 | 86.0 | 602 | 0.4630 | 0.81 | 0.3427 | 0.9896 | 0.81 | 0.7794 | 0.3070 | 0.0533 | | 0.3139 | 87.0 | 609 | 0.4630 | 0.81 | 0.3426 | 0.9890 | 0.81 | 0.7794 | 0.3069 | 0.0534 | | 0.3139 | 88.0 | 616 | 0.4630 | 0.81 | 0.3426 | 0.9893 | 0.81 | 0.7794 | 0.3069 | 0.0533 | | 0.3139 | 89.0 | 623 | 0.4630 | 0.81 | 0.3426 | 0.9897 | 0.81 | 0.7794 | 0.3001 | 0.0535 | | 0.3139 | 90.0 | 630 | 0.4631 | 0.81 | 0.3428 | 0.9902 | 0.81 | 0.7794 | 0.2904 | 0.0534 | | 0.3139 | 91.0 | 637 | 0.4631 | 0.81 | 0.3427 | 0.9892 | 0.81 | 0.7794 | 0.3139 | 0.0533 | | 0.3139 | 92.0 | 644 | 0.4631 | 0.81 | 0.3427 | 0.9894 | 0.81 | 0.7794 | 0.3071 | 0.0535 | | 0.3139 | 93.0 | 651 | 0.4631 | 0.81 | 0.3428 | 0.9899 | 0.81 | 0.7794 | 0.3001 | 0.0534 | | 0.3139 | 94.0 | 658 | 0.4630 | 0.81 | 0.3427 | 0.9894 | 0.81 | 0.7794 | 0.3069 | 0.0534 | | 0.3139 | 95.0 | 665 | 0.4631 | 0.81 | 0.3428 | 0.9896 | 0.81 | 0.7794 | 0.3071 | 0.0534 | | 0.3139 | 96.0 | 672 | 0.4630 | 0.81 | 0.3427 | 0.9893 | 0.81 | 0.7794 | 0.3070 | 0.0534 | | 0.3139 | 97.0 | 679 | 0.4631 | 0.81 | 0.3427 | 0.9895 | 0.81 | 0.7794 | 0.3002 | 0.0535 | | 0.3139 | 98.0 | 686 | 0.4631 | 0.81 | 0.3428 | 0.9899 | 0.81 | 0.7794 | 0.3002 | 0.0534 | | 0.3139 | 99.0 | 693 | 0.4631 | 0.81 | 0.3427 | 0.9897 | 0.81 | 0.7794 | 0.3002 | 0.0534 | | 0.3139 | 100.0 | 700 | 0.4631 | 0.81 | 0.3427 | 0.9896 | 0.81 | 0.7794 | 0.3002 | 0.0534 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

Sanfee18/my_awesome_food_model

# my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6056 - Accuracy: 0.901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6911 | 0.99 | 62 | 2.5097 | 0.856 | | 1.8202 | 2.0 | 125 | 1.7735 | 0.894 | | 1.5694 | 2.98 | 186 | 1.6056 | 0.901 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

jordyvl/60-tiny_tobacco3482

# 60-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1098 - Accuracy: 0.79 - Brier Loss: 0.4604 - Nll: 0.9058 - F1 Micro: 0.79 - F1 Macro: 0.7539 - Ece: 0.4083 - Aurc: 0.0644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.9988 | 0.16 | 1.0265 | 8.4697 | 0.16 | 0.0543 | 0.3527 | 0.8493 | | No log | 2.0 | 14 | 0.9241 | 0.1 | 0.9095 | 7.2634 | 0.1000 | 0.0853 | 0.2311 | 0.8707 | | No log | 3.0 | 21 | 0.5325 | 0.29 | 0.8525 | 5.4845 | 0.29 | 0.1287 | 0.2809 | 0.6344 | | No log | 4.0 | 28 | 0.4050 | 0.36 | 0.7891 | 3.8464 | 0.36 | 0.2714 | 0.2948 | 0.4598 | | No log | 5.0 | 35 | 0.3282 | 0.495 | 0.7189 | 2.5837 | 0.495 | 0.3741 | 0.3317 | 0.3367 | | No log | 6.0 | 42 | 0.2722 | 0.585 | 0.6827 | 2.2625 | 0.585 | 0.4818 | 0.3751 | 0.2241 | | No log | 7.0 | 49 | 0.2426 | 0.605 | 0.6424 | 2.0945 | 0.605 | 0.4641 | 0.3516 | 0.2034 | | No log | 8.0 | 56 | 0.2223 | 0.685 | 0.6171 | 1.9679 | 0.685 | 0.5711 | 0.4148 | 0.1545 | | No log | 9.0 | 63 | 0.1939 | 0.67 | 0.5684 | 1.8320 | 0.67 | 0.5495 | 0.3460 | 0.1438 | | No log | 10.0 | 70 | 0.1950 | 0.695 | 0.5454 | 1.3666 | 0.695 | 0.5750 | 0.3667 | 0.1276 | | No log | 11.0 | 77 | 0.1838 | 0.69 | 0.5436 | 1.4378 | 0.69 | 0.5745 | 0.3846 | 0.1162 | | No log | 12.0 | 84 | 0.1787 | 0.735 | 0.5138 | 1.4550 | 0.735 | 0.6490 | 0.3893 | 0.0949 | | No log | 13.0 | 91 | 0.1682 | 0.74 | 0.5209 | 1.6406 | 0.74 | 0.6452 | 0.3904 | 0.1052 | | No log | 14.0 | 98 | 0.1734 | 0.75 | 0.5420 | 1.4280 | 0.75 | 0.6656 | 0.4440 | 0.0864 | | No log | 15.0 | 105 | 0.1404 | 0.725 | 0.4961 | 1.3478 | 0.7250 | 0.6343 | 0.3816 | 0.0895 | | No log | 16.0 | 112 | 0.1439 | 0.76 | 0.4709 | 1.4366 | 0.76 | 0.6679 | 0.3721 | 0.0754 | | No log | 17.0 | 119 | 0.1356 | 0.74 | 0.4745 | 1.3609 | 0.74 | 0.6596 | 0.3643 | 0.0868 | | No log | 18.0 | 126 | 0.1373 | 0.75 | 0.4760 | 1.4421 | 0.75 | 0.6703 | 0.3773 | 0.0783 | | No log | 19.0 | 133 | 0.1352 | 0.765 | 0.4851 | 1.1693 | 0.765 | 0.6803 | 0.3952 | 0.0693 | | No log | 20.0 | 140 | 0.1323 | 0.765 | 0.4838 | 1.0026 | 0.765 | 0.6844 | 0.3866 | 0.0741 | | No log | 21.0 | 147 | 0.1334 | 0.785 | 0.4713 | 1.1479 | 0.785 | 0.7430 | 0.4105 | 0.0737 | | No log | 22.0 | 154 | 0.1267 | 0.775 | 0.4706 | 1.1082 | 0.775 | 0.7355 | 0.3838 | 0.0727 | | No log | 23.0 | 161 | 0.1279 | 0.77 | 0.4598 | 1.0869 | 0.7700 | 0.7254 | 0.3846 | 0.0732 | | No log | 24.0 | 168 | 0.1229 | 0.805 | 0.4838 | 1.0060 | 0.805 | 0.7635 | 0.4268 | 0.0625 | | No log | 25.0 | 175 | 0.1250 | 0.79 | 0.4740 | 0.9769 | 0.79 | 0.7462 | 0.3898 | 0.0684 | | No log | 26.0 | 182 | 0.1371 | 0.795 | 0.4784 | 1.1316 | 0.795 | 0.7641 | 0.4246 | 0.0732 | | No log | 27.0 | 189 | 0.1230 | 0.77 | 0.4625 | 0.9606 | 0.7700 | 0.7185 | 0.3816 | 0.0712 | | No log | 28.0 | 196 | 0.1161 | 0.775 | 0.4661 | 0.9889 | 0.775 | 0.7375 | 0.3925 | 0.0658 | | No log | 29.0 | 203 | 0.1194 | 0.775 | 0.4688 | 1.0280 | 0.775 | 0.7320 | 0.4087 | 0.0709 | | No log | 30.0 | 210 | 0.1211 | 0.795 | 0.4680 | 1.0785 | 0.795 | 0.7677 | 0.4168 | 0.0671 | | No log | 31.0 | 217 | 0.1208 | 0.79 | 0.4629 | 0.9986 | 0.79 | 0.7536 | 0.3892 | 0.0658 | | No log | 32.0 | 224 | 0.1194 | 0.77 | 0.4588 | 0.9202 | 0.7700 | 0.7313 | 0.3791 | 0.0679 | | No log | 33.0 | 231 | 0.1167 | 0.795 | 0.4567 | 0.9374 | 0.795 | 0.7602 | 0.3852 | 0.0668 | | No log | 34.0 | 238 | 0.1205 | 0.77 | 0.4653 | 0.9700 | 0.7700 | 0.7291 | 0.3829 | 0.0721 | | No log | 35.0 | 245 | 0.1179 | 0.77 | 0.4616 | 0.9313 | 0.7700 | 0.7366 | 0.3797 | 0.0724 | | No log | 36.0 | 252 | 0.1155 | 0.78 | 0.4566 | 0.9870 | 0.78 | 0.7391 | 0.3718 | 0.0661 | | No log | 37.0 | 259 | 0.1151 | 0.785 | 0.4614 | 0.8936 | 0.785 | 0.7455 | 0.4010 | 0.0684 | | No log | 38.0 | 266 | 0.1126 | 0.78 | 0.4588 | 0.9190 | 0.78 | 0.7406 | 0.3874 | 0.0669 | | No log | 39.0 | 273 | 0.1139 | 0.78 | 0.4637 | 0.9150 | 0.78 | 0.7408 | 0.3874 | 0.0708 | | No log | 40.0 | 280 | 0.1138 | 0.785 | 0.4650 | 0.9096 | 0.785 | 0.7500 | 0.4002 | 0.0680 | | No log | 41.0 | 287 | 0.1139 | 0.79 | 0.4644 | 0.9092 | 0.79 | 0.7590 | 0.4034 | 0.0668 | | No log | 42.0 | 294 | 0.1140 | 0.79 | 0.4670 | 0.9062 | 0.79 | 0.7494 | 0.4042 | 0.0665 | | No log | 43.0 | 301 | 0.1126 | 0.785 | 0.4623 | 0.8470 | 0.785 | 0.7502 | 0.4079 | 0.0660 | | No log | 44.0 | 308 | 0.1146 | 0.775 | 0.4651 | 0.9065 | 0.775 | 0.7314 | 0.3907 | 0.0721 | | No log | 45.0 | 315 | 0.1122 | 0.785 | 0.4643 | 0.8626 | 0.785 | 0.7443 | 0.3991 | 0.0639 | | No log | 46.0 | 322 | 0.1109 | 0.795 | 0.4644 | 0.9087 | 0.795 | 0.7631 | 0.4017 | 0.0629 | | No log | 47.0 | 329 | 0.1116 | 0.79 | 0.4640 | 0.8473 | 0.79 | 0.7584 | 0.3959 | 0.0634 | | No log | 48.0 | 336 | 0.1147 | 0.78 | 0.4662 | 0.8717 | 0.78 | 0.7467 | 0.3859 | 0.0677 | | No log | 49.0 | 343 | 0.1154 | 0.765 | 0.4586 | 1.0035 | 0.765 | 0.7366 | 0.3826 | 0.0764 | | No log | 50.0 | 350 | 0.1112 | 0.79 | 0.4582 | 0.9230 | 0.79 | 0.7525 | 0.3854 | 0.0672 | | No log | 51.0 | 357 | 0.1104 | 0.79 | 0.4633 | 0.9120 | 0.79 | 0.7598 | 0.4000 | 0.0667 | | No log | 52.0 | 364 | 0.1115 | 0.79 | 0.4641 | 0.8550 | 0.79 | 0.7603 | 0.3914 | 0.0672 | | No log | 53.0 | 371 | 0.1150 | 0.77 | 0.4613 | 0.9215 | 0.7700 | 0.7333 | 0.3882 | 0.0733 | | No log | 54.0 | 378 | 0.1100 | 0.8 | 0.4596 | 0.9149 | 0.8000 | 0.7610 | 0.4055 | 0.0657 | | No log | 55.0 | 385 | 0.1094 | 0.785 | 0.4613 | 0.9060 | 0.785 | 0.7506 | 0.3956 | 0.0664 | | No log | 56.0 | 392 | 0.1087 | 0.785 | 0.4607 | 0.9068 | 0.785 | 0.7498 | 0.3984 | 0.0649 | | No log | 57.0 | 399 | 0.1094 | 0.785 | 0.4630 | 0.8993 | 0.785 | 0.7491 | 0.3943 | 0.0674 | | No log | 58.0 | 406 | 0.1100 | 0.805 | 0.4627 | 0.9130 | 0.805 | 0.7693 | 0.4018 | 0.0637 | | No log | 59.0 | 413 | 0.1103 | 0.795 | 0.4619 | 0.8483 | 0.795 | 0.7618 | 0.3992 | 0.0632 | | No log | 60.0 | 420 | 0.1093 | 0.79 | 0.4631 | 0.9007 | 0.79 | 0.7539 | 0.3936 | 0.0647 | | No log | 61.0 | 427 | 0.1095 | 0.79 | 0.4594 | 0.9073 | 0.79 | 0.7539 | 0.4129 | 0.0654 | | No log | 62.0 | 434 | 0.1092 | 0.79 | 0.4591 | 0.9087 | 0.79 | 0.7539 | 0.3956 | 0.0638 | | No log | 63.0 | 441 | 0.1096 | 0.79 | 0.4611 | 0.9075 | 0.79 | 0.7539 | 0.4088 | 0.0654 | | No log | 64.0 | 448 | 0.1093 | 0.79 | 0.4610 | 0.9041 | 0.79 | 0.7539 | 0.3953 | 0.0650 | | No log | 65.0 | 455 | 0.1092 | 0.79 | 0.4602 | 0.9049 | 0.79 | 0.7539 | 0.3845 | 0.0642 | | No log | 66.0 | 462 | 0.1092 | 0.79 | 0.4605 | 0.9027 | 0.79 | 0.7539 | 0.3870 | 0.0646 | | No log | 67.0 | 469 | 0.1094 | 0.79 | 0.4610 | 0.9047 | 0.79 | 0.7539 | 0.3967 | 0.0643 | | No log | 68.0 | 476 | 0.1094 | 0.79 | 0.4602 | 0.9053 | 0.79 | 0.7539 | 0.3956 | 0.0645 | | No log | 69.0 | 483 | 0.1094 | 0.79 | 0.4599 | 0.9054 | 0.79 | 0.7539 | 0.3950 | 0.0646 | | No log | 70.0 | 490 | 0.1095 | 0.79 | 0.4609 | 0.9036 | 0.79 | 0.7539 | 0.4054 | 0.0647 | | No log | 71.0 | 497 | 0.1095 | 0.79 | 0.4601 | 0.9066 | 0.79 | 0.7539 | 0.3937 | 0.0646 | | 0.1361 | 72.0 | 504 | 0.1095 | 0.79 | 0.4602 | 0.9045 | 0.79 | 0.7539 | 0.3958 | 0.0644 | | 0.1361 | 73.0 | 511 | 0.1095 | 0.79 | 0.4605 | 0.9064 | 0.79 | 0.7539 | 0.3900 | 0.0645 | | 0.1361 | 74.0 | 518 | 0.1095 | 0.79 | 0.4606 | 0.9037 | 0.79 | 0.7539 | 0.4116 | 0.0642 | | 0.1361 | 75.0 | 525 | 0.1095 | 0.79 | 0.4606 | 0.9072 | 0.79 | 0.7539 | 0.4079 | 0.0646 | | 0.1361 | 76.0 | 532 | 0.1096 | 0.79 | 0.4604 | 0.9066 | 0.79 | 0.7539 | 0.4017 | 0.0644 | | 0.1361 | 77.0 | 539 | 0.1095 | 0.79 | 0.4603 | 0.9062 | 0.79 | 0.7539 | 0.4014 | 0.0646 | | 0.1361 | 78.0 | 546 | 0.1096 | 0.79 | 0.4600 | 0.9053 | 0.79 | 0.7539 | 0.3957 | 0.0644 | | 0.1361 | 79.0 | 553 | 0.1096 | 0.79 | 0.4606 | 0.9056 | 0.79 | 0.7539 | 0.3986 | 0.0645 | | 0.1361 | 80.0 | 560 | 0.1097 | 0.79 | 0.4602 | 0.9059 | 0.79 | 0.7539 | 0.4023 | 0.0647 | | 0.1361 | 81.0 | 567 | 0.1096 | 0.79 | 0.4604 | 0.9056 | 0.79 | 0.7539 | 0.4042 | 0.0645 | | 0.1361 | 82.0 | 574 | 0.1097 | 0.79 | 0.4603 | 0.9058 | 0.79 | 0.7539 | 0.4082 | 0.0646 | | 0.1361 | 83.0 | 581 | 0.1097 | 0.79 | 0.4606 | 0.9066 | 0.79 | 0.7539 | 0.4085 | 0.0645 | | 0.1361 | 84.0 | 588 | 0.1097 | 0.79 | 0.4603 | 0.9060 | 0.79 | 0.7539 | 0.4040 | 0.0645 | | 0.1361 | 85.0 | 595 | 0.1097 | 0.79 | 0.4606 | 0.9059 | 0.79 | 0.7539 | 0.3949 | 0.0645 | | 0.1361 | 86.0 | 602 | 0.1097 | 0.79 | 0.4603 | 0.9059 | 0.79 | 0.7539 | 0.4040 | 0.0645 | | 0.1361 | 87.0 | 609 | 0.1097 | 0.79 | 0.4605 | 0.9051 | 0.79 | 0.7539 | 0.4025 | 0.0644 | | 0.1361 | 88.0 | 616 | 0.1097 | 0.79 | 0.4605 | 0.9055 | 0.79 | 0.7539 | 0.3962 | 0.0643 | | 0.1361 | 89.0 | 623 | 0.1097 | 0.79 | 0.4603 | 0.9056 | 0.79 | 0.7539 | 0.4040 | 0.0643 | | 0.1361 | 90.0 | 630 | 0.1098 | 0.79 | 0.4604 | 0.9051 | 0.79 | 0.7539 | 0.3962 | 0.0643 | | 0.1361 | 91.0 | 637 | 0.1098 | 0.79 | 0.4604 | 0.9064 | 0.79 | 0.7539 | 0.4041 | 0.0644 | | 0.1361 | 92.0 | 644 | 0.1098 | 0.79 | 0.4605 | 0.9055 | 0.79 | 0.7539 | 0.4004 | 0.0644 | | 0.1361 | 93.0 | 651 | 0.1098 | 0.79 | 0.4605 | 0.9059 | 0.79 | 0.7539 | 0.4042 | 0.0644 | | 0.1361 | 94.0 | 658 | 0.1098 | 0.79 | 0.4603 | 0.9059 | 0.79 | 0.7539 | 0.4094 | 0.0643 | | 0.1361 | 95.0 | 665 | 0.1098 | 0.79 | 0.4605 | 0.9056 | 0.79 | 0.7539 | 0.4138 | 0.0645 | | 0.1361 | 96.0 | 672 | 0.1098 | 0.79 | 0.4604 | 0.9059 | 0.79 | 0.7539 | 0.4095 | 0.0643 | | 0.1361 | 97.0 | 679 | 0.1098 | 0.79 | 0.4604 | 0.9057 | 0.79 | 0.7539 | 0.4137 | 0.0643 | | 0.1361 | 98.0 | 686 | 0.1098 | 0.79 | 0.4604 | 0.9059 | 0.79 | 0.7539 | 0.4096 | 0.0643 | | 0.1361 | 99.0 | 693 | 0.1098 | 0.79 | 0.4604 | 0.9059 | 0.79 | 0.7539 | 0.4137 | 0.0644 | | 0.1361 | 100.0 | 700 | 0.1098 | 0.79 | 0.4604 | 0.9058 | 0.79 | 0.7539 | 0.4083 | 0.0644 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/60-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# 60-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0655 - Accuracy: 0.82 - Brier Loss: 0.2655 - Nll: 1.0777 - F1 Micro: 0.82 - F1 Macro: 0.7953 - Ece: 0.1364 - Aurc: 0.0539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 5.7153 | 0.09 | 1.0182 | 8.6654 | 0.09 | 0.0745 | 0.3226 | 0.8984 | | No log | 2.0 | 14 | 4.7081 | 0.13 | 0.9028 | 8.0605 | 0.13 | 0.0949 | 0.2402 | 0.8297 | | No log | 3.0 | 21 | 4.3197 | 0.215 | 0.8419 | 6.1917 | 0.2150 | 0.1338 | 0.2566 | 0.5994 | | No log | 4.0 | 28 | 4.1502 | 0.405 | 0.7493 | 4.1855 | 0.405 | 0.3261 | 0.3035 | 0.4065 | | No log | 5.0 | 35 | 4.0310 | 0.545 | 0.6643 | 2.9984 | 0.545 | 0.4451 | 0.3080 | 0.2731 | | No log | 6.0 | 42 | 3.9472 | 0.64 | 0.6089 | 2.4473 | 0.64 | 0.5047 | 0.3290 | 0.1849 | | No log | 7.0 | 49 | 3.8616 | 0.655 | 0.5435 | 2.3233 | 0.655 | 0.5434 | 0.3086 | 0.1536 | | No log | 8.0 | 56 | 3.8289 | 0.72 | 0.5177 | 2.1266 | 0.72 | 0.6162 | 0.3496 | 0.1202 | | No log | 9.0 | 63 | 3.7812 | 0.715 | 0.4461 | 1.7718 | 0.715 | 0.6276 | 0.2464 | 0.1099 | | No log | 10.0 | 70 | 3.7599 | 0.715 | 0.3960 | 1.7795 | 0.715 | 0.6158 | 0.2262 | 0.0879 | | No log | 11.0 | 77 | 3.7506 | 0.74 | 0.3758 | 1.9447 | 0.74 | 0.6284 | 0.2212 | 0.0847 | | No log | 12.0 | 84 | 3.7261 | 0.79 | 0.3515 | 1.7839 | 0.79 | 0.7107 | 0.2419 | 0.0698 | | No log | 13.0 | 91 | 3.8045 | 0.72 | 0.3765 | 1.8827 | 0.72 | 0.6249 | 0.1943 | 0.0941 | | No log | 14.0 | 98 | 3.6964 | 0.79 | 0.3309 | 1.8042 | 0.79 | 0.6873 | 0.2162 | 0.0629 | | No log | 15.0 | 105 | 3.7058 | 0.78 | 0.3285 | 1.8229 | 0.78 | 0.6827 | 0.1999 | 0.0672 | | No log | 16.0 | 112 | 3.8395 | 0.74 | 0.3502 | 1.9683 | 0.74 | 0.6554 | 0.1777 | 0.0880 | | No log | 17.0 | 119 | 3.7568 | 0.795 | 0.3120 | 1.3060 | 0.795 | 0.7121 | 0.1656 | 0.0633 | | No log | 18.0 | 126 | 3.7399 | 0.795 | 0.3099 | 1.6225 | 0.795 | 0.7133 | 0.1721 | 0.0615 | | No log | 19.0 | 133 | 3.7813 | 0.82 | 0.2884 | 1.3165 | 0.82 | 0.7475 | 0.1698 | 0.0565 | | No log | 20.0 | 140 | 3.7907 | 0.8 | 0.3074 | 1.2638 | 0.8000 | 0.7701 | 0.1862 | 0.0654 | | No log | 21.0 | 147 | 3.7687 | 0.83 | 0.2860 | 1.2646 | 0.83 | 0.8077 | 0.1730 | 0.0569 | | No log | 22.0 | 154 | 3.7182 | 0.82 | 0.2813 | 1.3271 | 0.82 | 0.7848 | 0.1762 | 0.0551 | | No log | 23.0 | 161 | 3.7801 | 0.83 | 0.2679 | 1.1397 | 0.83 | 0.8015 | 0.1574 | 0.0568 | | No log | 24.0 | 168 | 3.7659 | 0.82 | 0.2752 | 1.1301 | 0.82 | 0.7893 | 0.1581 | 0.0582 | | No log | 25.0 | 175 | 3.7960 | 0.825 | 0.2755 | 1.2565 | 0.825 | 0.8017 | 0.1663 | 0.0540 | | No log | 26.0 | 182 | 3.7630 | 0.82 | 0.2715 | 1.0265 | 0.82 | 0.7971 | 0.1439 | 0.0536 | | No log | 27.0 | 189 | 3.8411 | 0.82 | 0.2822 | 1.2532 | 0.82 | 0.7929 | 0.1621 | 0.0623 | | No log | 28.0 | 196 | 3.7869 | 0.83 | 0.2608 | 1.1781 | 0.83 | 0.8013 | 0.1461 | 0.0531 | | No log | 29.0 | 203 | 3.7798 | 0.81 | 0.2724 | 1.0950 | 0.81 | 0.7785 | 0.1436 | 0.0562 | | No log | 30.0 | 210 | 3.8412 | 0.82 | 0.2724 | 1.3168 | 0.82 | 0.7892 | 0.1473 | 0.0596 | | No log | 31.0 | 217 | 3.7937 | 0.83 | 0.2683 | 1.1818 | 0.83 | 0.8137 | 0.1523 | 0.0515 | | No log | 32.0 | 224 | 3.7964 | 0.825 | 0.2655 | 1.1605 | 0.825 | 0.8028 | 0.1580 | 0.0551 | | No log | 33.0 | 231 | 3.7950 | 0.81 | 0.2685 | 1.2514 | 0.81 | 0.7854 | 0.1275 | 0.0534 | | No log | 34.0 | 238 | 3.8086 | 0.83 | 0.2599 | 0.9670 | 0.83 | 0.8022 | 0.1309 | 0.0529 | | No log | 35.0 | 245 | 3.8393 | 0.83 | 0.2649 | 0.9615 | 0.83 | 0.8050 | 0.1445 | 0.0543 | | No log | 36.0 | 252 | 3.8140 | 0.84 | 0.2528 | 1.0685 | 0.8400 | 0.8194 | 0.1371 | 0.0528 | | No log | 37.0 | 259 | 3.8164 | 0.825 | 0.2665 | 1.0001 | 0.825 | 0.8030 | 0.1501 | 0.0548 | | No log | 38.0 | 266 | 3.8298 | 0.82 | 0.2669 | 1.0166 | 0.82 | 0.8007 | 0.1413 | 0.0549 | | No log | 39.0 | 273 | 3.8346 | 0.815 | 0.2646 | 1.1609 | 0.815 | 0.7876 | 0.1453 | 0.0551 | | No log | 40.0 | 280 | 3.8409 | 0.825 | 0.2610 | 1.1811 | 0.825 | 0.8054 | 0.1432 | 0.0551 | | No log | 41.0 | 287 | 3.8106 | 0.835 | 0.2642 | 1.0262 | 0.835 | 0.8078 | 0.1471 | 0.0547 | | No log | 42.0 | 294 | 3.8402 | 0.81 | 0.2591 | 0.9683 | 0.81 | 0.7863 | 0.1638 | 0.0546 | | No log | 43.0 | 301 | 3.8480 | 0.82 | 0.2604 | 1.1408 | 0.82 | 0.7933 | 0.1448 | 0.0538 | | No log | 44.0 | 308 | 3.8627 | 0.835 | 0.2717 | 1.0142 | 0.835 | 0.8113 | 0.1472 | 0.0550 | | No log | 45.0 | 315 | 3.8274 | 0.83 | 0.2589 | 1.0148 | 0.83 | 0.8138 | 0.1333 | 0.0518 | | No log | 46.0 | 322 | 3.8349 | 0.83 | 0.2563 | 0.9773 | 0.83 | 0.8086 | 0.1237 | 0.0537 | | No log | 47.0 | 329 | 3.8656 | 0.82 | 0.2668 | 0.9728 | 0.82 | 0.7947 | 0.1330 | 0.0546 | | No log | 48.0 | 336 | 3.8455 | 0.84 | 0.2514 | 0.9784 | 0.8400 | 0.8175 | 0.1519 | 0.0509 | | No log | 49.0 | 343 | 3.8827 | 0.81 | 0.2740 | 0.9515 | 0.81 | 0.7852 | 0.1325 | 0.0565 | | No log | 50.0 | 350 | 3.8617 | 0.815 | 0.2659 | 1.2129 | 0.815 | 0.7885 | 0.1430 | 0.0565 | | No log | 51.0 | 357 | 3.8555 | 0.825 | 0.2601 | 0.9544 | 0.825 | 0.8023 | 0.1500 | 0.0535 | | No log | 52.0 | 364 | 3.8794 | 0.825 | 0.2611 | 1.1499 | 0.825 | 0.7998 | 0.1520 | 0.0550 | | No log | 53.0 | 371 | 3.8563 | 0.815 | 0.2592 | 1.0856 | 0.815 | 0.7896 | 0.1198 | 0.0525 | | No log | 54.0 | 378 | 3.8851 | 0.825 | 0.2579 | 0.8909 | 0.825 | 0.8042 | 0.1320 | 0.0543 | | No log | 55.0 | 385 | 3.8682 | 0.815 | 0.2589 | 1.1381 | 0.815 | 0.7906 | 0.1376 | 0.0553 | | No log | 56.0 | 392 | 3.8646 | 0.83 | 0.2582 | 1.0602 | 0.83 | 0.8144 | 0.1330 | 0.0532 | | No log | 57.0 | 399 | 3.9205 | 0.82 | 0.2678 | 1.0739 | 0.82 | 0.7991 | 0.1403 | 0.0569 | | No log | 58.0 | 406 | 3.8878 | 0.835 | 0.2613 | 0.9320 | 0.835 | 0.8112 | 0.1575 | 0.0527 | | No log | 59.0 | 413 | 3.8944 | 0.82 | 0.2662 | 1.0116 | 0.82 | 0.7966 | 0.1304 | 0.0544 | | No log | 60.0 | 420 | 3.8819 | 0.825 | 0.2597 | 1.1330 | 0.825 | 0.8019 | 0.1471 | 0.0529 | | No log | 61.0 | 427 | 3.9219 | 0.82 | 0.2719 | 1.0206 | 0.82 | 0.7957 | 0.1465 | 0.0563 | | No log | 62.0 | 434 | 3.8903 | 0.815 | 0.2583 | 0.9983 | 0.815 | 0.7945 | 0.1428 | 0.0532 | | No log | 63.0 | 441 | 3.9258 | 0.82 | 0.2674 | 1.0722 | 0.82 | 0.7941 | 0.1312 | 0.0552 | | No log | 64.0 | 448 | 3.9086 | 0.83 | 0.2546 | 1.1474 | 0.83 | 0.8049 | 0.1375 | 0.0522 | | No log | 65.0 | 455 | 3.9260 | 0.83 | 0.2655 | 1.0795 | 0.83 | 0.8074 | 0.1300 | 0.0537 | | No log | 66.0 | 462 | 3.9424 | 0.82 | 0.2634 | 1.0220 | 0.82 | 0.7978 | 0.1298 | 0.0542 | | No log | 67.0 | 469 | 3.9315 | 0.815 | 0.2605 | 1.0206 | 0.815 | 0.7882 | 0.1404 | 0.0534 | | No log | 68.0 | 476 | 3.9583 | 0.82 | 0.2634 | 1.0018 | 0.82 | 0.7991 | 0.1321 | 0.0532 | | No log | 69.0 | 483 | 3.9447 | 0.825 | 0.2606 | 1.0840 | 0.825 | 0.8003 | 0.1326 | 0.0533 | | No log | 70.0 | 490 | 3.9698 | 0.815 | 0.2646 | 0.9721 | 0.815 | 0.7940 | 0.1359 | 0.0546 | | No log | 71.0 | 497 | 3.9621 | 0.82 | 0.2615 | 1.0207 | 0.82 | 0.7953 | 0.1312 | 0.0540 | | 3.4582 | 72.0 | 504 | 3.9687 | 0.81 | 0.2628 | 1.0078 | 0.81 | 0.7861 | 0.1379 | 0.0549 | | 3.4582 | 73.0 | 511 | 3.9699 | 0.825 | 0.2575 | 1.0739 | 0.825 | 0.8024 | 0.1257 | 0.0534 | | 3.4582 | 74.0 | 518 | 3.9791 | 0.82 | 0.2638 | 1.0290 | 0.82 | 0.7978 | 0.1337 | 0.0543 | | 3.4582 | 75.0 | 525 | 3.9893 | 0.82 | 0.2664 | 1.0711 | 0.82 | 0.7978 | 0.1323 | 0.0543 | | 3.4582 | 76.0 | 532 | 3.9843 | 0.825 | 0.2620 | 0.9559 | 0.825 | 0.7999 | 0.1307 | 0.0535 | | 3.4582 | 77.0 | 539 | 3.9983 | 0.82 | 0.2626 | 1.0239 | 0.82 | 0.7953 | 0.1333 | 0.0543 | | 3.4582 | 78.0 | 546 | 4.0068 | 0.82 | 0.2663 | 1.0205 | 0.82 | 0.7953 | 0.1421 | 0.0542 | | 3.4582 | 79.0 | 553 | 4.0096 | 0.82 | 0.2636 | 1.0204 | 0.82 | 0.7953 | 0.1296 | 0.0541 | | 3.4582 | 80.0 | 560 | 4.0211 | 0.82 | 0.2655 | 1.0147 | 0.82 | 0.7953 | 0.1365 | 0.0541 | | 3.4582 | 81.0 | 567 | 4.0206 | 0.815 | 0.2631 | 1.0781 | 0.815 | 0.7902 | 0.1378 | 0.0543 | | 3.4582 | 82.0 | 574 | 4.0270 | 0.82 | 0.2652 | 1.0752 | 0.82 | 0.7953 | 0.1407 | 0.0539 | | 3.4582 | 83.0 | 581 | 4.0301 | 0.82 | 0.2642 | 1.0763 | 0.82 | 0.7953 | 0.1444 | 0.0537 | | 3.4582 | 84.0 | 588 | 4.0369 | 0.82 | 0.2652 | 1.0758 | 0.82 | 0.7953 | 0.1355 | 0.0539 | | 3.4582 | 85.0 | 595 | 4.0395 | 0.82 | 0.2648 | 1.0244 | 0.82 | 0.7953 | 0.1300 | 0.0539 | | 3.4582 | 86.0 | 602 | 4.0428 | 0.82 | 0.2651 | 1.0768 | 0.82 | 0.7953 | 0.1209 | 0.0538 | | 3.4582 | 87.0 | 609 | 4.0461 | 0.82 | 0.2653 | 1.0780 | 0.82 | 0.7953 | 0.1295 | 0.0536 | | 3.4582 | 88.0 | 616 | 4.0485 | 0.82 | 0.2650 | 1.0780 | 0.82 | 0.7953 | 0.1375 | 0.0537 | | 3.4582 | 89.0 | 623 | 4.0521 | 0.82 | 0.2652 | 1.0259 | 0.82 | 0.7953 | 0.1296 | 0.0537 | | 3.4582 | 90.0 | 630 | 4.0540 | 0.82 | 0.2652 | 1.0466 | 0.82 | 0.7953 | 0.1282 | 0.0538 | | 3.4582 | 91.0 | 637 | 4.0562 | 0.82 | 0.2653 | 1.0778 | 0.82 | 0.7953 | 0.1304 | 0.0538 | | 3.4582 | 92.0 | 644 | 4.0587 | 0.82 | 0.2655 | 1.0774 | 0.82 | 0.7953 | 0.1241 | 0.0537 | | 3.4582 | 93.0 | 651 | 4.0607 | 0.82 | 0.2657 | 1.0771 | 0.82 | 0.7953 | 0.1357 | 0.0538 | | 3.4582 | 94.0 | 658 | 4.0615 | 0.82 | 0.2655 | 1.0775 | 0.82 | 0.7953 | 0.1268 | 0.0538 | | 3.4582 | 95.0 | 665 | 4.0625 | 0.82 | 0.2654 | 1.0300 | 0.82 | 0.7953 | 0.1211 | 0.0538 | | 3.4582 | 96.0 | 672 | 4.0641 | 0.82 | 0.2657 | 1.0771 | 0.82 | 0.7953 | 0.1365 | 0.0538 | | 3.4582 | 97.0 | 679 | 4.0641 | 0.82 | 0.2654 | 1.0312 | 0.82 | 0.7953 | 0.1210 | 0.0538 | | 3.4582 | 98.0 | 686 | 4.0652 | 0.82 | 0.2656 | 1.0776 | 0.82 | 0.7953 | 0.1269 | 0.0538 | | 3.4582 | 99.0 | 693 | 4.0654 | 0.82 | 0.2656 | 1.0775 | 0.82 | 0.7953 | 0.1364 | 0.0539 | | 3.4582 | 100.0 | 700 | 4.0655 | 0.82 | 0.2655 | 1.0777 | 0.82 | 0.7953 | 0.1364 | 0.0539 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/60-tiny_tobacco3482_hint_

# 60-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 63.5396 - Accuracy: 0.84 - Brier Loss: 0.3043 - Nll: 1.1495 - F1 Micro: 0.8400 - F1 Macro: 0.8244 - Ece: 0.1568 - Aurc: 0.0457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 66.6267 | 0.26 | 0.8706 | 4.9000 | 0.26 | 0.1920 | 0.2904 | 0.7812 | | No log | 2.0 | 50 | 65.8408 | 0.54 | 0.5951 | 2.8543 | 0.54 | 0.4535 | 0.2542 | 0.2567 | | No log | 3.0 | 75 | 65.3708 | 0.675 | 0.4400 | 1.6094 | 0.675 | 0.6134 | 0.2395 | 0.1333 | | No log | 4.0 | 100 | 65.0889 | 0.76 | 0.3809 | 1.5505 | 0.76 | 0.7422 | 0.2333 | 0.1125 | | No log | 5.0 | 125 | 64.7800 | 0.8 | 0.3080 | 1.7523 | 0.8000 | 0.7663 | 0.1734 | 0.0708 | | No log | 6.0 | 150 | 64.6296 | 0.78 | 0.3286 | 1.7771 | 0.78 | 0.7427 | 0.1752 | 0.0642 | | No log | 7.0 | 175 | 64.3879 | 0.765 | 0.3584 | 1.7400 | 0.765 | 0.6986 | 0.1799 | 0.0937 | | No log | 8.0 | 200 | 64.4361 | 0.72 | 0.4640 | 1.4368 | 0.72 | 0.7385 | 0.2350 | 0.1314 | | No log | 9.0 | 225 | 64.2223 | 0.76 | 0.3846 | 1.6420 | 0.76 | 0.7417 | 0.2006 | 0.0915 | | No log | 10.0 | 250 | 64.2618 | 0.725 | 0.4268 | 1.6667 | 0.7250 | 0.7132 | 0.2131 | 0.1110 | | No log | 11.0 | 275 | 64.2839 | 0.7 | 0.4830 | 1.7975 | 0.7 | 0.6829 | 0.2213 | 0.1207 | | No log | 12.0 | 300 | 64.0218 | 0.785 | 0.3523 | 1.7098 | 0.785 | 0.7363 | 0.1742 | 0.0702 | | No log | 13.0 | 325 | 63.8071 | 0.78 | 0.3218 | 1.4587 | 0.78 | 0.7574 | 0.1640 | 0.0674 | | No log | 14.0 | 350 | 64.7387 | 0.645 | 0.5871 | 2.0188 | 0.645 | 0.6360 | 0.2996 | 0.1765 | | No log | 15.0 | 375 | 64.2173 | 0.765 | 0.3832 | 1.8093 | 0.765 | 0.6909 | 0.1978 | 0.0892 | | No log | 16.0 | 400 | 64.2233 | 0.765 | 0.3897 | 1.4456 | 0.765 | 0.7432 | 0.1983 | 0.0805 | | No log | 17.0 | 425 | 63.7977 | 0.825 | 0.2971 | 1.4248 | 0.825 | 0.8057 | 0.1583 | 0.0546 | | No log | 18.0 | 450 | 63.5818 | 0.82 | 0.2983 | 1.3079 | 0.82 | 0.7936 | 0.1532 | 0.0474 | | No log | 19.0 | 475 | 64.1935 | 0.78 | 0.3764 | 1.6662 | 0.78 | 0.7618 | 0.1911 | 0.0669 | | 63.0313 | 20.0 | 500 | 63.6054 | 0.825 | 0.2871 | 1.4054 | 0.825 | 0.8118 | 0.1605 | 0.0520 | | 63.0313 | 21.0 | 525 | 63.6316 | 0.79 | 0.3258 | 1.3131 | 0.79 | 0.7714 | 0.1632 | 0.0485 | | 63.0313 | 22.0 | 550 | 63.6978 | 0.84 | 0.2935 | 1.2425 | 0.8400 | 0.8236 | 0.1508 | 0.0586 | | 63.0313 | 23.0 | 575 | 63.8266 | 0.825 | 0.3117 | 1.5766 | 0.825 | 0.8019 | 0.1550 | 0.0554 | | 63.0313 | 24.0 | 600 | 63.6750 | 0.825 | 0.3130 | 1.1848 | 0.825 | 0.8158 | 0.1553 | 0.0462 | | 63.0313 | 25.0 | 625 | 63.8469 | 0.82 | 0.3259 | 1.3997 | 0.82 | 0.8007 | 0.1603 | 0.0564 | | 63.0313 | 26.0 | 650 | 63.7656 | 0.815 | 0.3285 | 1.2752 | 0.815 | 0.7969 | 0.1656 | 0.0535 | | 63.0313 | 27.0 | 675 | 63.8074 | 0.805 | 0.3455 | 1.1282 | 0.805 | 0.7870 | 0.1732 | 0.0542 | | 63.0313 | 28.0 | 700 | 63.8411 | 0.81 | 0.3437 | 1.1501 | 0.81 | 0.7917 | 0.1759 | 0.0529 | | 63.0313 | 29.0 | 725 | 63.8158 | 0.81 | 0.3345 | 1.1519 | 0.81 | 0.7901 | 0.1706 | 0.0544 | | 63.0313 | 30.0 | 750 | 63.7917 | 0.815 | 0.3383 | 1.2013 | 0.815 | 0.8006 | 0.1706 | 0.0557 | | 63.0313 | 31.0 | 775 | 63.7855 | 0.815 | 0.3396 | 1.2088 | 0.815 | 0.7974 | 0.1687 | 0.0551 | | 63.0313 | 32.0 | 800 | 63.8003 | 0.825 | 0.3297 | 1.2233 | 0.825 | 0.8091 | 0.1694 | 0.0547 | | 63.0313 | 33.0 | 825 | 63.8029 | 0.815 | 0.3405 | 1.2628 | 0.815 | 0.8007 | 0.1729 | 0.0547 | | 63.0313 | 34.0 | 850 | 63.7752 | 0.81 | 0.3352 | 1.2587 | 0.81 | 0.7979 | 0.1727 | 0.0574 | | 63.0313 | 35.0 | 875 | 63.7800 | 0.815 | 0.3346 | 1.1948 | 0.815 | 0.7977 | 0.1679 | 0.0560 | | 63.0313 | 36.0 | 900 | 63.7885 | 0.825 | 0.3313 | 1.2728 | 0.825 | 0.8173 | 0.1591 | 0.0569 | | 63.0313 | 37.0 | 925 | 63.7730 | 0.815 | 0.3354 | 1.2726 | 0.815 | 0.8027 | 0.1689 | 0.0555 | | 63.0313 | 38.0 | 950 | 63.8327 | 0.815 | 0.3405 | 1.4350 | 0.815 | 0.8043 | 0.1675 | 0.0632 | | 63.0313 | 39.0 | 975 | 63.7324 | 0.785 | 0.3686 | 1.6439 | 0.785 | 0.7745 | 0.1808 | 0.0666 | | 61.6786 | 40.0 | 1000 | 63.8625 | 0.765 | 0.3946 | 1.6127 | 0.765 | 0.7727 | 0.1961 | 0.0723 | | 61.6786 | 41.0 | 1025 | 64.1254 | 0.765 | 0.3904 | 1.5456 | 0.765 | 0.7570 | 0.2020 | 0.0850 | | 61.6786 | 42.0 | 1050 | 63.6201 | 0.78 | 0.3728 | 1.4198 | 0.78 | 0.7447 | 0.1869 | 0.0647 | | 61.6786 | 43.0 | 1075 | 63.6033 | 0.835 | 0.2968 | 1.5430 | 0.835 | 0.8059 | 0.1574 | 0.0479 | | 61.6786 | 44.0 | 1100 | 63.6777 | 0.795 | 0.3606 | 1.3542 | 0.795 | 0.7638 | 0.1806 | 0.0529 | | 61.6786 | 45.0 | 1125 | 63.5747 | 0.83 | 0.2996 | 1.5403 | 0.83 | 0.8079 | 0.1450 | 0.0504 | | 61.6786 | 46.0 | 1150 | 63.6022 | 0.805 | 0.3389 | 1.3842 | 0.805 | 0.7791 | 0.1794 | 0.0466 | | 61.6786 | 47.0 | 1175 | 63.6342 | 0.81 | 0.3346 | 1.2861 | 0.81 | 0.7811 | 0.1678 | 0.0476 | | 61.6786 | 48.0 | 1200 | 63.6065 | 0.81 | 0.3298 | 1.2911 | 0.81 | 0.7807 | 0.1654 | 0.0465 | | 61.6786 | 49.0 | 1225 | 63.5937 | 0.815 | 0.3260 | 1.3576 | 0.815 | 0.7844 | 0.1613 | 0.0467 | | 61.6786 | 50.0 | 1250 | 63.6029 | 0.815 | 0.3241 | 1.2826 | 0.815 | 0.7844 | 0.1662 | 0.0467 | | 61.6786 | 51.0 | 1275 | 63.5947 | 0.81 | 0.3232 | 1.4156 | 0.81 | 0.7789 | 0.1631 | 0.0471 | | 61.6786 | 52.0 | 1300 | 63.6501 | 0.81 | 0.3268 | 1.4148 | 0.81 | 0.7785 | 0.1703 | 0.0468 | | 61.6786 | 53.0 | 1325 | 63.6207 | 0.81 | 0.3207 | 1.2785 | 0.81 | 0.7785 | 0.1698 | 0.0479 | | 61.6786 | 54.0 | 1350 | 63.6021 | 0.815 | 0.3233 | 1.3519 | 0.815 | 0.7818 | 0.1629 | 0.0456 | | 61.6786 | 55.0 | 1375 | 63.6128 | 0.815 | 0.3207 | 1.2837 | 0.815 | 0.7818 | 0.1641 | 0.0474 | | 61.6786 | 56.0 | 1400 | 63.5974 | 0.81 | 0.3194 | 1.3542 | 0.81 | 0.7789 | 0.1679 | 0.0474 | | 61.6786 | 57.0 | 1425 | 63.6173 | 0.81 | 0.3260 | 1.2907 | 0.81 | 0.7761 | 0.1653 | 0.0486 | | 61.6786 | 58.0 | 1450 | 63.6057 | 0.81 | 0.3163 | 1.2981 | 0.81 | 0.7789 | 0.1651 | 0.0471 | | 61.6786 | 59.0 | 1475 | 63.6052 | 0.81 | 0.3197 | 1.3444 | 0.81 | 0.7789 | 0.1680 | 0.0467 | | 61.52 | 60.0 | 1500 | 63.5865 | 0.82 | 0.3143 | 1.2748 | 0.82 | 0.7920 | 0.1617 | 0.0465 | | 61.52 | 61.0 | 1525 | 63.5754 | 0.82 | 0.3126 | 1.2677 | 0.82 | 0.7920 | 0.1595 | 0.0468 | | 61.52 | 62.0 | 1550 | 63.5876 | 0.815 | 0.3120 | 1.2691 | 0.815 | 0.7879 | 0.1567 | 0.0478 | | 61.52 | 63.0 | 1575 | 63.6040 | 0.82 | 0.3110 | 1.2632 | 0.82 | 0.7920 | 0.1526 | 0.0472 | | 61.52 | 64.0 | 1600 | 63.5956 | 0.82 | 0.3111 | 1.1976 | 0.82 | 0.7963 | 0.1592 | 0.0468 | | 61.52 | 65.0 | 1625 | 63.5792 | 0.815 | 0.3095 | 1.1928 | 0.815 | 0.7879 | 0.1571 | 0.0469 | | 61.52 | 66.0 | 1650 | 63.5704 | 0.82 | 0.3086 | 1.2509 | 0.82 | 0.7936 | 0.1543 | 0.0467 | | 61.52 | 67.0 | 1675 | 63.5918 | 0.82 | 0.3118 | 1.2536 | 0.82 | 0.7936 | 0.1619 | 0.0471 | | 61.52 | 68.0 | 1700 | 63.5741 | 0.82 | 0.3072 | 1.2491 | 0.82 | 0.7963 | 0.1562 | 0.0465 | | 61.52 | 69.0 | 1725 | 63.5581 | 0.825 | 0.3085 | 1.2490 | 0.825 | 0.8021 | 0.1566 | 0.0460 | | 61.52 | 70.0 | 1750 | 63.5796 | 0.82 | 0.3087 | 1.2456 | 0.82 | 0.7963 | 0.1556 | 0.0471 | | 61.52 | 71.0 | 1775 | 63.5776 | 0.825 | 0.3073 | 1.2530 | 0.825 | 0.8021 | 0.1571 | 0.0474 | | 61.52 | 72.0 | 1800 | 63.5524 | 0.825 | 0.3064 | 1.2402 | 0.825 | 0.8021 | 0.1555 | 0.0465 | | 61.52 | 73.0 | 1825 | 63.5638 | 0.825 | 0.3075 | 1.2465 | 0.825 | 0.8021 | 0.1607 | 0.0466 | | 61.52 | 74.0 | 1850 | 63.5654 | 0.82 | 0.3058 | 1.2425 | 0.82 | 0.7963 | 0.1552 | 0.0468 | | 61.52 | 75.0 | 1875 | 63.5654 | 0.825 | 0.3041 | 1.2439 | 0.825 | 0.8021 | 0.1563 | 0.0466 | | 61.52 | 76.0 | 1900 | 63.5499 | 0.83 | 0.3018 | 1.2432 | 0.83 | 0.8082 | 0.1541 | 0.0463 | | 61.52 | 77.0 | 1925 | 63.5563 | 0.825 | 0.3059 | 1.2385 | 0.825 | 0.8021 | 0.1570 | 0.0466 | | 61.52 | 78.0 | 1950 | 63.5524 | 0.825 | 0.3045 | 1.2364 | 0.825 | 0.8021 | 0.1524 | 0.0464 | | 61.52 | 79.0 | 1975 | 63.5507 | 0.825 | 0.3064 | 1.2344 | 0.825 | 0.8021 | 0.1523 | 0.0463 | | 61.4257 | 80.0 | 2000 | 63.5531 | 0.825 | 0.3062 | 1.2266 | 0.825 | 0.8035 | 0.1625 | 0.0463 | | 61.4257 | 81.0 | 2025 | 63.5486 | 0.825 | 0.3029 | 1.1850 | 0.825 | 0.8024 | 0.1506 | 0.0463 | | 61.4257 | 82.0 | 2050 | 63.5479 | 0.82 | 0.3081 | 1.2269 | 0.82 | 0.7963 | 0.1588 | 0.0458 | | 61.4257 | 83.0 | 2075 | 63.5444 | 0.835 | 0.3029 | 1.1721 | 0.835 | 0.8139 | 0.1475 | 0.0461 | | 61.4257 | 84.0 | 2100 | 63.5435 | 0.835 | 0.3047 | 1.2306 | 0.835 | 0.8171 | 0.1529 | 0.0464 | | 61.4257 | 85.0 | 2125 | 63.5393 | 0.83 | 0.3058 | 1.2255 | 0.83 | 0.8081 | 0.1462 | 0.0464 | | 61.4257 | 86.0 | 2150 | 63.5437 | 0.835 | 0.3048 | 1.2254 | 0.835 | 0.8171 | 0.1481 | 0.0464 | | 61.4257 | 87.0 | 2175 | 63.5463 | 0.83 | 0.3039 | 1.1549 | 0.83 | 0.8115 | 0.1562 | 0.0463 | | 61.4257 | 88.0 | 2200 | 63.5408 | 0.835 | 0.3055 | 1.2211 | 0.835 | 0.8187 | 0.1485 | 0.0462 | | 61.4257 | 89.0 | 2225 | 63.5477 | 0.825 | 0.3054 | 1.1541 | 0.825 | 0.8024 | 0.1521 | 0.0463 | | 61.4257 | 90.0 | 2250 | 63.5383 | 0.83 | 0.3051 | 1.1577 | 0.83 | 0.8095 | 0.1532 | 0.0463 | | 61.4257 | 91.0 | 2275 | 63.5466 | 0.84 | 0.3057 | 1.1583 | 0.8400 | 0.8244 | 0.1516 | 0.0458 | | 61.4257 | 92.0 | 2300 | 63.5447 | 0.835 | 0.3049 | 1.1518 | 0.835 | 0.8188 | 0.1615 | 0.0462 | | 61.4257 | 93.0 | 2325 | 63.5327 | 0.84 | 0.3044 | 1.1540 | 0.8400 | 0.8244 | 0.1508 | 0.0459 | | 61.4257 | 94.0 | 2350 | 63.5392 | 0.84 | 0.3046 | 1.1506 | 0.8400 | 0.8244 | 0.1569 | 0.0459 | | 61.4257 | 95.0 | 2375 | 63.5305 | 0.835 | 0.3050 | 1.1520 | 0.835 | 0.8188 | 0.1571 | 0.0457 | | 61.4257 | 96.0 | 2400 | 63.5413 | 0.835 | 0.3042 | 1.1494 | 0.835 | 0.8188 | 0.1571 | 0.0461 | | 61.4257 | 97.0 | 2425 | 63.5387 | 0.835 | 0.3047 | 1.1489 | 0.835 | 0.8188 | 0.1652 | 0.0461 | | 61.4257 | 98.0 | 2450 | 63.5383 | 0.84 | 0.3046 | 1.1503 | 0.8400 | 0.8244 | 0.1568 | 0.0458 | | 61.4257 | 99.0 | 2475 | 63.5374 | 0.835 | 0.3045 | 1.1489 | 0.835 | 0.8188 | 0.1570 | 0.0456 | | 61.3919 | 100.0 | 2500 | 63.5396 | 0.84 | 0.3043 | 1.1495 | 0.8400 | 0.8244 | 0.1568 | 0.0457 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/39-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 39-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5285 - Accuracy: 0.81 - Brier Loss: 0.3907 - Nll: 0.9159 - F1 Micro: 0.81 - F1 Macro: 0.7907 - Ece: 0.3421 - Aurc: 0.0542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.8936 | 0.11 | 1.0097 | 8.5078 | 0.11 | 0.0902 | 0.3251 | 0.8953 | | No log | 2.0 | 14 | 1.2597 | 0.16 | 0.8753 | 5.5353 | 0.16 | 0.1308 | 0.2539 | 0.7902 | | No log | 3.0 | 21 | 1.0230 | 0.355 | 0.7833 | 5.1396 | 0.3550 | 0.2872 | 0.2810 | 0.4521 | | No log | 4.0 | 28 | 0.8743 | 0.545 | 0.6497 | 3.0412 | 0.545 | 0.4328 | 0.3224 | 0.2775 | | No log | 5.0 | 35 | 0.8020 | 0.625 | 0.5958 | 2.6099 | 0.625 | 0.5465 | 0.3186 | 0.2136 | | No log | 6.0 | 42 | 0.7221 | 0.675 | 0.5300 | 2.3085 | 0.675 | 0.5632 | 0.3257 | 0.1562 | | No log | 7.0 | 49 | 0.6964 | 0.68 | 0.4843 | 1.9033 | 0.68 | 0.5761 | 0.3039 | 0.1453 | | No log | 8.0 | 56 | 0.6729 | 0.72 | 0.4598 | 1.8200 | 0.72 | 0.6195 | 0.3089 | 0.1170 | | No log | 9.0 | 63 | 0.6470 | 0.77 | 0.4318 | 1.5607 | 0.7700 | 0.7058 | 0.3518 | 0.0897 | | No log | 10.0 | 70 | 0.5889 | 0.795 | 0.4019 | 1.1238 | 0.795 | 0.7546 | 0.3324 | 0.0675 | | No log | 11.0 | 77 | 0.5829 | 0.795 | 0.4013 | 1.0267 | 0.795 | 0.7667 | 0.3142 | 0.0728 | | No log | 12.0 | 84 | 0.5763 | 0.785 | 0.3923 | 1.2697 | 0.785 | 0.7655 | 0.3286 | 0.0751 | | No log | 13.0 | 91 | 0.5854 | 0.765 | 0.3934 | 1.4915 | 0.765 | 0.7291 | 0.2936 | 0.0806 | | No log | 14.0 | 98 | 0.5779 | 0.795 | 0.3983 | 1.2207 | 0.795 | 0.7409 | 0.3141 | 0.0681 | | No log | 15.0 | 105 | 0.5564 | 0.795 | 0.3752 | 1.1974 | 0.795 | 0.7687 | 0.3201 | 0.0626 | | No log | 16.0 | 112 | 0.5599 | 0.815 | 0.3945 | 1.0987 | 0.815 | 0.7827 | 0.3233 | 0.0618 | | No log | 17.0 | 119 | 0.5748 | 0.77 | 0.4001 | 1.2395 | 0.7700 | 0.7497 | 0.3136 | 0.0866 | | No log | 18.0 | 126 | 0.5611 | 0.79 | 0.4028 | 1.3279 | 0.79 | 0.7738 | 0.3127 | 0.0680 | | No log | 19.0 | 133 | 0.5514 | 0.805 | 0.4063 | 0.8598 | 0.805 | 0.7873 | 0.3656 | 0.0575 | | No log | 20.0 | 140 | 0.5566 | 0.81 | 0.4028 | 0.9944 | 0.81 | 0.7943 | 0.3449 | 0.0676 | | No log | 21.0 | 147 | 0.5489 | 0.81 | 0.3879 | 1.1351 | 0.81 | 0.7966 | 0.3432 | 0.0682 | | No log | 22.0 | 154 | 0.5586 | 0.82 | 0.4091 | 1.1107 | 0.82 | 0.7894 | 0.3526 | 0.0580 | | No log | 23.0 | 161 | 0.5593 | 0.795 | 0.4131 | 1.1693 | 0.795 | 0.7765 | 0.3483 | 0.0641 | | No log | 24.0 | 168 | 0.5493 | 0.79 | 0.3962 | 1.2363 | 0.79 | 0.7740 | 0.3494 | 0.0646 | | No log | 25.0 | 175 | 0.5489 | 0.8 | 0.3930 | 1.0310 | 0.8000 | 0.7638 | 0.3342 | 0.0614 | | No log | 26.0 | 182 | 0.5492 | 0.79 | 0.3944 | 1.3201 | 0.79 | 0.7670 | 0.3096 | 0.0667 | | No log | 27.0 | 189 | 0.5441 | 0.805 | 0.4002 | 1.1304 | 0.805 | 0.7886 | 0.3528 | 0.0600 | | No log | 28.0 | 196 | 0.5397 | 0.815 | 0.3960 | 1.1210 | 0.815 | 0.7902 | 0.3630 | 0.0544 | | No log | 29.0 | 203 | 0.5418 | 0.785 | 0.3977 | 0.9580 | 0.785 | 0.7575 | 0.3536 | 0.0646 | | No log | 30.0 | 210 | 0.5374 | 0.815 | 0.3931 | 1.0186 | 0.815 | 0.7855 | 0.3422 | 0.0604 | | No log | 31.0 | 217 | 0.5405 | 0.815 | 0.3983 | 0.8948 | 0.815 | 0.7980 | 0.3671 | 0.0531 | | No log | 32.0 | 224 | 0.5394 | 0.805 | 0.3998 | 1.0680 | 0.805 | 0.7841 | 0.3695 | 0.0568 | | No log | 33.0 | 231 | 0.5296 | 0.81 | 0.3868 | 1.1222 | 0.81 | 0.7891 | 0.3530 | 0.0545 | | No log | 34.0 | 238 | 0.5338 | 0.81 | 0.3952 | 1.1333 | 0.81 | 0.7825 | 0.3453 | 0.0559 | | No log | 35.0 | 245 | 0.5339 | 0.805 | 0.3941 | 0.8600 | 0.805 | 0.7905 | 0.3552 | 0.0554 | | No log | 36.0 | 252 | 0.5332 | 0.81 | 0.3918 | 0.9018 | 0.81 | 0.7996 | 0.3669 | 0.0527 | | No log | 37.0 | 259 | 0.5336 | 0.79 | 0.3907 | 0.7768 | 0.79 | 0.7612 | 0.3374 | 0.0611 | | No log | 38.0 | 266 | 0.5327 | 0.805 | 0.3906 | 0.9987 | 0.805 | 0.7750 | 0.3430 | 0.0564 | | No log | 39.0 | 273 | 0.5342 | 0.805 | 0.3898 | 1.1024 | 0.805 | 0.7837 | 0.3295 | 0.0563 | | No log | 40.0 | 280 | 0.5310 | 0.81 | 0.3906 | 0.8426 | 0.81 | 0.7820 | 0.3513 | 0.0556 | | No log | 41.0 | 287 | 0.5327 | 0.81 | 0.3950 | 1.0952 | 0.81 | 0.7927 | 0.3418 | 0.0570 | | No log | 42.0 | 294 | 0.5305 | 0.82 | 0.3961 | 0.7830 | 0.82 | 0.8011 | 0.3501 | 0.0545 | | No log | 43.0 | 301 | 0.5308 | 0.81 | 0.3926 | 0.9752 | 0.81 | 0.7907 | 0.3534 | 0.0573 | | No log | 44.0 | 308 | 0.5287 | 0.81 | 0.3898 | 0.9838 | 0.81 | 0.7904 | 0.3454 | 0.0570 | | No log | 45.0 | 315 | 0.5270 | 0.815 | 0.3890 | 0.8682 | 0.815 | 0.8004 | 0.3499 | 0.0543 | | No log | 46.0 | 322 | 0.5272 | 0.81 | 0.3884 | 0.9784 | 0.81 | 0.7827 | 0.3415 | 0.0541 | | No log | 47.0 | 329 | 0.5306 | 0.805 | 0.3900 | 1.1153 | 0.805 | 0.7800 | 0.3388 | 0.0571 | | No log | 48.0 | 336 | 0.5288 | 0.82 | 0.3915 | 0.9916 | 0.82 | 0.7912 | 0.3519 | 0.0527 | | No log | 49.0 | 343 | 0.5274 | 0.81 | 0.3886 | 0.8415 | 0.81 | 0.7855 | 0.3524 | 0.0550 | | No log | 50.0 | 350 | 0.5264 | 0.81 | 0.3868 | 0.9713 | 0.81 | 0.7907 | 0.3408 | 0.0559 | | No log | 51.0 | 357 | 0.5295 | 0.815 | 0.3916 | 1.0340 | 0.815 | 0.7933 | 0.3683 | 0.0536 | | No log | 52.0 | 364 | 0.5294 | 0.81 | 0.3920 | 0.9178 | 0.81 | 0.7854 | 0.3499 | 0.0563 | | No log | 53.0 | 371 | 0.5283 | 0.81 | 0.3912 | 0.8517 | 0.81 | 0.7907 | 0.3648 | 0.0540 | | No log | 54.0 | 378 | 0.5301 | 0.815 | 0.3927 | 0.9279 | 0.815 | 0.7933 | 0.3579 | 0.0558 | | No log | 55.0 | 385 | 0.5275 | 0.805 | 0.3888 | 0.9225 | 0.805 | 0.7800 | 0.3406 | 0.0553 | | No log | 56.0 | 392 | 0.5284 | 0.815 | 0.3903 | 0.9064 | 0.815 | 0.7933 | 0.3463 | 0.0551 | | No log | 57.0 | 399 | 0.5261 | 0.81 | 0.3872 | 0.9072 | 0.81 | 0.7907 | 0.3527 | 0.0551 | | No log | 58.0 | 406 | 0.5278 | 0.815 | 0.3900 | 0.8469 | 0.815 | 0.7966 | 0.3622 | 0.0526 | | No log | 59.0 | 413 | 0.5280 | 0.81 | 0.3900 | 0.9220 | 0.81 | 0.7907 | 0.3467 | 0.0551 | | No log | 60.0 | 420 | 0.5296 | 0.81 | 0.3932 | 0.9166 | 0.81 | 0.7907 | 0.3620 | 0.0555 | | No log | 61.0 | 427 | 0.5288 | 0.815 | 0.3925 | 0.8647 | 0.815 | 0.7966 | 0.3491 | 0.0529 | | No log | 62.0 | 434 | 0.5288 | 0.81 | 0.3909 | 0.9205 | 0.81 | 0.7907 | 0.3482 | 0.0552 | | No log | 63.0 | 441 | 0.5274 | 0.81 | 0.3889 | 0.9143 | 0.81 | 0.7907 | 0.3457 | 0.0541 | | No log | 64.0 | 448 | 0.5283 | 0.81 | 0.3905 | 0.9141 | 0.81 | 0.7907 | 0.3578 | 0.0549 | | No log | 65.0 | 455 | 0.5283 | 0.81 | 0.3907 | 0.9177 | 0.81 | 0.7907 | 0.3536 | 0.0548 | | No log | 66.0 | 462 | 0.5289 | 0.81 | 0.3912 | 0.9179 | 0.81 | 0.7907 | 0.3502 | 0.0550 | | No log | 67.0 | 469 | 0.5282 | 0.81 | 0.3903 | 0.9134 | 0.81 | 0.7907 | 0.3511 | 0.0547 | | No log | 68.0 | 476 | 0.5279 | 0.81 | 0.3901 | 0.9105 | 0.81 | 0.7907 | 0.3473 | 0.0541 | | No log | 69.0 | 483 | 0.5283 | 0.81 | 0.3907 | 0.9128 | 0.81 | 0.7907 | 0.3558 | 0.0539 | | No log | 70.0 | 490 | 0.5283 | 0.81 | 0.3904 | 0.9191 | 0.81 | 0.7907 | 0.3414 | 0.0543 | | No log | 71.0 | 497 | 0.5284 | 0.81 | 0.3905 | 0.9183 | 0.81 | 0.7907 | 0.3478 | 0.0546 | | 0.3962 | 72.0 | 504 | 0.5285 | 0.81 | 0.3909 | 0.9151 | 0.81 | 0.7907 | 0.3415 | 0.0545 | | 0.3962 | 73.0 | 511 | 0.5283 | 0.81 | 0.3906 | 0.9144 | 0.81 | 0.7907 | 0.3499 | 0.0542 | | 0.3962 | 74.0 | 518 | 0.5282 | 0.81 | 0.3903 | 0.9146 | 0.81 | 0.7907 | 0.3411 | 0.0541 | | 0.3962 | 75.0 | 525 | 0.5284 | 0.81 | 0.3909 | 0.9159 | 0.81 | 0.7907 | 0.3571 | 0.0542 | | 0.3962 | 76.0 | 532 | 0.5284 | 0.81 | 0.3906 | 0.9155 | 0.81 | 0.7907 | 0.3361 | 0.0543 | | 0.3962 | 77.0 | 539 | 0.5283 | 0.81 | 0.3906 | 0.9159 | 0.81 | 0.7907 | 0.3480 | 0.0541 | | 0.3962 | 78.0 | 546 | 0.5282 | 0.81 | 0.3905 | 0.9120 | 0.81 | 0.7907 | 0.3413 | 0.0540 | | 0.3962 | 79.0 | 553 | 0.5283 | 0.81 | 0.3905 | 0.9162 | 0.81 | 0.7907 | 0.3412 | 0.0542 | | 0.3962 | 80.0 | 560 | 0.5285 | 0.81 | 0.3907 | 0.9189 | 0.81 | 0.7907 | 0.3361 | 0.0543 | | 0.3962 | 81.0 | 567 | 0.5285 | 0.81 | 0.3907 | 0.9162 | 0.81 | 0.7907 | 0.3470 | 0.0541 | | 0.3962 | 82.0 | 574 | 0.5283 | 0.81 | 0.3904 | 0.9144 | 0.81 | 0.7907 | 0.3411 | 0.0540 | | 0.3962 | 83.0 | 581 | 0.5284 | 0.81 | 0.3906 | 0.9153 | 0.81 | 0.7907 | 0.3361 | 0.0542 | | 0.3962 | 84.0 | 588 | 0.5284 | 0.81 | 0.3907 | 0.9151 | 0.81 | 0.7907 | 0.3419 | 0.0542 | | 0.3962 | 85.0 | 595 | 0.5283 | 0.81 | 0.3905 | 0.9143 | 0.81 | 0.7907 | 0.3362 | 0.0541 | | 0.3962 | 86.0 | 602 | 0.5285 | 0.81 | 0.3908 | 0.9152 | 0.81 | 0.7907 | 0.3418 | 0.0540 | | 0.3962 | 87.0 | 609 | 0.5284 | 0.81 | 0.3907 | 0.9156 | 0.81 | 0.7907 | 0.3365 | 0.0543 | | 0.3962 | 88.0 | 616 | 0.5285 | 0.81 | 0.3907 | 0.9155 | 0.81 | 0.7907 | 0.3419 | 0.0541 | | 0.3962 | 89.0 | 623 | 0.5284 | 0.81 | 0.3906 | 0.9154 | 0.81 | 0.7907 | 0.3360 | 0.0541 | | 0.3962 | 90.0 | 630 | 0.5285 | 0.81 | 0.3907 | 0.9168 | 0.81 | 0.7907 | 0.3418 | 0.0543 | | 0.3962 | 91.0 | 637 | 0.5285 | 0.81 | 0.3907 | 0.9160 | 0.81 | 0.7907 | 0.3420 | 0.0543 | | 0.3962 | 92.0 | 644 | 0.5285 | 0.81 | 0.3908 | 0.9164 | 0.81 | 0.7907 | 0.3421 | 0.0541 | | 0.3962 | 93.0 | 651 | 0.5285 | 0.81 | 0.3907 | 0.9164 | 0.81 | 0.7907 | 0.3473 | 0.0542 | | 0.3962 | 94.0 | 658 | 0.5285 | 0.81 | 0.3907 | 0.9164 | 0.81 | 0.7907 | 0.3420 | 0.0542 | | 0.3962 | 95.0 | 665 | 0.5285 | 0.81 | 0.3907 | 0.9161 | 0.81 | 0.7907 | 0.3473 | 0.0541 | | 0.3962 | 96.0 | 672 | 0.5285 | 0.81 | 0.3907 | 0.9157 | 0.81 | 0.7907 | 0.3421 | 0.0542 | | 0.3962 | 97.0 | 679 | 0.5285 | 0.81 | 0.3907 | 0.9154 | 0.81 | 0.7907 | 0.3363 | 0.0542 | | 0.3962 | 98.0 | 686 | 0.5285 | 0.81 | 0.3907 | 0.9164 | 0.81 | 0.7907 | 0.3420 | 0.0542 | | 0.3962 | 99.0 | 693 | 0.5285 | 0.81 | 0.3907 | 0.9162 | 0.81 | 0.7907 | 0.3420 | 0.0542 | | 0.3962 | 100.0 | 700 | 0.5285 | 0.81 | 0.3907 | 0.9159 | 0.81 | 0.7907 | 0.3421 | 0.0542 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/39-tiny_tobacco3482

# 39-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0652 - Accuracy: 0.68 - Brier Loss: 0.8297 - Nll: 1.9403 - F1 Micro: 0.68 - F1 Macro: 0.5899 - Ece: 0.5728 - Aurc: 0.1321 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.0877 | 0.11 | 0.8983 | 8.4701 | 0.11 | 0.0377 | 0.1765 | 0.9065 | | No log | 2.0 | 50 | 0.0811 | 0.09 | 0.8984 | 6.6861 | 0.09 | 0.0364 | 0.1641 | 0.9033 | | No log | 3.0 | 75 | 0.0803 | 0.165 | 0.8981 | 7.2209 | 0.165 | 0.0520 | 0.2115 | 0.7480 | | No log | 4.0 | 100 | 0.0799 | 0.17 | 0.8977 | 7.1709 | 0.17 | 0.0713 | 0.2184 | 0.6439 | | No log | 5.0 | 125 | 0.0794 | 0.15 | 0.8970 | 7.1655 | 0.15 | 0.0553 | 0.2075 | 0.6278 | | No log | 6.0 | 150 | 0.0786 | 0.225 | 0.8959 | 6.1378 | 0.225 | 0.1083 | 0.2511 | 0.5827 | | No log | 7.0 | 175 | 0.0777 | 0.275 | 0.8944 | 5.1450 | 0.275 | 0.1053 | 0.2840 | 0.5127 | | No log | 8.0 | 200 | 0.0767 | 0.305 | 0.8920 | 5.0689 | 0.305 | 0.1177 | 0.3095 | 0.4372 | | No log | 9.0 | 225 | 0.0756 | 0.305 | 0.8893 | 4.6689 | 0.305 | 0.1015 | 0.3114 | 0.4279 | | No log | 10.0 | 250 | 0.0747 | 0.325 | 0.8865 | 5.1088 | 0.325 | 0.1067 | 0.3257 | 0.3891 | | No log | 11.0 | 275 | 0.0738 | 0.345 | 0.8803 | 4.9911 | 0.345 | 0.1297 | 0.3418 | 0.3672 | | No log | 12.0 | 300 | 0.0729 | 0.405 | 0.8748 | 4.7892 | 0.405 | 0.1649 | 0.3774 | 0.3276 | | No log | 13.0 | 325 | 0.0721 | 0.395 | 0.8696 | 4.6346 | 0.395 | 0.1561 | 0.3723 | 0.3221 | | No log | 14.0 | 350 | 0.0713 | 0.44 | 0.8676 | 3.8051 | 0.44 | 0.1780 | 0.4047 | 0.2743 | | No log | 15.0 | 375 | 0.0705 | 0.43 | 0.8612 | 3.6440 | 0.4300 | 0.1893 | 0.3994 | 0.2701 | | No log | 16.0 | 400 | 0.0698 | 0.48 | 0.8569 | 3.4952 | 0.48 | 0.2607 | 0.4216 | 0.2640 | | No log | 17.0 | 425 | 0.0690 | 0.54 | 0.8533 | 3.2018 | 0.54 | 0.3042 | 0.4771 | 0.2168 | | No log | 18.0 | 450 | 0.0683 | 0.54 | 0.8500 | 2.7042 | 0.54 | 0.3161 | 0.4677 | 0.2075 | | No log | 19.0 | 475 | 0.0679 | 0.6 | 0.8469 | 2.5343 | 0.6 | 0.4246 | 0.5186 | 0.1865 | | 0.0767 | 20.0 | 500 | 0.0677 | 0.615 | 0.8455 | 2.6485 | 0.615 | 0.4407 | 0.5207 | 0.1908 | | 0.0767 | 21.0 | 525 | 0.0673 | 0.625 | 0.8463 | 2.2841 | 0.625 | 0.4471 | 0.5369 | 0.1558 | | 0.0767 | 22.0 | 550 | 0.0667 | 0.645 | 0.8398 | 2.4032 | 0.645 | 0.4633 | 0.5462 | 0.1500 | | 0.0767 | 23.0 | 575 | 0.0664 | 0.63 | 0.8388 | 2.4376 | 0.63 | 0.4600 | 0.5356 | 0.1583 | | 0.0767 | 24.0 | 600 | 0.0663 | 0.645 | 0.8371 | 2.3057 | 0.645 | 0.4731 | 0.5443 | 0.1437 | | 0.0767 | 25.0 | 625 | 0.0662 | 0.635 | 0.8386 | 2.2486 | 0.635 | 0.4606 | 0.5425 | 0.1515 | | 0.0767 | 26.0 | 650 | 0.0661 | 0.63 | 0.8374 | 2.2367 | 0.63 | 0.4543 | 0.5423 | 0.1549 | | 0.0767 | 27.0 | 675 | 0.0660 | 0.64 | 0.8358 | 2.1278 | 0.64 | 0.4554 | 0.5486 | 0.1350 | | 0.0767 | 28.0 | 700 | 0.0660 | 0.64 | 0.8360 | 2.2416 | 0.64 | 0.4726 | 0.5363 | 0.1429 | | 0.0767 | 29.0 | 725 | 0.0660 | 0.67 | 0.8364 | 2.1574 | 0.67 | 0.4990 | 0.5648 | 0.1264 | | 0.0767 | 30.0 | 750 | 0.0659 | 0.665 | 0.8357 | 2.2015 | 0.665 | 0.5113 | 0.5645 | 0.1383 | | 0.0767 | 31.0 | 775 | 0.0658 | 0.65 | 0.8347 | 2.1367 | 0.65 | 0.4995 | 0.5522 | 0.1461 | | 0.0767 | 32.0 | 800 | 0.0656 | 0.67 | 0.8341 | 2.1025 | 0.67 | 0.5110 | 0.5666 | 0.1307 | | 0.0767 | 33.0 | 825 | 0.0656 | 0.645 | 0.8354 | 2.0398 | 0.645 | 0.5034 | 0.5442 | 0.1334 | | 0.0767 | 34.0 | 850 | 0.0656 | 0.67 | 0.8346 | 2.1934 | 0.67 | 0.5112 | 0.5569 | 0.1299 | | 0.0767 | 35.0 | 875 | 0.0658 | 0.665 | 0.8353 | 2.0671 | 0.665 | 0.5255 | 0.5646 | 0.1295 | | 0.0767 | 36.0 | 900 | 0.0655 | 0.665 | 0.8320 | 2.0168 | 0.665 | 0.5138 | 0.5680 | 0.1306 | | 0.0767 | 37.0 | 925 | 0.0655 | 0.675 | 0.8315 | 2.0974 | 0.675 | 0.5229 | 0.5672 | 0.1333 | | 0.0767 | 38.0 | 950 | 0.0655 | 0.675 | 0.8341 | 2.0624 | 0.675 | 0.5457 | 0.5750 | 0.1256 | | 0.0767 | 39.0 | 975 | 0.0653 | 0.69 | 0.8321 | 2.0556 | 0.69 | 0.5498 | 0.5856 | 0.1250 | | 0.0625 | 40.0 | 1000 | 0.0653 | 0.69 | 0.8330 | 1.9627 | 0.69 | 0.5812 | 0.5765 | 0.1243 | | 0.0625 | 41.0 | 1025 | 0.0653 | 0.705 | 0.8335 | 2.0491 | 0.705 | 0.5900 | 0.5919 | 0.1155 | | 0.0625 | 42.0 | 1050 | 0.0653 | 0.705 | 0.8335 | 2.0357 | 0.705 | 0.5984 | 0.5945 | 0.1250 | | 0.0625 | 43.0 | 1075 | 0.0652 | 0.7 | 0.8316 | 2.0326 | 0.7 | 0.5957 | 0.5932 | 0.1230 | | 0.0625 | 44.0 | 1100 | 0.0653 | 0.69 | 0.8323 | 2.0244 | 0.69 | 0.5904 | 0.5911 | 0.1252 | | 0.0625 | 45.0 | 1125 | 0.0653 | 0.68 | 0.8310 | 2.0410 | 0.68 | 0.5644 | 0.5699 | 0.1305 | | 0.0625 | 46.0 | 1150 | 0.0653 | 0.695 | 0.8323 | 2.0288 | 0.695 | 0.5944 | 0.5837 | 0.1251 | | 0.0625 | 47.0 | 1175 | 0.0652 | 0.685 | 0.8312 | 1.9613 | 0.685 | 0.5894 | 0.5834 | 0.1244 | | 0.0625 | 48.0 | 1200 | 0.0652 | 0.685 | 0.8312 | 1.9620 | 0.685 | 0.5753 | 0.5728 | 0.1321 | | 0.0625 | 49.0 | 1225 | 0.0652 | 0.695 | 0.8317 | 1.9706 | 0.695 | 0.5962 | 0.5837 | 0.1291 | | 0.0625 | 50.0 | 1250 | 0.0651 | 0.69 | 0.8314 | 1.9661 | 0.69 | 0.5902 | 0.5759 | 0.1315 | | 0.0625 | 51.0 | 1275 | 0.0652 | 0.68 | 0.8319 | 1.9542 | 0.68 | 0.5695 | 0.5704 | 0.1288 | | 0.0625 | 52.0 | 1300 | 0.0651 | 0.695 | 0.8308 | 1.9577 | 0.695 | 0.5834 | 0.5823 | 0.1276 | | 0.0625 | 53.0 | 1325 | 0.0652 | 0.67 | 0.8315 | 1.8876 | 0.67 | 0.5604 | 0.5680 | 0.1326 | | 0.0625 | 54.0 | 1350 | 0.0651 | 0.68 | 0.8318 | 1.8731 | 0.68 | 0.5925 | 0.5644 | 0.1317 | | 0.0625 | 55.0 | 1375 | 0.0651 | 0.7 | 0.8292 | 1.9448 | 0.7 | 0.5856 | 0.5903 | 0.1214 | | 0.0625 | 56.0 | 1400 | 0.0652 | 0.705 | 0.8310 | 2.0042 | 0.705 | 0.6059 | 0.5881 | 0.1195 | | 0.0625 | 57.0 | 1425 | 0.0651 | 0.685 | 0.8309 | 1.9467 | 0.685 | 0.5832 | 0.5734 | 0.1273 | | 0.0625 | 58.0 | 1450 | 0.0651 | 0.705 | 0.8306 | 1.9480 | 0.705 | 0.6064 | 0.5956 | 0.1227 | | 0.0625 | 59.0 | 1475 | 0.0651 | 0.695 | 0.8302 | 1.9453 | 0.695 | 0.5998 | 0.5806 | 0.1310 | | 0.0604 | 60.0 | 1500 | 0.0651 | 0.68 | 0.8305 | 1.8892 | 0.68 | 0.5813 | 0.5643 | 0.1276 | | 0.0604 | 61.0 | 1525 | 0.0651 | 0.725 | 0.8302 | 1.9304 | 0.7250 | 0.6346 | 0.6022 | 0.1194 | | 0.0604 | 62.0 | 1550 | 0.0651 | 0.685 | 0.8303 | 1.8831 | 0.685 | 0.5773 | 0.5815 | 0.1322 | | 0.0604 | 63.0 | 1575 | 0.0650 | 0.71 | 0.8299 | 1.9502 | 0.7100 | 0.6140 | 0.5944 | 0.1257 | | 0.0604 | 64.0 | 1600 | 0.0651 | 0.68 | 0.8296 | 1.9407 | 0.68 | 0.5701 | 0.5727 | 0.1337 | | 0.0604 | 65.0 | 1625 | 0.0651 | 0.695 | 0.8309 | 1.9413 | 0.695 | 0.5995 | 0.5884 | 0.1234 | | 0.0604 | 66.0 | 1650 | 0.0651 | 0.69 | 0.8298 | 1.9474 | 0.69 | 0.5865 | 0.5723 | 0.1293 | | 0.0604 | 67.0 | 1675 | 0.0650 | 0.705 | 0.8298 | 1.8996 | 0.705 | 0.6109 | 0.5966 | 0.1258 | | 0.0604 | 68.0 | 1700 | 0.0651 | 0.7 | 0.8298 | 1.9938 | 0.7 | 0.6089 | 0.5895 | 0.1283 | | 0.0604 | 69.0 | 1725 | 0.0651 | 0.695 | 0.8296 | 1.9273 | 0.695 | 0.5923 | 0.5776 | 0.1251 | | 0.0604 | 70.0 | 1750 | 0.0651 | 0.705 | 0.8297 | 1.8920 | 0.705 | 0.6162 | 0.5868 | 0.1323 | | 0.0604 | 71.0 | 1775 | 0.0651 | 0.7 | 0.8304 | 1.9852 | 0.7 | 0.6123 | 0.5878 | 0.1282 | | 0.0604 | 72.0 | 1800 | 0.0651 | 0.68 | 0.8310 | 1.9399 | 0.68 | 0.5963 | 0.5633 | 0.1345 | | 0.0604 | 73.0 | 1825 | 0.0650 | 0.725 | 0.8302 | 1.9237 | 0.7250 | 0.6266 | 0.6029 | 0.1192 | | 0.0604 | 74.0 | 1850 | 0.0651 | 0.68 | 0.8306 | 1.9521 | 0.68 | 0.5967 | 0.5745 | 0.1342 | | 0.0604 | 75.0 | 1875 | 0.0651 | 0.695 | 0.8301 | 1.9911 | 0.695 | 0.6047 | 0.5841 | 0.1317 | | 0.0604 | 76.0 | 1900 | 0.0651 | 0.695 | 0.8299 | 1.9333 | 0.695 | 0.5935 | 0.5715 | 0.1299 | | 0.0604 | 77.0 | 1925 | 0.0651 | 0.695 | 0.8298 | 1.9429 | 0.695 | 0.6041 | 0.5679 | 0.1293 | | 0.0604 | 78.0 | 1950 | 0.0651 | 0.695 | 0.8298 | 1.9367 | 0.695 | 0.6101 | 0.5792 | 0.1279 | | 0.0604 | 79.0 | 1975 | 0.0651 | 0.695 | 0.8301 | 1.9934 | 0.695 | 0.6095 | 0.5898 | 0.1324 | | 0.0596 | 80.0 | 2000 | 0.0651 | 0.7 | 0.8297 | 1.9332 | 0.7 | 0.6071 | 0.5778 | 0.1271 | | 0.0596 | 81.0 | 2025 | 0.0651 | 0.685 | 0.8303 | 1.9457 | 0.685 | 0.5986 | 0.5807 | 0.1320 | | 0.0596 | 82.0 | 2050 | 0.0651 | 0.7 | 0.8300 | 1.9337 | 0.7 | 0.6072 | 0.5896 | 0.1296 | | 0.0596 | 83.0 | 2075 | 0.0651 | 0.685 | 0.8298 | 1.9424 | 0.685 | 0.5985 | 0.5753 | 0.1319 | | 0.0596 | 84.0 | 2100 | 0.0651 | 0.7 | 0.8297 | 1.9407 | 0.7 | 0.6116 | 0.5847 | 0.1311 | | 0.0596 | 85.0 | 2125 | 0.0651 | 0.685 | 0.8298 | 1.9364 | 0.685 | 0.5983 | 0.5841 | 0.1311 | | 0.0596 | 86.0 | 2150 | 0.0651 | 0.685 | 0.8299 | 1.9345 | 0.685 | 0.5983 | 0.5806 | 0.1318 | | 0.0596 | 87.0 | 2175 | 0.0652 | 0.685 | 0.8299 | 1.9402 | 0.685 | 0.5979 | 0.5778 | 0.1317 | | 0.0596 | 88.0 | 2200 | 0.0651 | 0.685 | 0.8298 | 1.9385 | 0.685 | 0.5983 | 0.5726 | 0.1315 | | 0.0596 | 89.0 | 2225 | 0.0652 | 0.68 | 0.8296 | 1.9367 | 0.68 | 0.5899 | 0.5732 | 0.1314 | | 0.0596 | 90.0 | 2250 | 0.0652 | 0.68 | 0.8298 | 1.9383 | 0.68 | 0.5896 | 0.5782 | 0.1321 | | 0.0596 | 91.0 | 2275 | 0.0652 | 0.68 | 0.8297 | 1.9408 | 0.68 | 0.5896 | 0.5782 | 0.1317 | | 0.0596 | 92.0 | 2300 | 0.0652 | 0.68 | 0.8299 | 1.9370 | 0.68 | 0.5899 | 0.5701 | 0.1320 | | 0.0596 | 93.0 | 2325 | 0.0652 | 0.68 | 0.8298 | 1.9395 | 0.68 | 0.5899 | 0.5754 | 0.1321 | | 0.0596 | 94.0 | 2350 | 0.0652 | 0.68 | 0.8297 | 1.9392 | 0.68 | 0.5899 | 0.5701 | 0.1326 | | 0.0596 | 95.0 | 2375 | 0.0652 | 0.68 | 0.8297 | 1.9393 | 0.68 | 0.5899 | 0.5651 | 0.1320 | | 0.0596 | 96.0 | 2400 | 0.0652 | 0.68 | 0.8297 | 1.9397 | 0.68 | 0.5899 | 0.5701 | 0.1321 | | 0.0596 | 97.0 | 2425 | 0.0652 | 0.68 | 0.8297 | 1.9400 | 0.68 | 0.5899 | 0.5676 | 0.1322 | | 0.0596 | 98.0 | 2450 | 0.0652 | 0.68 | 0.8297 | 1.9391 | 0.68 | 0.5899 | 0.5677 | 0.1320 | | 0.0596 | 99.0 | 2475 | 0.0652 | 0.68 | 0.8297 | 1.9397 | 0.68 | 0.5899 | 0.5701 | 0.1321 | | 0.0592 | 100.0 | 2500 | 0.0652 | 0.68 | 0.8297 | 1.9403 | 0.68 | 0.5899 | 0.5728 | 0.1321 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/39-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# 39-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0812 - Accuracy: 0.835 - Brier Loss: 0.2748 - Nll: 1.2215 - F1 Micro: 0.835 - F1 Macro: 0.8213 - Ece: 0.1443 - Aurc: 0.0548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 5.1938 | 0.095 | 1.0201 | 8.6917 | 0.095 | 0.0778 | 0.3242 | 0.9009 | | No log | 2.0 | 14 | 4.3129 | 0.13 | 0.9109 | 8.2379 | 0.13 | 0.0910 | 0.2544 | 0.8466 | | No log | 3.0 | 21 | 3.9690 | 0.225 | 0.8600 | 6.8547 | 0.225 | 0.1401 | 0.2626 | 0.6398 | | No log | 4.0 | 28 | 3.8651 | 0.375 | 0.7978 | 5.6610 | 0.375 | 0.2964 | 0.3198 | 0.4692 | | No log | 5.0 | 35 | 3.8115 | 0.465 | 0.7222 | 3.4731 | 0.465 | 0.3435 | 0.3007 | 0.3464 | | No log | 6.0 | 42 | 3.7351 | 0.575 | 0.6691 | 2.6672 | 0.575 | 0.4736 | 0.3509 | 0.2284 | | No log | 7.0 | 49 | 3.6913 | 0.62 | 0.6152 | 2.6026 | 0.62 | 0.4700 | 0.3255 | 0.1827 | | No log | 8.0 | 56 | 3.6687 | 0.68 | 0.5820 | 1.9726 | 0.68 | 0.5400 | 0.3735 | 0.1472 | | No log | 9.0 | 63 | 3.6771 | 0.645 | 0.5464 | 1.9938 | 0.645 | 0.5211 | 0.3013 | 0.1595 | | No log | 10.0 | 70 | 3.6759 | 0.685 | 0.4884 | 1.9735 | 0.685 | 0.5678 | 0.2672 | 0.1278 | | No log | 11.0 | 77 | 3.6587 | 0.71 | 0.4696 | 2.0625 | 0.7100 | 0.6080 | 0.2956 | 0.1115 | | No log | 12.0 | 84 | 3.6317 | 0.72 | 0.4121 | 2.2088 | 0.72 | 0.6137 | 0.2372 | 0.0925 | | No log | 13.0 | 91 | 3.6799 | 0.745 | 0.4167 | 2.0639 | 0.745 | 0.6372 | 0.2480 | 0.0978 | | No log | 14.0 | 98 | 3.6191 | 0.745 | 0.3850 | 1.9955 | 0.745 | 0.6384 | 0.2363 | 0.0728 | | No log | 15.0 | 105 | 3.6813 | 0.715 | 0.3814 | 2.0731 | 0.715 | 0.6026 | 0.1995 | 0.0918 | | No log | 16.0 | 112 | 3.6394 | 0.75 | 0.3644 | 1.9093 | 0.75 | 0.6492 | 0.1904 | 0.0777 | | No log | 17.0 | 119 | 3.7661 | 0.735 | 0.3786 | 1.5402 | 0.735 | 0.6352 | 0.2032 | 0.0982 | | No log | 18.0 | 126 | 3.6849 | 0.79 | 0.3369 | 1.8761 | 0.79 | 0.6965 | 0.1954 | 0.0708 | | No log | 19.0 | 133 | 3.6776 | 0.775 | 0.3358 | 1.4981 | 0.775 | 0.7021 | 0.1919 | 0.0744 | | No log | 20.0 | 140 | 3.6814 | 0.755 | 0.3546 | 1.5225 | 0.755 | 0.6873 | 0.1840 | 0.0794 | | No log | 21.0 | 147 | 3.6948 | 0.775 | 0.3267 | 1.4776 | 0.775 | 0.7052 | 0.1630 | 0.0710 | | No log | 22.0 | 154 | 3.7210 | 0.795 | 0.3191 | 1.3634 | 0.795 | 0.7383 | 0.1737 | 0.0705 | | No log | 23.0 | 161 | 3.7231 | 0.805 | 0.3062 | 1.3141 | 0.805 | 0.7679 | 0.1629 | 0.0665 | | No log | 24.0 | 168 | 3.7322 | 0.815 | 0.2903 | 1.2030 | 0.815 | 0.7771 | 0.1789 | 0.0609 | | No log | 25.0 | 175 | 3.7237 | 0.815 | 0.3020 | 1.1721 | 0.815 | 0.7947 | 0.1759 | 0.0603 | | No log | 26.0 | 182 | 3.8243 | 0.8 | 0.3138 | 1.3356 | 0.8000 | 0.7699 | 0.1735 | 0.0720 | | No log | 27.0 | 189 | 3.7675 | 0.81 | 0.3038 | 1.2662 | 0.81 | 0.7853 | 0.1891 | 0.0699 | | No log | 28.0 | 196 | 3.8006 | 0.81 | 0.2992 | 1.3422 | 0.81 | 0.7805 | 0.1709 | 0.0698 | | No log | 29.0 | 203 | 3.7783 | 0.815 | 0.3009 | 1.3322 | 0.815 | 0.7959 | 0.1729 | 0.0669 | | No log | 30.0 | 210 | 3.7547 | 0.835 | 0.2775 | 0.9761 | 0.835 | 0.8228 | 0.1751 | 0.0566 | | No log | 31.0 | 217 | 3.7810 | 0.82 | 0.2905 | 1.1472 | 0.82 | 0.7953 | 0.1670 | 0.0631 | | No log | 32.0 | 224 | 3.7935 | 0.82 | 0.2732 | 1.2016 | 0.82 | 0.7967 | 0.1429 | 0.0590 | | No log | 33.0 | 231 | 3.7871 | 0.83 | 0.2774 | 1.2459 | 0.83 | 0.8134 | 0.1495 | 0.0562 | | No log | 34.0 | 238 | 3.7689 | 0.815 | 0.2756 | 1.1135 | 0.815 | 0.7825 | 0.1609 | 0.0596 | | No log | 35.0 | 245 | 3.8169 | 0.81 | 0.2801 | 1.2621 | 0.81 | 0.7880 | 0.1570 | 0.0624 | | No log | 36.0 | 252 | 3.7973 | 0.82 | 0.2729 | 1.1310 | 0.82 | 0.7894 | 0.1466 | 0.0585 | | No log | 37.0 | 259 | 3.8560 | 0.835 | 0.2825 | 1.3222 | 0.835 | 0.8114 | 0.1466 | 0.0606 | | No log | 38.0 | 266 | 3.8351 | 0.83 | 0.2892 | 1.2548 | 0.83 | 0.8178 | 0.1489 | 0.0593 | | No log | 39.0 | 273 | 3.8258 | 0.82 | 0.2711 | 1.1900 | 0.82 | 0.8037 | 0.1455 | 0.0589 | | No log | 40.0 | 280 | 3.8288 | 0.815 | 0.2840 | 1.2167 | 0.815 | 0.7913 | 0.1574 | 0.0619 | | No log | 41.0 | 287 | 3.8264 | 0.82 | 0.2790 | 1.1737 | 0.82 | 0.8020 | 0.1394 | 0.0609 | | No log | 42.0 | 294 | 3.8276 | 0.81 | 0.2797 | 1.1603 | 0.81 | 0.7888 | 0.1585 | 0.0580 | | No log | 43.0 | 301 | 3.8554 | 0.815 | 0.2771 | 1.1695 | 0.815 | 0.7943 | 0.1310 | 0.0594 | | No log | 44.0 | 308 | 3.8405 | 0.825 | 0.2768 | 1.1593 | 0.825 | 0.8149 | 0.1413 | 0.0569 | | No log | 45.0 | 315 | 3.8640 | 0.815 | 0.2891 | 1.1752 | 0.815 | 0.7980 | 0.1516 | 0.0590 | | No log | 46.0 | 322 | 3.8624 | 0.825 | 0.2653 | 1.1548 | 0.825 | 0.8024 | 0.1384 | 0.0581 | | No log | 47.0 | 329 | 3.8546 | 0.83 | 0.2766 | 1.1634 | 0.83 | 0.8106 | 0.1411 | 0.0594 | | No log | 48.0 | 336 | 3.8652 | 0.82 | 0.2805 | 1.1651 | 0.82 | 0.8069 | 0.1278 | 0.0581 | | No log | 49.0 | 343 | 3.8716 | 0.83 | 0.2758 | 1.1895 | 0.83 | 0.8065 | 0.1486 | 0.0590 | | No log | 50.0 | 350 | 3.8720 | 0.815 | 0.2737 | 1.1709 | 0.815 | 0.7937 | 0.1375 | 0.0578 | | No log | 51.0 | 357 | 3.8812 | 0.82 | 0.2762 | 1.2348 | 0.82 | 0.7993 | 0.1292 | 0.0600 | | No log | 52.0 | 364 | 3.8844 | 0.805 | 0.2815 | 1.0870 | 0.805 | 0.7843 | 0.1525 | 0.0581 | | No log | 53.0 | 371 | 3.8968 | 0.825 | 0.2704 | 1.2235 | 0.825 | 0.8011 | 0.1452 | 0.0582 | | No log | 54.0 | 378 | 3.8996 | 0.81 | 0.2788 | 1.3264 | 0.81 | 0.7909 | 0.1453 | 0.0573 | | No log | 55.0 | 385 | 3.9037 | 0.81 | 0.2757 | 1.2231 | 0.81 | 0.7928 | 0.1307 | 0.0574 | | No log | 56.0 | 392 | 3.9024 | 0.81 | 0.2775 | 1.2369 | 0.81 | 0.7869 | 0.1493 | 0.0581 | | No log | 57.0 | 399 | 3.8951 | 0.83 | 0.2722 | 1.2151 | 0.83 | 0.8171 | 0.1491 | 0.0556 | | No log | 58.0 | 406 | 3.9224 | 0.82 | 0.2741 | 1.2957 | 0.82 | 0.8001 | 0.1351 | 0.0575 | | No log | 59.0 | 413 | 3.9397 | 0.805 | 0.2782 | 1.3017 | 0.805 | 0.7870 | 0.1342 | 0.0584 | | No log | 60.0 | 420 | 3.9250 | 0.835 | 0.2721 | 1.2251 | 0.835 | 0.8151 | 0.1466 | 0.0570 | | No log | 61.0 | 427 | 3.9381 | 0.825 | 0.2753 | 1.2330 | 0.825 | 0.8044 | 0.1384 | 0.0577 | | No log | 62.0 | 434 | 3.9475 | 0.82 | 0.2759 | 1.2171 | 0.82 | 0.8054 | 0.1485 | 0.0576 | | No log | 63.0 | 441 | 3.9591 | 0.83 | 0.2761 | 1.2299 | 0.83 | 0.8122 | 0.1551 | 0.0568 | | No log | 64.0 | 448 | 3.9496 | 0.835 | 0.2709 | 1.2282 | 0.835 | 0.8223 | 0.1397 | 0.0559 | | No log | 65.0 | 455 | 3.9360 | 0.83 | 0.2688 | 1.2238 | 0.83 | 0.8171 | 0.1384 | 0.0535 | | No log | 66.0 | 462 | 3.9594 | 0.835 | 0.2733 | 1.2395 | 0.835 | 0.8094 | 0.1540 | 0.0563 | | No log | 67.0 | 469 | 3.9648 | 0.84 | 0.2700 | 1.2154 | 0.8400 | 0.8252 | 0.1673 | 0.0557 | | No log | 68.0 | 476 | 3.9725 | 0.83 | 0.2712 | 1.2297 | 0.83 | 0.8171 | 0.1248 | 0.0552 | | No log | 69.0 | 483 | 3.9844 | 0.835 | 0.2719 | 1.2243 | 0.835 | 0.8151 | 0.1605 | 0.0557 | | No log | 70.0 | 490 | 3.9845 | 0.83 | 0.2699 | 1.2288 | 0.83 | 0.8100 | 0.1223 | 0.0553 | | No log | 71.0 | 497 | 3.9986 | 0.835 | 0.2729 | 1.2206 | 0.835 | 0.8223 | 0.1381 | 0.0556 | | 3.4116 | 72.0 | 504 | 3.9973 | 0.835 | 0.2727 | 1.2242 | 0.835 | 0.8223 | 0.1446 | 0.0553 | | 3.4116 | 73.0 | 511 | 4.0092 | 0.835 | 0.2733 | 1.2226 | 0.835 | 0.8223 | 0.1482 | 0.0554 | | 3.4116 | 74.0 | 518 | 4.0072 | 0.83 | 0.2714 | 1.2248 | 0.83 | 0.8152 | 0.1219 | 0.0549 | | 3.4116 | 75.0 | 525 | 4.0168 | 0.835 | 0.2742 | 1.2200 | 0.835 | 0.8223 | 0.1329 | 0.0551 | | 3.4116 | 76.0 | 532 | 4.0223 | 0.835 | 0.2737 | 1.2248 | 0.835 | 0.8213 | 0.1380 | 0.0552 | | 3.4116 | 77.0 | 539 | 4.0250 | 0.84 | 0.2719 | 1.2208 | 0.8400 | 0.8252 | 0.1405 | 0.0551 | | 3.4116 | 78.0 | 546 | 4.0338 | 0.835 | 0.2745 | 1.2242 | 0.835 | 0.8213 | 0.1536 | 0.0551 | | 3.4116 | 79.0 | 553 | 4.0380 | 0.835 | 0.2740 | 1.2234 | 0.835 | 0.8213 | 0.1494 | 0.0552 | | 3.4116 | 80.0 | 560 | 4.0445 | 0.835 | 0.2744 | 1.2223 | 0.835 | 0.8213 | 0.1500 | 0.0555 | | 3.4116 | 81.0 | 567 | 4.0449 | 0.835 | 0.2735 | 1.2209 | 0.835 | 0.8213 | 0.1504 | 0.0552 | | 3.4116 | 82.0 | 574 | 4.0515 | 0.835 | 0.2747 | 1.2228 | 0.835 | 0.8213 | 0.1526 | 0.0549 | | 3.4116 | 83.0 | 581 | 4.0534 | 0.835 | 0.2743 | 1.2226 | 0.835 | 0.8213 | 0.1501 | 0.0548 | | 3.4116 | 84.0 | 588 | 4.0572 | 0.835 | 0.2740 | 1.2225 | 0.835 | 0.8213 | 0.1447 | 0.0550 | | 3.4116 | 85.0 | 595 | 4.0605 | 0.835 | 0.2743 | 1.2222 | 0.835 | 0.8213 | 0.1466 | 0.0548 | | 3.4116 | 86.0 | 602 | 4.0621 | 0.835 | 0.2744 | 1.2215 | 0.835 | 0.8213 | 0.1427 | 0.0548 | | 3.4116 | 87.0 | 609 | 4.0653 | 0.835 | 0.2745 | 1.2214 | 0.835 | 0.8213 | 0.1439 | 0.0549 | | 3.4116 | 88.0 | 616 | 4.0673 | 0.835 | 0.2746 | 1.2217 | 0.835 | 0.8213 | 0.1410 | 0.0548 | | 3.4116 | 89.0 | 623 | 4.0705 | 0.835 | 0.2748 | 1.2214 | 0.835 | 0.8213 | 0.1440 | 0.0549 | | 3.4116 | 90.0 | 630 | 4.0717 | 0.835 | 0.2744 | 1.2217 | 0.835 | 0.8213 | 0.1426 | 0.0547 | | 3.4116 | 91.0 | 637 | 4.0740 | 0.835 | 0.2747 | 1.2217 | 0.835 | 0.8213 | 0.1432 | 0.0548 | | 3.4116 | 92.0 | 644 | 4.0753 | 0.835 | 0.2748 | 1.2217 | 0.835 | 0.8213 | 0.1442 | 0.0547 | | 3.4116 | 93.0 | 651 | 4.0763 | 0.835 | 0.2746 | 1.2214 | 0.835 | 0.8213 | 0.1434 | 0.0546 | | 3.4116 | 94.0 | 658 | 4.0777 | 0.835 | 0.2746 | 1.2213 | 0.835 | 0.8213 | 0.1433 | 0.0547 | | 3.4116 | 95.0 | 665 | 4.0788 | 0.835 | 0.2747 | 1.2217 | 0.835 | 0.8213 | 0.1442 | 0.0547 | | 3.4116 | 96.0 | 672 | 4.0800 | 0.835 | 0.2748 | 1.2217 | 0.835 | 0.8213 | 0.1466 | 0.0547 | | 3.4116 | 97.0 | 679 | 4.0802 | 0.835 | 0.2747 | 1.2215 | 0.835 | 0.8213 | 0.1435 | 0.0547 | | 3.4116 | 98.0 | 686 | 4.0808 | 0.835 | 0.2747 | 1.2214 | 0.835 | 0.8213 | 0.1435 | 0.0547 | | 3.4116 | 99.0 | 693 | 4.0811 | 0.835 | 0.2748 | 1.2214 | 0.835 | 0.8213 | 0.1443 | 0.0547 | | 3.4116 | 100.0 | 700 | 4.0812 | 0.835 | 0.2748 | 1.2215 | 0.835 | 0.8213 | 0.1443 | 0.0548 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

leopuv/dog_chicken_muffin_classifier

# leopuv/dog_chicken_muffin_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0326 - Train Accuracy: 0.9947 - Validation Loss: 0.0191 - Validation Accuracy: 0.9947 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 22500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4271 | 0.996 | 0.1010 | 0.9960 | 0 | | 0.1111 | 0.9933 | 0.0613 | 0.9933 | 1 | | 0.0774 | 0.9947 | 0.0451 | 0.9947 | 2 | | 0.0592 | 0.9947 | 0.0354 | 0.9947 | 3 | | 0.0457 | 0.9987 | 0.0252 | 0.9987 | 4 | | 0.0434 | 0.992 | 0.0346 | 0.9920 | 5 | | 0.0429 | 0.996 | 0.0270 | 0.9960 | 6 | | 0.0397 | 0.9973 | 0.0177 | 0.9973 | 7 | | 0.0326 | 0.9973 | 0.0161 | 0.9973 | 8 | | 0.0326 | 0.9947 | 0.0191 | 0.9947 | 9 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "chicken", "dog", "muffin" ]

jordyvl/39-tiny_tobacco3482_hint_

# 39-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 65.8239 - Accuracy: 0.84 - Brier Loss: 0.2807 - Nll: 1.1327 - F1 Micro: 0.8400 - F1 Macro: 0.8280 - Ece: 0.1437 - Aurc: 0.0472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 69.1264 | 0.26 | 0.8707 | 4.9002 | 0.26 | 0.1920 | 0.3064 | 0.7815 | | No log | 2.0 | 50 | 68.3319 | 0.545 | 0.5960 | 2.8558 | 0.545 | 0.4562 | 0.2850 | 0.2564 | | No log | 3.0 | 75 | 67.8627 | 0.68 | 0.4406 | 1.6064 | 0.68 | 0.6157 | 0.2543 | 0.1333 | | No log | 4.0 | 100 | 67.5797 | 0.75 | 0.3829 | 1.5484 | 0.75 | 0.7343 | 0.2220 | 0.1152 | | No log | 5.0 | 125 | 67.2608 | 0.8 | 0.3072 | 1.7491 | 0.8000 | 0.7573 | 0.1809 | 0.0698 | | No log | 6.0 | 150 | 67.0950 | 0.78 | 0.3169 | 1.7708 | 0.78 | 0.7441 | 0.1576 | 0.0607 | | No log | 7.0 | 175 | 66.9178 | 0.755 | 0.3812 | 1.6929 | 0.755 | 0.6848 | 0.1899 | 0.1043 | | No log | 8.0 | 200 | 66.7335 | 0.75 | 0.3763 | 1.7649 | 0.75 | 0.7399 | 0.1870 | 0.0806 | | No log | 9.0 | 225 | 66.4371 | 0.805 | 0.3036 | 1.4686 | 0.805 | 0.7896 | 0.1378 | 0.0545 | | No log | 10.0 | 250 | 66.6823 | 0.75 | 0.3924 | 1.8808 | 0.75 | 0.6665 | 0.1964 | 0.0829 | | No log | 11.0 | 275 | 66.6079 | 0.775 | 0.3570 | 1.8872 | 0.775 | 0.7442 | 0.1811 | 0.0839 | | No log | 12.0 | 300 | 66.4364 | 0.765 | 0.3689 | 1.6981 | 0.765 | 0.7550 | 0.1909 | 0.0732 | | No log | 13.0 | 325 | 66.1317 | 0.785 | 0.3346 | 1.4062 | 0.785 | 0.7823 | 0.1753 | 0.0572 | | No log | 14.0 | 350 | 66.5182 | 0.73 | 0.4453 | 1.4431 | 0.7300 | 0.7208 | 0.2310 | 0.0985 | | No log | 15.0 | 375 | 66.5154 | 0.775 | 0.3769 | 1.4897 | 0.775 | 0.7398 | 0.1875 | 0.0821 | | No log | 16.0 | 400 | 66.4191 | 0.8 | 0.3315 | 1.5327 | 0.8000 | 0.7771 | 0.1651 | 0.0685 | | No log | 17.0 | 425 | 66.2374 | 0.765 | 0.3520 | 1.5388 | 0.765 | 0.7401 | 0.1767 | 0.0760 | | No log | 18.0 | 450 | 66.2010 | 0.805 | 0.3320 | 1.4280 | 0.805 | 0.7721 | 0.1756 | 0.0684 | | No log | 19.0 | 475 | 66.0335 | 0.85 | 0.2625 | 1.3549 | 0.85 | 0.8352 | 0.1431 | 0.0430 | | 65.4034 | 20.0 | 500 | 66.2213 | 0.815 | 0.3213 | 1.3912 | 0.815 | 0.7955 | 0.1645 | 0.0579 | | 65.4034 | 21.0 | 525 | 66.2647 | 0.77 | 0.3656 | 1.3241 | 0.7700 | 0.7743 | 0.1899 | 0.0755 | | 65.4034 | 22.0 | 550 | 66.1220 | 0.86 | 0.2684 | 1.2459 | 0.8600 | 0.8354 | 0.1327 | 0.0473 | | 65.4034 | 23.0 | 575 | 66.1615 | 0.85 | 0.2623 | 1.3400 | 0.85 | 0.8231 | 0.1291 | 0.0448 | | 65.4034 | 24.0 | 600 | 66.2114 | 0.825 | 0.3130 | 1.4118 | 0.825 | 0.8122 | 0.1565 | 0.0498 | | 65.4034 | 25.0 | 625 | 66.1048 | 0.835 | 0.2704 | 1.3571 | 0.835 | 0.8196 | 0.1405 | 0.0450 | | 65.4034 | 26.0 | 650 | 65.9832 | 0.825 | 0.2990 | 1.1514 | 0.825 | 0.8253 | 0.1603 | 0.0423 | | 65.4034 | 27.0 | 675 | 66.2567 | 0.805 | 0.3307 | 1.3509 | 0.805 | 0.8022 | 0.1699 | 0.0634 | | 65.4034 | 28.0 | 700 | 66.0668 | 0.82 | 0.3172 | 1.1445 | 0.82 | 0.7973 | 0.1538 | 0.0419 | | 65.4034 | 29.0 | 725 | 66.2254 | 0.81 | 0.3252 | 1.3290 | 0.81 | 0.8011 | 0.1659 | 0.0523 | | 65.4034 | 30.0 | 750 | 65.9643 | 0.84 | 0.2697 | 1.2052 | 0.8400 | 0.8245 | 0.1319 | 0.0425 | | 65.4034 | 31.0 | 775 | 66.3419 | 0.81 | 0.3249 | 1.2772 | 0.81 | 0.7969 | 0.1700 | 0.0612 | | 65.4034 | 32.0 | 800 | 66.0324 | 0.825 | 0.3003 | 1.3138 | 0.825 | 0.8000 | 0.1584 | 0.0445 | | 65.4034 | 33.0 | 825 | 66.3326 | 0.82 | 0.3336 | 1.2983 | 0.82 | 0.7826 | 0.1754 | 0.0590 | | 65.4034 | 34.0 | 850 | 66.1374 | 0.825 | 0.3061 | 1.5645 | 0.825 | 0.8012 | 0.1500 | 0.0459 | | 65.4034 | 35.0 | 875 | 66.2310 | 0.815 | 0.3207 | 1.5607 | 0.815 | 0.7939 | 0.1712 | 0.0646 | | 65.4034 | 36.0 | 900 | 66.0388 | 0.84 | 0.2873 | 1.1966 | 0.8400 | 0.8327 | 0.1456 | 0.0585 | | 65.4034 | 37.0 | 925 | 66.0520 | 0.835 | 0.2958 | 1.2728 | 0.835 | 0.8180 | 0.1508 | 0.0477 | | 65.4034 | 38.0 | 950 | 65.9916 | 0.84 | 0.2783 | 1.1635 | 0.8400 | 0.8233 | 0.1398 | 0.0438 | | 65.4034 | 39.0 | 975 | 65.9391 | 0.845 | 0.2743 | 1.2660 | 0.845 | 0.8289 | 0.1396 | 0.0458 | | 64.0802 | 40.0 | 1000 | 65.9291 | 0.845 | 0.2762 | 1.3335 | 0.845 | 0.8259 | 0.1373 | 0.0430 | | 64.0802 | 41.0 | 1025 | 65.8559 | 0.85 | 0.2686 | 1.3432 | 0.85 | 0.8338 | 0.1345 | 0.0428 | | 64.0802 | 42.0 | 1050 | 65.8612 | 0.845 | 0.2772 | 1.2679 | 0.845 | 0.8255 | 0.1389 | 0.0431 | | 64.0802 | 43.0 | 1075 | 65.8953 | 0.84 | 0.2742 | 1.2614 | 0.8400 | 0.8227 | 0.1408 | 0.0435 | | 64.0802 | 44.0 | 1100 | 65.8569 | 0.835 | 0.2769 | 1.2730 | 0.835 | 0.8199 | 0.1426 | 0.0432 | | 64.0802 | 45.0 | 1125 | 65.8610 | 0.84 | 0.2769 | 1.2622 | 0.8400 | 0.8248 | 0.1485 | 0.0425 | | 64.0802 | 46.0 | 1150 | 65.8237 | 0.845 | 0.2729 | 1.1920 | 0.845 | 0.8334 | 0.1462 | 0.0432 | | 64.0802 | 47.0 | 1175 | 65.8416 | 0.845 | 0.2785 | 1.1826 | 0.845 | 0.8317 | 0.1376 | 0.0431 | | 64.0802 | 48.0 | 1200 | 65.8452 | 0.845 | 0.2817 | 1.1876 | 0.845 | 0.8317 | 0.1417 | 0.0441 | | 64.0802 | 49.0 | 1225 | 65.8394 | 0.845 | 0.2750 | 1.1993 | 0.845 | 0.8309 | 0.1315 | 0.0419 | | 64.0802 | 50.0 | 1250 | 65.8527 | 0.84 | 0.2796 | 1.1860 | 0.8400 | 0.8279 | 0.1410 | 0.0432 | | 64.0802 | 51.0 | 1275 | 65.8286 | 0.845 | 0.2749 | 1.1977 | 0.845 | 0.8333 | 0.1444 | 0.0428 | | 64.0802 | 52.0 | 1300 | 65.8296 | 0.83 | 0.2779 | 1.1926 | 0.83 | 0.8171 | 0.1382 | 0.0435 | | 64.0802 | 53.0 | 1325 | 65.8121 | 0.83 | 0.2779 | 1.1955 | 0.83 | 0.8155 | 0.1387 | 0.0436 | | 64.0802 | 54.0 | 1350 | 65.8361 | 0.825 | 0.2769 | 1.1909 | 0.825 | 0.8095 | 0.1435 | 0.0419 | | 64.0802 | 55.0 | 1375 | 65.8370 | 0.83 | 0.2816 | 1.1925 | 0.83 | 0.8171 | 0.1416 | 0.0435 | | 64.0802 | 56.0 | 1400 | 65.8301 | 0.825 | 0.2763 | 1.1908 | 0.825 | 0.8101 | 0.1393 | 0.0439 | | 64.0802 | 57.0 | 1425 | 65.8301 | 0.82 | 0.2791 | 1.1881 | 0.82 | 0.8040 | 0.1443 | 0.0440 | | 64.0802 | 58.0 | 1450 | 65.8324 | 0.83 | 0.2754 | 1.1938 | 0.83 | 0.8198 | 0.1387 | 0.0460 | | 64.0802 | 59.0 | 1475 | 65.8407 | 0.825 | 0.2818 | 1.1893 | 0.825 | 0.8138 | 0.1393 | 0.0439 | | 63.8765 | 60.0 | 1500 | 65.8236 | 0.84 | 0.2782 | 1.1871 | 0.8400 | 0.8290 | 0.1512 | 0.0449 | | 63.8765 | 61.0 | 1525 | 65.8198 | 0.825 | 0.2846 | 1.1752 | 0.825 | 0.8138 | 0.1505 | 0.0438 | | 63.8765 | 62.0 | 1550 | 65.8243 | 0.83 | 0.2796 | 1.1753 | 0.83 | 0.8196 | 0.1480 | 0.0445 | | 63.8765 | 63.0 | 1575 | 65.8495 | 0.835 | 0.2781 | 1.1766 | 0.835 | 0.8257 | 0.1353 | 0.0451 | | 63.8765 | 64.0 | 1600 | 65.8204 | 0.835 | 0.2833 | 1.1752 | 0.835 | 0.8239 | 0.1400 | 0.0447 | | 63.8765 | 65.0 | 1625 | 65.8374 | 0.835 | 0.2800 | 1.1829 | 0.835 | 0.8239 | 0.1474 | 0.0441 | | 63.8765 | 66.0 | 1650 | 65.8433 | 0.83 | 0.2855 | 1.1678 | 0.83 | 0.8148 | 0.1498 | 0.0444 | | 63.8765 | 67.0 | 1675 | 65.8259 | 0.835 | 0.2820 | 1.1725 | 0.835 | 0.8257 | 0.1518 | 0.0457 | | 63.8765 | 68.0 | 1700 | 65.8443 | 0.83 | 0.2841 | 1.1652 | 0.83 | 0.8196 | 0.1491 | 0.0457 | | 63.8765 | 69.0 | 1725 | 65.8255 | 0.835 | 0.2849 | 1.1620 | 0.835 | 0.8247 | 0.1499 | 0.0460 | | 63.8765 | 70.0 | 1750 | 65.8421 | 0.83 | 0.2870 | 1.1681 | 0.83 | 0.8196 | 0.1418 | 0.0475 | | 63.8765 | 71.0 | 1775 | 65.8402 | 0.835 | 0.2839 | 1.1614 | 0.835 | 0.8230 | 0.1359 | 0.0466 | | 63.8765 | 72.0 | 1800 | 65.8224 | 0.84 | 0.2831 | 1.1555 | 0.8400 | 0.8280 | 0.1467 | 0.0459 | | 63.8765 | 73.0 | 1825 | 65.8233 | 0.84 | 0.2824 | 1.1578 | 0.8400 | 0.8280 | 0.1428 | 0.0465 | | 63.8765 | 74.0 | 1850 | 65.8299 | 0.84 | 0.2814 | 1.1574 | 0.8400 | 0.8280 | 0.1469 | 0.0465 | | 63.8765 | 75.0 | 1875 | 65.8309 | 0.835 | 0.2790 | 1.1575 | 0.835 | 0.8219 | 0.1407 | 0.0465 | | 63.8765 | 76.0 | 1900 | 65.8199 | 0.84 | 0.2789 | 1.1496 | 0.8400 | 0.8280 | 0.1437 | 0.0460 | | 63.8765 | 77.0 | 1925 | 65.8222 | 0.84 | 0.2828 | 1.1520 | 0.8400 | 0.8280 | 0.1539 | 0.0461 | | 63.8765 | 78.0 | 1950 | 65.8312 | 0.84 | 0.2801 | 1.1459 | 0.8400 | 0.8280 | 0.1354 | 0.0458 | | 63.8765 | 79.0 | 1975 | 65.8253 | 0.84 | 0.2836 | 1.1448 | 0.8400 | 0.8280 | 0.1542 | 0.0465 | | 63.7964 | 80.0 | 2000 | 65.8332 | 0.84 | 0.2839 | 1.1408 | 0.8400 | 0.8280 | 0.1486 | 0.0462 | | 63.7964 | 81.0 | 2025 | 65.8316 | 0.84 | 0.2818 | 1.1419 | 0.8400 | 0.8280 | 0.1430 | 0.0460 | | 63.7964 | 82.0 | 2050 | 65.8238 | 0.84 | 0.2824 | 1.1387 | 0.8400 | 0.8280 | 0.1411 | 0.0452 | | 63.7964 | 83.0 | 2075 | 65.8294 | 0.84 | 0.2786 | 1.1410 | 0.8400 | 0.8280 | 0.1539 | 0.0469 | | 63.7964 | 84.0 | 2100 | 65.8267 | 0.84 | 0.2818 | 1.1391 | 0.8400 | 0.8280 | 0.1463 | 0.0471 | | 63.7964 | 85.0 | 2125 | 65.8222 | 0.84 | 0.2814 | 1.1401 | 0.8400 | 0.8280 | 0.1463 | 0.0470 | | 63.7964 | 86.0 | 2150 | 65.8264 | 0.84 | 0.2776 | 1.1380 | 0.8400 | 0.8280 | 0.1359 | 0.0460 | | 63.7964 | 87.0 | 2175 | 65.8228 | 0.84 | 0.2781 | 1.1366 | 0.8400 | 0.8280 | 0.1468 | 0.0460 | | 63.7964 | 88.0 | 2200 | 65.8229 | 0.84 | 0.2832 | 1.1367 | 0.8400 | 0.8280 | 0.1455 | 0.0476 | | 63.7964 | 89.0 | 2225 | 65.8271 | 0.84 | 0.2792 | 1.1376 | 0.8400 | 0.8280 | 0.1598 | 0.0467 | | 63.7964 | 90.0 | 2250 | 65.8234 | 0.84 | 0.2830 | 1.1352 | 0.8400 | 0.8280 | 0.1427 | 0.0474 | | 63.7964 | 91.0 | 2275 | 65.8309 | 0.84 | 0.2804 | 1.1352 | 0.8400 | 0.8280 | 0.1426 | 0.0467 | | 63.7964 | 92.0 | 2300 | 65.8305 | 0.84 | 0.2796 | 1.1345 | 0.8400 | 0.8280 | 0.1438 | 0.0466 | | 63.7964 | 93.0 | 2325 | 65.8155 | 0.84 | 0.2808 | 1.1347 | 0.8400 | 0.8280 | 0.1499 | 0.0471 | | 63.7964 | 94.0 | 2350 | 65.8218 | 0.84 | 0.2803 | 1.1336 | 0.8400 | 0.8280 | 0.1487 | 0.0473 | | 63.7964 | 95.0 | 2375 | 65.8152 | 0.84 | 0.2812 | 1.1334 | 0.8400 | 0.8280 | 0.1441 | 0.0466 | | 63.7964 | 96.0 | 2400 | 65.8230 | 0.84 | 0.2801 | 1.1344 | 0.8400 | 0.8280 | 0.1488 | 0.0472 | | 63.7964 | 97.0 | 2425 | 65.8206 | 0.84 | 0.2808 | 1.1328 | 0.8400 | 0.8280 | 0.1490 | 0.0472 | | 63.7964 | 98.0 | 2450 | 65.8221 | 0.84 | 0.2807 | 1.1332 | 0.8400 | 0.8280 | 0.1438 | 0.0474 | | 63.7964 | 99.0 | 2475 | 65.8207 | 0.84 | 0.2809 | 1.1326 | 0.8400 | 0.8280 | 0.1446 | 0.0472 | | 63.7613 | 100.0 | 2500 | 65.8239 | 0.84 | 0.2807 | 1.1327 | 0.8400 | 0.8280 | 0.1437 | 0.0472 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

annazhong/vit-base-patch16-224-finetuned-eurosat

# vit-base-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7730 - Accuracy: 0.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 150 - eval_batch_size: 150 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 600 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 1.8264 | 0.1143 | | No log | 2.0 | 2 | 1.7730 | 0.2 | | No log | 3.0 | 3 | 1.8143 | 0.2 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]

jordyvl/18-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 18-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6385 - Accuracy: 0.795 - Brier Loss: 0.4484 - Nll: 0.9250 - F1 Micro: 0.795 - F1 Macro: 0.7709 - Ece: 0.4225 - Aurc: 0.0567 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.8736 | 0.105 | 1.0144 | 8.6059 | 0.1050 | 0.0844 | 0.3169 | 0.8967 | | No log | 2.0 | 14 | 1.2559 | 0.155 | 0.8899 | 7.1587 | 0.155 | 0.1259 | 0.2459 | 0.7824 | | No log | 3.0 | 21 | 1.0441 | 0.33 | 0.8123 | 5.3633 | 0.33 | 0.2575 | 0.2995 | 0.5173 | | No log | 4.0 | 28 | 0.9169 | 0.525 | 0.6852 | 3.4671 | 0.525 | 0.4253 | 0.3387 | 0.2892 | | No log | 5.0 | 35 | 0.8589 | 0.615 | 0.6269 | 3.1119 | 0.615 | 0.5500 | 0.3683 | 0.2124 | | No log | 6.0 | 42 | 0.7954 | 0.675 | 0.5756 | 2.2578 | 0.675 | 0.5752 | 0.3626 | 0.1550 | | No log | 7.0 | 49 | 0.7664 | 0.685 | 0.5143 | 1.8811 | 0.685 | 0.6073 | 0.3254 | 0.1390 | | No log | 8.0 | 56 | 0.7305 | 0.76 | 0.4895 | 1.5449 | 0.76 | 0.6768 | 0.3695 | 0.1016 | | No log | 9.0 | 63 | 0.7056 | 0.765 | 0.4721 | 1.3575 | 0.765 | 0.6991 | 0.3828 | 0.0927 | | No log | 10.0 | 70 | 0.6961 | 0.77 | 0.4380 | 1.2662 | 0.7700 | 0.7509 | 0.3549 | 0.0803 | | No log | 11.0 | 77 | 0.6772 | 0.81 | 0.4508 | 1.3169 | 0.81 | 0.7915 | 0.4175 | 0.0629 | | No log | 12.0 | 84 | 0.6766 | 0.785 | 0.4491 | 1.2979 | 0.785 | 0.7650 | 0.3839 | 0.0800 | | No log | 13.0 | 91 | 0.6754 | 0.785 | 0.4382 | 1.2395 | 0.785 | 0.7794 | 0.3609 | 0.0689 | | No log | 14.0 | 98 | 0.6768 | 0.8 | 0.4472 | 1.2218 | 0.8000 | 0.7837 | 0.3910 | 0.0640 | | No log | 15.0 | 105 | 0.6793 | 0.81 | 0.4663 | 1.2698 | 0.81 | 0.7856 | 0.4293 | 0.0672 | | No log | 16.0 | 112 | 0.6784 | 0.795 | 0.4726 | 1.3043 | 0.795 | 0.7728 | 0.4232 | 0.0669 | | No log | 17.0 | 119 | 0.6638 | 0.805 | 0.4372 | 1.2746 | 0.805 | 0.7747 | 0.3956 | 0.0677 | | No log | 18.0 | 126 | 0.6588 | 0.8 | 0.4297 | 1.4466 | 0.8000 | 0.7762 | 0.3866 | 0.0686 | | No log | 19.0 | 133 | 0.6588 | 0.81 | 0.4588 | 1.2093 | 0.81 | 0.7912 | 0.4029 | 0.0702 | | No log | 20.0 | 140 | 0.6587 | 0.81 | 0.4534 | 1.0697 | 0.81 | 0.7980 | 0.4197 | 0.0641 | | No log | 21.0 | 147 | 0.6527 | 0.815 | 0.4529 | 1.1527 | 0.815 | 0.7942 | 0.4196 | 0.0598 | | No log | 22.0 | 154 | 0.6608 | 0.78 | 0.4559 | 1.2039 | 0.78 | 0.7581 | 0.3612 | 0.0725 | | No log | 23.0 | 161 | 0.6558 | 0.8 | 0.4547 | 1.0687 | 0.8000 | 0.7644 | 0.3964 | 0.0584 | | No log | 24.0 | 168 | 0.6584 | 0.8 | 0.4491 | 1.2869 | 0.8000 | 0.7735 | 0.3810 | 0.0687 | | No log | 25.0 | 175 | 0.6493 | 0.805 | 0.4497 | 0.9981 | 0.805 | 0.7887 | 0.4162 | 0.0570 | | No log | 26.0 | 182 | 0.6425 | 0.795 | 0.4424 | 1.1317 | 0.795 | 0.7790 | 0.3974 | 0.0596 | | No log | 27.0 | 189 | 0.6518 | 0.8 | 0.4552 | 0.9743 | 0.8000 | 0.7715 | 0.4122 | 0.0592 | | No log | 28.0 | 196 | 0.6526 | 0.805 | 0.4630 | 1.1343 | 0.805 | 0.7941 | 0.4171 | 0.0672 | | No log | 29.0 | 203 | 0.6515 | 0.8 | 0.4531 | 1.0062 | 0.8000 | 0.7681 | 0.3970 | 0.0566 | | No log | 30.0 | 210 | 0.6459 | 0.795 | 0.4534 | 1.0893 | 0.795 | 0.7853 | 0.3972 | 0.0600 | | No log | 31.0 | 217 | 0.6423 | 0.81 | 0.4483 | 0.9035 | 0.81 | 0.7927 | 0.4297 | 0.0536 | | No log | 32.0 | 224 | 0.6454 | 0.8 | 0.4517 | 1.1025 | 0.8000 | 0.7688 | 0.3923 | 0.0599 | | No log | 33.0 | 231 | 0.6417 | 0.805 | 0.4476 | 0.9658 | 0.805 | 0.7767 | 0.4136 | 0.0563 | | No log | 34.0 | 238 | 0.6399 | 0.815 | 0.4462 | 0.8565 | 0.815 | 0.7940 | 0.4234 | 0.0550 | | No log | 35.0 | 245 | 0.6430 | 0.81 | 0.4505 | 1.0491 | 0.81 | 0.7855 | 0.4279 | 0.0629 | | No log | 36.0 | 252 | 0.6440 | 0.815 | 0.4481 | 1.0288 | 0.815 | 0.7813 | 0.4132 | 0.0539 | | No log | 37.0 | 259 | 0.6396 | 0.82 | 0.4493 | 0.9477 | 0.82 | 0.8125 | 0.4266 | 0.0525 | | No log | 38.0 | 266 | 0.6410 | 0.815 | 0.4462 | 1.0462 | 0.815 | 0.7971 | 0.4157 | 0.0522 | | No log | 39.0 | 273 | 0.6360 | 0.8 | 0.4399 | 0.9645 | 0.8000 | 0.7779 | 0.3974 | 0.0566 | | No log | 40.0 | 280 | 0.6376 | 0.805 | 0.4412 | 0.8777 | 0.805 | 0.7772 | 0.4104 | 0.0544 | | No log | 41.0 | 287 | 0.6411 | 0.795 | 0.4475 | 0.9240 | 0.795 | 0.7780 | 0.4062 | 0.0583 | | No log | 42.0 | 294 | 0.6398 | 0.795 | 0.4509 | 0.9279 | 0.795 | 0.7650 | 0.4068 | 0.0577 | | No log | 43.0 | 301 | 0.6430 | 0.79 | 0.4567 | 0.9279 | 0.79 | 0.7683 | 0.4073 | 0.0590 | | No log | 44.0 | 308 | 0.6401 | 0.8 | 0.4495 | 0.9915 | 0.8000 | 0.7744 | 0.4200 | 0.0565 | | No log | 45.0 | 315 | 0.6364 | 0.795 | 0.4448 | 0.9245 | 0.795 | 0.7729 | 0.4115 | 0.0568 | | No log | 46.0 | 322 | 0.6391 | 0.79 | 0.4472 | 1.0060 | 0.79 | 0.7633 | 0.4044 | 0.0561 | | No log | 47.0 | 329 | 0.6376 | 0.795 | 0.4470 | 0.9530 | 0.795 | 0.7693 | 0.3989 | 0.0578 | | No log | 48.0 | 336 | 0.6383 | 0.8 | 0.4476 | 0.9992 | 0.8000 | 0.7804 | 0.4084 | 0.0579 | | No log | 49.0 | 343 | 0.6353 | 0.8 | 0.4424 | 0.8500 | 0.8000 | 0.7756 | 0.4055 | 0.0546 | | No log | 50.0 | 350 | 0.6381 | 0.795 | 0.4470 | 0.9931 | 0.795 | 0.7691 | 0.4170 | 0.0573 | | No log | 51.0 | 357 | 0.6374 | 0.795 | 0.4477 | 0.9729 | 0.795 | 0.7630 | 0.4076 | 0.0563 | | No log | 52.0 | 364 | 0.6377 | 0.8 | 0.4481 | 0.9846 | 0.8000 | 0.7759 | 0.4212 | 0.0555 | | No log | 53.0 | 371 | 0.6378 | 0.795 | 0.4485 | 0.9379 | 0.795 | 0.7733 | 0.4052 | 0.0565 | | No log | 54.0 | 378 | 0.6385 | 0.79 | 0.4477 | 0.9900 | 0.79 | 0.7684 | 0.4165 | 0.0571 | | No log | 55.0 | 385 | 0.6371 | 0.81 | 0.4466 | 0.9178 | 0.81 | 0.7867 | 0.4149 | 0.0546 | | No log | 56.0 | 392 | 0.6373 | 0.795 | 0.4460 | 0.9254 | 0.795 | 0.7692 | 0.4081 | 0.0568 | | No log | 57.0 | 399 | 0.6376 | 0.79 | 0.4476 | 0.9194 | 0.79 | 0.7596 | 0.3996 | 0.0568 | | No log | 58.0 | 406 | 0.6380 | 0.79 | 0.4477 | 0.9259 | 0.79 | 0.7619 | 0.4024 | 0.0575 | | No log | 59.0 | 413 | 0.6377 | 0.8 | 0.4474 | 0.9100 | 0.8000 | 0.7806 | 0.4096 | 0.0569 | | No log | 60.0 | 420 | 0.6378 | 0.8 | 0.4481 | 0.9189 | 0.8000 | 0.7806 | 0.4076 | 0.0566 | | No log | 61.0 | 427 | 0.6378 | 0.795 | 0.4478 | 0.9860 | 0.795 | 0.7709 | 0.3994 | 0.0566 | | No log | 62.0 | 434 | 0.6380 | 0.795 | 0.4480 | 0.9189 | 0.795 | 0.7692 | 0.4070 | 0.0564 | | No log | 63.0 | 441 | 0.6381 | 0.8 | 0.4482 | 0.9195 | 0.8000 | 0.7806 | 0.4047 | 0.0568 | | No log | 64.0 | 448 | 0.6379 | 0.8 | 0.4480 | 0.9223 | 0.8000 | 0.7806 | 0.4224 | 0.0563 | | No log | 65.0 | 455 | 0.6382 | 0.8 | 0.4481 | 0.9196 | 0.8000 | 0.7806 | 0.4113 | 0.0569 | | No log | 66.0 | 462 | 0.6381 | 0.8 | 0.4484 | 0.9200 | 0.8000 | 0.7806 | 0.4308 | 0.0566 | | No log | 67.0 | 469 | 0.6379 | 0.8 | 0.4479 | 0.9198 | 0.8000 | 0.7806 | 0.4186 | 0.0566 | | No log | 68.0 | 476 | 0.6378 | 0.8 | 0.4476 | 0.9167 | 0.8000 | 0.7806 | 0.4166 | 0.0569 | | No log | 69.0 | 483 | 0.6380 | 0.8 | 0.4481 | 0.9179 | 0.8000 | 0.7806 | 0.4254 | 0.0566 | | No log | 70.0 | 490 | 0.6384 | 0.795 | 0.4486 | 0.9225 | 0.795 | 0.7709 | 0.4158 | 0.0566 | | No log | 71.0 | 497 | 0.6380 | 0.795 | 0.4476 | 0.9211 | 0.795 | 0.7709 | 0.4215 | 0.0568 | | 0.5133 | 72.0 | 504 | 0.6381 | 0.795 | 0.4480 | 0.9232 | 0.795 | 0.7709 | 0.4151 | 0.0566 | | 0.5133 | 73.0 | 511 | 0.6380 | 0.795 | 0.4479 | 0.9242 | 0.795 | 0.7709 | 0.4218 | 0.0564 | | 0.5133 | 74.0 | 518 | 0.6380 | 0.795 | 0.4478 | 0.9231 | 0.795 | 0.7709 | 0.4151 | 0.0566 | | 0.5133 | 75.0 | 525 | 0.6382 | 0.795 | 0.4484 | 0.9245 | 0.795 | 0.7709 | 0.4156 | 0.0565 | | 0.5133 | 76.0 | 532 | 0.6382 | 0.795 | 0.4481 | 0.9216 | 0.795 | 0.7709 | 0.4153 | 0.0567 | | 0.5133 | 77.0 | 539 | 0.6382 | 0.795 | 0.4481 | 0.9231 | 0.795 | 0.7709 | 0.4222 | 0.0567 | | 0.5133 | 78.0 | 546 | 0.6382 | 0.795 | 0.4481 | 0.9210 | 0.795 | 0.7709 | 0.4220 | 0.0565 | | 0.5133 | 79.0 | 553 | 0.6382 | 0.795 | 0.4480 | 0.9220 | 0.795 | 0.7709 | 0.4220 | 0.0565 | | 0.5133 | 80.0 | 560 | 0.6384 | 0.795 | 0.4484 | 0.9220 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 81.0 | 567 | 0.6383 | 0.795 | 0.4483 | 0.9218 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 82.0 | 574 | 0.6382 | 0.795 | 0.4480 | 0.9220 | 0.795 | 0.7709 | 0.4221 | 0.0568 | | 0.5133 | 83.0 | 581 | 0.6384 | 0.795 | 0.4484 | 0.9240 | 0.795 | 0.7709 | 0.4157 | 0.0566 | | 0.5133 | 84.0 | 588 | 0.6384 | 0.795 | 0.4484 | 0.9262 | 0.795 | 0.7709 | 0.4224 | 0.0566 | | 0.5133 | 85.0 | 595 | 0.6382 | 0.795 | 0.4481 | 0.9235 | 0.795 | 0.7709 | 0.4221 | 0.0566 | | 0.5133 | 86.0 | 602 | 0.6384 | 0.795 | 0.4484 | 0.9236 | 0.795 | 0.7709 | 0.4225 | 0.0566 | | 0.5133 | 87.0 | 609 | 0.6384 | 0.795 | 0.4484 | 0.9235 | 0.795 | 0.7709 | 0.4225 | 0.0567 | | 0.5133 | 88.0 | 616 | 0.6384 | 0.795 | 0.4483 | 0.9250 | 0.795 | 0.7709 | 0.4224 | 0.0566 | | 0.5133 | 89.0 | 623 | 0.6384 | 0.795 | 0.4483 | 0.9244 | 0.795 | 0.7709 | 0.4223 | 0.0567 | | 0.5133 | 90.0 | 630 | 0.6384 | 0.795 | 0.4483 | 0.9251 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 91.0 | 637 | 0.6384 | 0.795 | 0.4484 | 0.9246 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 92.0 | 644 | 0.6384 | 0.795 | 0.4484 | 0.9256 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 93.0 | 651 | 0.6385 | 0.795 | 0.4484 | 0.9252 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 94.0 | 658 | 0.6384 | 0.795 | 0.4484 | 0.9245 | 0.795 | 0.7709 | 0.4223 | 0.0565 | | 0.5133 | 95.0 | 665 | 0.6385 | 0.795 | 0.4484 | 0.9254 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 96.0 | 672 | 0.6384 | 0.795 | 0.4484 | 0.9242 | 0.795 | 0.7709 | 0.4225 | 0.0566 | | 0.5133 | 97.0 | 679 | 0.6384 | 0.795 | 0.4484 | 0.9242 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 98.0 | 686 | 0.6385 | 0.795 | 0.4484 | 0.9249 | 0.795 | 0.7709 | 0.4224 | 0.0567 | | 0.5133 | 99.0 | 693 | 0.6385 | 0.795 | 0.4484 | 0.9252 | 0.795 | 0.7709 | 0.4224 | 0.0566 | | 0.5133 | 100.0 | 700 | 0.6385 | 0.795 | 0.4484 | 0.9250 | 0.795 | 0.7709 | 0.4225 | 0.0567 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/18-tiny_tobacco3482

# 18-tiny_tobacco3482 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0652 - Accuracy: 0.29 - Brier Loss: 0.8849 - Nll: 4.7897 - F1 Micro: 0.29 - F1 Macro: 0.1634 - Ece: 0.2953 - Aurc: 0.5243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.0888 | 0.1 | 0.8987 | 8.8684 | 0.1000 | 0.036 | 0.1707 | 0.9181 | | No log | 2.0 | 50 | 0.0822 | 0.07 | 0.8987 | 7.4095 | 0.07 | 0.0264 | 0.1526 | 0.9367 | | No log | 3.0 | 75 | 0.0815 | 0.17 | 0.8985 | 7.9603 | 0.17 | 0.0549 | 0.2145 | 0.8165 | | No log | 4.0 | 100 | 0.0811 | 0.175 | 0.8981 | 8.1220 | 0.175 | 0.0636 | 0.2224 | 0.6841 | | No log | 5.0 | 125 | 0.0807 | 0.18 | 0.8980 | 8.0205 | 0.18 | 0.0595 | 0.2162 | 0.6466 | | No log | 6.0 | 150 | 0.0802 | 0.16 | 0.8979 | 8.0022 | 0.16 | 0.0588 | 0.2146 | 0.6987 | | No log | 7.0 | 175 | 0.0795 | 0.16 | 0.8978 | 8.1389 | 0.16 | 0.1003 | 0.2087 | 0.8300 | | No log | 8.0 | 200 | 0.0786 | 0.165 | 0.8973 | 7.2103 | 0.165 | 0.0840 | 0.2019 | 0.7778 | | No log | 9.0 | 225 | 0.0777 | 0.135 | 0.8971 | 6.5106 | 0.135 | 0.0644 | 0.2023 | 0.9632 | | No log | 10.0 | 250 | 0.0768 | 0.12 | 0.8971 | 7.0176 | 0.12 | 0.0555 | 0.1928 | 0.9684 | | No log | 11.0 | 275 | 0.0758 | 0.08 | 0.8962 | 6.4528 | 0.08 | 0.0356 | 0.1609 | 0.9735 | | No log | 12.0 | 300 | 0.0747 | 0.08 | 0.8954 | 6.9898 | 0.08 | 0.0365 | 0.1608 | 0.9594 | | No log | 13.0 | 325 | 0.0736 | 0.14 | 0.8941 | 6.5287 | 0.14 | 0.0645 | 0.2064 | 0.6977 | | No log | 14.0 | 350 | 0.0725 | 0.17 | 0.8937 | 7.0519 | 0.17 | 0.0891 | 0.2278 | 0.6074 | | No log | 15.0 | 375 | 0.0712 | 0.17 | 0.8919 | 6.3769 | 0.17 | 0.0888 | 0.2218 | 0.6154 | | No log | 16.0 | 400 | 0.0702 | 0.18 | 0.8912 | 6.2503 | 0.18 | 0.1086 | 0.2297 | 0.6140 | | No log | 17.0 | 425 | 0.0691 | 0.16 | 0.8909 | 6.5096 | 0.16 | 0.1064 | 0.2081 | 0.6497 | | No log | 18.0 | 450 | 0.0686 | 0.175 | 0.8904 | 6.2506 | 0.175 | 0.1196 | 0.2252 | 0.6744 | | No log | 19.0 | 475 | 0.0681 | 0.16 | 0.8891 | 5.3019 | 0.16 | 0.1000 | 0.2185 | 0.7715 | | 0.0776 | 20.0 | 500 | 0.0678 | 0.155 | 0.8887 | 5.3003 | 0.155 | 0.0980 | 0.2208 | 0.7213 | | 0.0776 | 21.0 | 525 | 0.0677 | 0.155 | 0.8891 | 5.7776 | 0.155 | 0.0944 | 0.2223 | 0.7075 | | 0.0776 | 22.0 | 550 | 0.0671 | 0.185 | 0.8885 | 5.4219 | 0.185 | 0.0998 | 0.2333 | 0.6805 | | 0.0776 | 23.0 | 575 | 0.0669 | 0.17 | 0.8887 | 5.4554 | 0.17 | 0.0813 | 0.2304 | 0.6676 | | 0.0776 | 24.0 | 600 | 0.0667 | 0.175 | 0.8877 | 5.4877 | 0.175 | 0.0791 | 0.2294 | 0.6991 | | 0.0776 | 25.0 | 625 | 0.0665 | 0.18 | 0.8876 | 5.3499 | 0.18 | 0.0846 | 0.2342 | 0.6774 | | 0.0776 | 26.0 | 650 | 0.0665 | 0.18 | 0.8870 | 4.9329 | 0.18 | 0.0888 | 0.2320 | 0.6823 | | 0.0776 | 27.0 | 675 | 0.0663 | 0.21 | 0.8872 | 4.7787 | 0.2100 | 0.1204 | 0.2521 | 0.6698 | | 0.0776 | 28.0 | 700 | 0.0665 | 0.21 | 0.8870 | 4.8102 | 0.2100 | 0.1177 | 0.2481 | 0.6893 | | 0.0776 | 29.0 | 725 | 0.0663 | 0.2 | 0.8866 | 4.8474 | 0.2000 | 0.1124 | 0.2433 | 0.6616 | | 0.0776 | 30.0 | 750 | 0.0663 | 0.21 | 0.8862 | 4.6460 | 0.2100 | 0.1060 | 0.2514 | 0.6663 | | 0.0776 | 31.0 | 775 | 0.0661 | 0.215 | 0.8874 | 5.0331 | 0.2150 | 0.1219 | 0.2585 | 0.6564 | | 0.0776 | 32.0 | 800 | 0.0661 | 0.23 | 0.8871 | 4.9313 | 0.23 | 0.1299 | 0.2600 | 0.6596 | | 0.0776 | 33.0 | 825 | 0.0661 | 0.245 | 0.8870 | 5.2183 | 0.245 | 0.1400 | 0.2729 | 0.6216 | | 0.0776 | 34.0 | 850 | 0.0660 | 0.215 | 0.8869 | 5.0987 | 0.2150 | 0.1130 | 0.2473 | 0.6745 | | 0.0776 | 35.0 | 875 | 0.0659 | 0.245 | 0.8872 | 5.1536 | 0.245 | 0.1404 | 0.2684 | 0.6753 | | 0.0776 | 36.0 | 900 | 0.0661 | 0.23 | 0.8865 | 5.0364 | 0.23 | 0.1219 | 0.2603 | 0.7257 | | 0.0776 | 37.0 | 925 | 0.0659 | 0.25 | 0.8866 | 5.0315 | 0.25 | 0.1289 | 0.2709 | 0.6743 | | 0.0776 | 38.0 | 950 | 0.0658 | 0.245 | 0.8865 | 5.1304 | 0.245 | 0.1348 | 0.2708 | 0.6383 | | 0.0776 | 39.0 | 975 | 0.0659 | 0.255 | 0.8865 | 4.8854 | 0.255 | 0.1343 | 0.2762 | 0.6336 | | 0.0623 | 40.0 | 1000 | 0.0658 | 0.245 | 0.8866 | 4.8705 | 0.245 | 0.1287 | 0.2709 | 0.6663 | | 0.0623 | 41.0 | 1025 | 0.0656 | 0.245 | 0.8872 | 5.2534 | 0.245 | 0.1307 | 0.2662 | 0.6369 | | 0.0623 | 42.0 | 1050 | 0.0658 | 0.235 | 0.8866 | 5.1467 | 0.235 | 0.1220 | 0.2630 | 0.6834 | | 0.0623 | 43.0 | 1075 | 0.0656 | 0.235 | 0.8868 | 5.2909 | 0.235 | 0.1201 | 0.2629 | 0.6663 | | 0.0623 | 44.0 | 1100 | 0.0655 | 0.24 | 0.8870 | 5.3291 | 0.24 | 0.1315 | 0.2600 | 0.5948 | | 0.0623 | 45.0 | 1125 | 0.0656 | 0.25 | 0.8867 | 5.0194 | 0.25 | 0.1397 | 0.2663 | 0.6091 | | 0.0623 | 46.0 | 1150 | 0.0656 | 0.255 | 0.8863 | 5.0523 | 0.255 | 0.1303 | 0.2729 | 0.5687 | | 0.0623 | 47.0 | 1175 | 0.0655 | 0.25 | 0.8863 | 4.9527 | 0.25 | 0.1293 | 0.2679 | 0.5724 | | 0.0623 | 48.0 | 1200 | 0.0654 | 0.25 | 0.8863 | 5.0401 | 0.25 | 0.1307 | 0.2588 | 0.5783 | | 0.0623 | 49.0 | 1225 | 0.0655 | 0.25 | 0.8862 | 5.0198 | 0.25 | 0.1301 | 0.2781 | 0.6005 | | 0.0623 | 50.0 | 1250 | 0.0653 | 0.255 | 0.8858 | 5.0534 | 0.255 | 0.1311 | 0.2732 | 0.5601 | | 0.0623 | 51.0 | 1275 | 0.0653 | 0.26 | 0.8861 | 4.9558 | 0.26 | 0.1329 | 0.2779 | 0.5458 | | 0.0623 | 52.0 | 1300 | 0.0653 | 0.275 | 0.8855 | 4.8780 | 0.275 | 0.1497 | 0.2863 | 0.5410 | | 0.0623 | 53.0 | 1325 | 0.0653 | 0.27 | 0.8854 | 4.8854 | 0.27 | 0.1453 | 0.2857 | 0.5276 | | 0.0623 | 54.0 | 1350 | 0.0652 | 0.26 | 0.8853 | 4.9379 | 0.26 | 0.1322 | 0.2803 | 0.5457 | | 0.0623 | 55.0 | 1375 | 0.0653 | 0.26 | 0.8852 | 4.9101 | 0.26 | 0.1388 | 0.2778 | 0.5414 | | 0.0623 | 56.0 | 1400 | 0.0653 | 0.275 | 0.8849 | 4.8107 | 0.275 | 0.1541 | 0.2881 | 0.5204 | | 0.0623 | 57.0 | 1425 | 0.0652 | 0.26 | 0.8850 | 4.7712 | 0.26 | 0.1402 | 0.2709 | 0.5323 | | 0.0623 | 58.0 | 1450 | 0.0652 | 0.275 | 0.8850 | 4.8164 | 0.275 | 0.1618 | 0.2885 | 0.5288 | | 0.0623 | 59.0 | 1475 | 0.0652 | 0.27 | 0.8848 | 4.7627 | 0.27 | 0.1402 | 0.2830 | 0.5194 | | 0.0599 | 60.0 | 1500 | 0.0652 | 0.28 | 0.8851 | 4.8057 | 0.28 | 0.1624 | 0.2889 | 0.5304 | | 0.0599 | 61.0 | 1525 | 0.0652 | 0.26 | 0.8851 | 4.8892 | 0.26 | 0.1351 | 0.2823 | 0.5372 | | 0.0599 | 62.0 | 1550 | 0.0651 | 0.28 | 0.8852 | 4.8944 | 0.28 | 0.1625 | 0.2889 | 0.5320 | | 0.0599 | 63.0 | 1575 | 0.0652 | 0.275 | 0.8849 | 4.8180 | 0.275 | 0.1484 | 0.2858 | 0.5207 | | 0.0599 | 64.0 | 1600 | 0.0652 | 0.265 | 0.8849 | 4.8034 | 0.265 | 0.1366 | 0.2807 | 0.5431 | | 0.0599 | 65.0 | 1625 | 0.0651 | 0.28 | 0.8852 | 4.7696 | 0.28 | 0.1587 | 0.2888 | 0.5327 | | 0.0599 | 66.0 | 1650 | 0.0652 | 0.28 | 0.8850 | 4.8120 | 0.28 | 0.1561 | 0.2886 | 0.5134 | | 0.0599 | 67.0 | 1675 | 0.0651 | 0.28 | 0.8850 | 4.7193 | 0.28 | 0.1623 | 0.2867 | 0.5339 | | 0.0599 | 68.0 | 1700 | 0.0652 | 0.255 | 0.8848 | 4.7659 | 0.255 | 0.1287 | 0.2726 | 0.5406 | | 0.0599 | 69.0 | 1725 | 0.0651 | 0.275 | 0.8849 | 4.7354 | 0.275 | 0.1479 | 0.2880 | 0.5181 | | 0.0599 | 70.0 | 1750 | 0.0651 | 0.275 | 0.8850 | 4.8271 | 0.275 | 0.1543 | 0.2893 | 0.5313 | | 0.0599 | 71.0 | 1775 | 0.0651 | 0.275 | 0.8849 | 4.7387 | 0.275 | 0.1549 | 0.2839 | 0.5454 | | 0.0599 | 72.0 | 1800 | 0.0652 | 0.275 | 0.8851 | 4.7695 | 0.275 | 0.1475 | 0.2849 | 0.5313 | | 0.0599 | 73.0 | 1825 | 0.0652 | 0.275 | 0.8848 | 4.7794 | 0.275 | 0.1418 | 0.2845 | 0.5159 | | 0.0599 | 74.0 | 1850 | 0.0652 | 0.265 | 0.8848 | 4.7832 | 0.265 | 0.1334 | 0.2827 | 0.5350 | | 0.0599 | 75.0 | 1875 | 0.0652 | 0.27 | 0.8849 | 4.7822 | 0.27 | 0.1388 | 0.2822 | 0.5478 | | 0.0599 | 76.0 | 1900 | 0.0652 | 0.26 | 0.8850 | 4.7813 | 0.26 | 0.1293 | 0.2720 | 0.5386 | | 0.0599 | 77.0 | 1925 | 0.0652 | 0.265 | 0.8849 | 4.7949 | 0.265 | 0.1365 | 0.2818 | 0.5456 | | 0.0599 | 78.0 | 1950 | 0.0652 | 0.28 | 0.8850 | 4.7960 | 0.28 | 0.1509 | 0.2878 | 0.5361 | | 0.0599 | 79.0 | 1975 | 0.0652 | 0.265 | 0.8849 | 4.7800 | 0.265 | 0.1353 | 0.2803 | 0.5407 | | 0.059 | 80.0 | 2000 | 0.0652 | 0.265 | 0.8849 | 4.8926 | 0.265 | 0.1429 | 0.2782 | 0.5381 | | 0.059 | 81.0 | 2025 | 0.0653 | 0.27 | 0.8850 | 4.7975 | 0.27 | 0.1388 | 0.2847 | 0.5436 | | 0.059 | 82.0 | 2050 | 0.0652 | 0.275 | 0.8849 | 4.7902 | 0.275 | 0.1505 | 0.2849 | 0.5342 | | 0.059 | 83.0 | 2075 | 0.0652 | 0.265 | 0.8850 | 4.7813 | 0.265 | 0.1435 | 0.2805 | 0.5388 | | 0.059 | 84.0 | 2100 | 0.0652 | 0.265 | 0.8849 | 4.7866 | 0.265 | 0.1457 | 0.2873 | 0.5358 | | 0.059 | 85.0 | 2125 | 0.0652 | 0.275 | 0.8850 | 4.7877 | 0.275 | 0.1514 | 0.2872 | 0.5395 | | 0.059 | 86.0 | 2150 | 0.0652 | 0.265 | 0.8849 | 4.7870 | 0.265 | 0.1453 | 0.2862 | 0.5424 | | 0.059 | 87.0 | 2175 | 0.0652 | 0.275 | 0.8849 | 4.7887 | 0.275 | 0.1517 | 0.2926 | 0.5312 | | 0.059 | 88.0 | 2200 | 0.0652 | 0.27 | 0.8849 | 4.7862 | 0.27 | 0.1494 | 0.2809 | 0.5357 | | 0.059 | 89.0 | 2225 | 0.0652 | 0.275 | 0.8849 | 4.7911 | 0.275 | 0.1515 | 0.2871 | 0.5317 | | 0.059 | 90.0 | 2250 | 0.0652 | 0.28 | 0.8849 | 4.7825 | 0.28 | 0.1540 | 0.2887 | 0.5305 | | 0.059 | 91.0 | 2275 | 0.0652 | 0.28 | 0.8849 | 4.7892 | 0.28 | 0.1547 | 0.2900 | 0.5339 | | 0.059 | 92.0 | 2300 | 0.0652 | 0.29 | 0.8848 | 4.7848 | 0.29 | 0.1642 | 0.2932 | 0.5252 | | 0.059 | 93.0 | 2325 | 0.0652 | 0.29 | 0.8848 | 4.7823 | 0.29 | 0.1634 | 0.2953 | 0.5243 | | 0.059 | 94.0 | 2350 | 0.0652 | 0.29 | 0.8849 | 4.7869 | 0.29 | 0.1631 | 0.2932 | 0.5239 | | 0.059 | 95.0 | 2375 | 0.0652 | 0.285 | 0.8849 | 4.7902 | 0.285 | 0.1615 | 0.2893 | 0.5254 | | 0.059 | 96.0 | 2400 | 0.0652 | 0.29 | 0.8849 | 4.7894 | 0.29 | 0.1634 | 0.2909 | 0.5256 | | 0.059 | 97.0 | 2425 | 0.0652 | 0.29 | 0.8849 | 4.7915 | 0.29 | 0.1642 | 0.2932 | 0.5244 | | 0.059 | 98.0 | 2450 | 0.0652 | 0.29 | 0.8849 | 4.7902 | 0.29 | 0.1642 | 0.2909 | 0.5244 | | 0.059 | 99.0 | 2475 | 0.0652 | 0.29 | 0.8849 | 4.7889 | 0.29 | 0.1634 | 0.2953 | 0.5241 | | 0.0586 | 100.0 | 2500 | 0.0652 | 0.29 | 0.8849 | 4.7897 | 0.29 | 0.1634 | 0.2953 | 0.5243 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/18-tiny_tobacco3482_kd_NKD_t1.0_g1.5

# 18-tiny_tobacco3482_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0957 - Accuracy: 0.805 - Brier Loss: 0.2927 - Nll: 1.1753 - F1 Micro: 0.805 - F1 Macro: 0.7833 - Ece: 0.1572 - Aurc: 0.0655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 4.7898 | 0.1 | 1.0292 | 9.4902 | 0.1000 | 0.0772 | 0.3220 | 0.9001 | | No log | 2.0 | 14 | 3.9970 | 0.1 | 0.9420 | 10.0981 | 0.1000 | 0.1071 | 0.2441 | 0.8581 | | No log | 3.0 | 21 | 3.6641 | 0.075 | 0.8956 | 9.5324 | 0.075 | 0.0777 | 0.1896 | 0.9137 | | No log | 4.0 | 28 | 3.6014 | 0.18 | 0.8691 | 9.6679 | 0.18 | 0.0781 | 0.2345 | 0.5824 | | No log | 5.0 | 35 | 3.5833 | 0.23 | 0.8347 | 9.6569 | 0.23 | 0.1572 | 0.2618 | 0.5205 | | No log | 6.0 | 42 | 3.5576 | 0.44 | 0.7860 | 5.9410 | 0.44 | 0.2946 | 0.3475 | 0.3232 | | No log | 7.0 | 49 | 3.5400 | 0.575 | 0.7404 | 4.2387 | 0.575 | 0.4638 | 0.4007 | 0.2294 | | No log | 8.0 | 56 | 3.5319 | 0.545 | 0.7181 | 4.5958 | 0.545 | 0.4482 | 0.3502 | 0.2374 | | No log | 9.0 | 63 | 3.5405 | 0.52 | 0.7002 | 3.9862 | 0.52 | 0.4101 | 0.3148 | 0.2506 | | No log | 10.0 | 70 | 3.5341 | 0.61 | 0.6897 | 3.2707 | 0.61 | 0.5118 | 0.3775 | 0.2235 | | No log | 11.0 | 77 | 3.5259 | 0.66 | 0.6771 | 2.6882 | 0.66 | 0.5201 | 0.4365 | 0.1420 | | No log | 12.0 | 84 | 3.5215 | 0.66 | 0.6463 | 2.4544 | 0.66 | 0.5387 | 0.3750 | 0.1664 | | No log | 13.0 | 91 | 3.5363 | 0.58 | 0.6232 | 2.3149 | 0.58 | 0.5090 | 0.3285 | 0.1858 | | No log | 14.0 | 98 | 3.5161 | 0.675 | 0.6008 | 2.6144 | 0.675 | 0.5411 | 0.3690 | 0.1237 | | No log | 15.0 | 105 | 3.5073 | 0.67 | 0.5845 | 2.1229 | 0.67 | 0.5577 | 0.3405 | 0.1350 | | No log | 16.0 | 112 | 3.5272 | 0.67 | 0.5338 | 2.4215 | 0.67 | 0.5603 | 0.3154 | 0.1325 | | No log | 17.0 | 119 | 3.5332 | 0.695 | 0.5367 | 2.1675 | 0.695 | 0.6056 | 0.3140 | 0.1071 | | No log | 18.0 | 126 | 3.5659 | 0.655 | 0.4841 | 1.9565 | 0.655 | 0.5559 | 0.2600 | 0.1365 | | No log | 19.0 | 133 | 3.5438 | 0.69 | 0.4817 | 1.8201 | 0.69 | 0.5735 | 0.2574 | 0.1202 | | No log | 20.0 | 140 | 3.5019 | 0.74 | 0.4725 | 1.6346 | 0.74 | 0.6486 | 0.2939 | 0.0931 | | No log | 21.0 | 147 | 3.5236 | 0.755 | 0.4407 | 1.3134 | 0.755 | 0.6811 | 0.2762 | 0.0820 | | No log | 22.0 | 154 | 3.5303 | 0.755 | 0.4143 | 1.2834 | 0.755 | 0.6843 | 0.2434 | 0.0806 | | No log | 23.0 | 161 | 3.5541 | 0.77 | 0.4034 | 1.4417 | 0.7700 | 0.6891 | 0.2382 | 0.0842 | | No log | 24.0 | 168 | 3.5675 | 0.765 | 0.3853 | 1.6692 | 0.765 | 0.7072 | 0.2309 | 0.0807 | | No log | 25.0 | 175 | 3.5411 | 0.745 | 0.3914 | 1.2777 | 0.745 | 0.6720 | 0.2271 | 0.0784 | | No log | 26.0 | 182 | 3.5877 | 0.75 | 0.3710 | 1.4838 | 0.75 | 0.6717 | 0.2082 | 0.0789 | | No log | 27.0 | 189 | 3.6026 | 0.77 | 0.3483 | 1.4211 | 0.7700 | 0.7018 | 0.2089 | 0.0694 | | No log | 28.0 | 196 | 3.6374 | 0.78 | 0.3365 | 1.3205 | 0.78 | 0.7181 | 0.1953 | 0.0694 | | No log | 29.0 | 203 | 3.7319 | 0.775 | 0.3538 | 1.2749 | 0.775 | 0.7012 | 0.2149 | 0.0814 | | No log | 30.0 | 210 | 3.6359 | 0.805 | 0.3291 | 1.3272 | 0.805 | 0.7761 | 0.1991 | 0.0637 | | No log | 31.0 | 217 | 3.7160 | 0.785 | 0.3337 | 1.2632 | 0.785 | 0.7445 | 0.1727 | 0.0757 | | No log | 32.0 | 224 | 3.6810 | 0.8 | 0.3234 | 1.3720 | 0.8000 | 0.7636 | 0.1999 | 0.0649 | | No log | 33.0 | 231 | 3.7139 | 0.82 | 0.3221 | 1.2150 | 0.82 | 0.7919 | 0.2051 | 0.0677 | | No log | 34.0 | 238 | 3.7286 | 0.795 | 0.3130 | 1.0622 | 0.795 | 0.7575 | 0.1919 | 0.0639 | | No log | 35.0 | 245 | 3.7807 | 0.795 | 0.3154 | 1.0146 | 0.795 | 0.7672 | 0.1565 | 0.0714 | | No log | 36.0 | 252 | 3.6802 | 0.815 | 0.3131 | 1.0083 | 0.815 | 0.7933 | 0.2051 | 0.0626 | | No log | 37.0 | 259 | 3.7369 | 0.81 | 0.3168 | 1.0017 | 0.81 | 0.7862 | 0.1792 | 0.0690 | | No log | 38.0 | 266 | 3.7638 | 0.82 | 0.2971 | 1.3357 | 0.82 | 0.7977 | 0.1913 | 0.0628 | | No log | 39.0 | 273 | 3.7415 | 0.825 | 0.2954 | 1.0423 | 0.825 | 0.8072 | 0.1893 | 0.0599 | | No log | 40.0 | 280 | 3.8005 | 0.785 | 0.3140 | 1.0817 | 0.785 | 0.7453 | 0.1694 | 0.0684 | | No log | 41.0 | 287 | 3.7901 | 0.82 | 0.3127 | 1.0853 | 0.82 | 0.7993 | 0.1789 | 0.0673 | | No log | 42.0 | 294 | 3.7811 | 0.825 | 0.3019 | 1.2712 | 0.825 | 0.8020 | 0.1644 | 0.0644 | | No log | 43.0 | 301 | 3.7689 | 0.81 | 0.3110 | 0.8553 | 0.81 | 0.7932 | 0.1785 | 0.0645 | | No log | 44.0 | 308 | 3.7796 | 0.82 | 0.2919 | 1.2589 | 0.82 | 0.7972 | 0.1875 | 0.0643 | | No log | 45.0 | 315 | 3.8005 | 0.805 | 0.3036 | 1.1993 | 0.805 | 0.7789 | 0.1840 | 0.0660 | | No log | 46.0 | 322 | 3.7811 | 0.82 | 0.2909 | 1.0962 | 0.82 | 0.8004 | 0.1735 | 0.0618 | | No log | 47.0 | 329 | 3.8145 | 0.8 | 0.3040 | 1.1968 | 0.8000 | 0.7759 | 0.1795 | 0.0671 | | No log | 48.0 | 336 | 3.7969 | 0.835 | 0.2816 | 1.1019 | 0.835 | 0.8118 | 0.1624 | 0.0603 | | No log | 49.0 | 343 | 3.8020 | 0.815 | 0.2855 | 1.0383 | 0.815 | 0.7978 | 0.1556 | 0.0639 | | No log | 50.0 | 350 | 3.8049 | 0.815 | 0.2884 | 1.1121 | 0.815 | 0.7935 | 0.1608 | 0.0616 | | No log | 51.0 | 357 | 3.8048 | 0.81 | 0.2873 | 1.1173 | 0.81 | 0.7898 | 0.1574 | 0.0632 | | No log | 52.0 | 364 | 3.8581 | 0.8 | 0.2923 | 1.1257 | 0.8000 | 0.7767 | 0.1436 | 0.0664 | | No log | 53.0 | 371 | 3.8565 | 0.79 | 0.2984 | 1.0513 | 0.79 | 0.7670 | 0.1622 | 0.0668 | | No log | 54.0 | 378 | 3.8787 | 0.805 | 0.2901 | 1.0619 | 0.805 | 0.7874 | 0.1335 | 0.0655 | | No log | 55.0 | 385 | 3.8777 | 0.805 | 0.2940 | 1.0378 | 0.805 | 0.7883 | 0.1450 | 0.0647 | | No log | 56.0 | 392 | 3.8743 | 0.805 | 0.2906 | 1.1702 | 0.805 | 0.7849 | 0.1610 | 0.0634 | | No log | 57.0 | 399 | 3.9082 | 0.795 | 0.2959 | 1.0951 | 0.795 | 0.7711 | 0.1761 | 0.0662 | | No log | 58.0 | 406 | 3.8894 | 0.8 | 0.2898 | 1.0979 | 0.8000 | 0.7816 | 0.1774 | 0.0638 | | No log | 59.0 | 413 | 3.9005 | 0.825 | 0.2914 | 1.2358 | 0.825 | 0.8088 | 0.1687 | 0.0637 | | No log | 60.0 | 420 | 3.9115 | 0.815 | 0.2863 | 1.0318 | 0.815 | 0.7928 | 0.1672 | 0.0640 | | No log | 61.0 | 427 | 3.9172 | 0.805 | 0.2956 | 1.1397 | 0.805 | 0.7884 | 0.1646 | 0.0667 | | No log | 62.0 | 434 | 3.8993 | 0.82 | 0.2862 | 1.2349 | 0.82 | 0.8001 | 0.1544 | 0.0645 | | No log | 63.0 | 441 | 3.9334 | 0.825 | 0.2896 | 1.1718 | 0.825 | 0.8061 | 0.1662 | 0.0646 | | No log | 64.0 | 448 | 3.9179 | 0.815 | 0.2861 | 1.1727 | 0.815 | 0.7966 | 0.1592 | 0.0650 | | No log | 65.0 | 455 | 3.9489 | 0.8 | 0.2981 | 1.1681 | 0.8000 | 0.7805 | 0.1522 | 0.0674 | | No log | 66.0 | 462 | 3.9372 | 0.81 | 0.2855 | 1.1041 | 0.81 | 0.7870 | 0.1709 | 0.0647 | | No log | 67.0 | 469 | 3.9651 | 0.8 | 0.2935 | 1.1723 | 0.8000 | 0.7816 | 0.1492 | 0.0667 | | No log | 68.0 | 476 | 3.9600 | 0.815 | 0.2903 | 1.1687 | 0.815 | 0.7950 | 0.1466 | 0.0650 | | No log | 69.0 | 483 | 3.9695 | 0.82 | 0.2908 | 1.1251 | 0.82 | 0.8026 | 0.1532 | 0.0654 | | No log | 70.0 | 490 | 3.9817 | 0.805 | 0.2915 | 1.1879 | 0.805 | 0.7861 | 0.1537 | 0.0657 | | No log | 71.0 | 497 | 3.9838 | 0.81 | 0.2899 | 1.1688 | 0.81 | 0.7892 | 0.1538 | 0.0648 | | 3.4085 | 72.0 | 504 | 3.9960 | 0.805 | 0.2910 | 1.1702 | 0.805 | 0.7904 | 0.1568 | 0.0657 | | 3.4085 | 73.0 | 511 | 4.0046 | 0.8 | 0.2931 | 1.1743 | 0.8000 | 0.7800 | 0.1529 | 0.0658 | | 3.4085 | 74.0 | 518 | 4.0115 | 0.815 | 0.2917 | 1.1718 | 0.815 | 0.7968 | 0.1589 | 0.0647 | | 3.4085 | 75.0 | 525 | 4.0205 | 0.805 | 0.2920 | 1.1719 | 0.805 | 0.7833 | 0.1575 | 0.0654 | | 3.4085 | 76.0 | 532 | 4.0272 | 0.805 | 0.2919 | 1.1725 | 0.805 | 0.7833 | 0.1547 | 0.0659 | | 3.4085 | 77.0 | 539 | 4.0323 | 0.81 | 0.2923 | 1.1720 | 0.81 | 0.7892 | 0.1547 | 0.0653 | | 3.4085 | 78.0 | 546 | 4.0364 | 0.81 | 0.2907 | 1.1715 | 0.81 | 0.7892 | 0.1607 | 0.0650 | | 3.4085 | 79.0 | 553 | 4.0405 | 0.81 | 0.2910 | 1.1716 | 0.81 | 0.7892 | 0.1451 | 0.0650 | | 3.4085 | 80.0 | 560 | 4.0476 | 0.81 | 0.2917 | 1.1743 | 0.81 | 0.7892 | 0.1453 | 0.0650 | | 3.4085 | 81.0 | 567 | 4.0529 | 0.805 | 0.2921 | 1.1736 | 0.805 | 0.7833 | 0.1573 | 0.0654 | | 3.4085 | 82.0 | 574 | 4.0570 | 0.805 | 0.2919 | 1.1741 | 0.805 | 0.7861 | 0.1717 | 0.0655 | | 3.4085 | 83.0 | 581 | 4.0601 | 0.81 | 0.2918 | 1.1727 | 0.81 | 0.7892 | 0.1508 | 0.0650 | | 3.4085 | 84.0 | 588 | 4.0643 | 0.81 | 0.2919 | 1.1743 | 0.81 | 0.7892 | 0.1507 | 0.0652 | | 3.4085 | 85.0 | 595 | 4.0678 | 0.81 | 0.2922 | 1.1744 | 0.81 | 0.7892 | 0.1552 | 0.0651 | | 3.4085 | 86.0 | 602 | 4.0743 | 0.81 | 0.2925 | 1.1746 | 0.81 | 0.7892 | 0.1526 | 0.0651 | | 3.4085 | 87.0 | 609 | 4.0758 | 0.805 | 0.2924 | 1.1753 | 0.805 | 0.7833 | 0.1718 | 0.0653 | | 3.4085 | 88.0 | 616 | 4.0796 | 0.805 | 0.2924 | 1.1758 | 0.805 | 0.7833 | 0.1567 | 0.0654 | | 3.4085 | 89.0 | 623 | 4.0803 | 0.81 | 0.2920 | 1.1742 | 0.81 | 0.7892 | 0.1587 | 0.0650 | | 3.4085 | 90.0 | 630 | 4.0842 | 0.81 | 0.2925 | 1.1744 | 0.81 | 0.7892 | 0.1529 | 0.0651 | | 3.4085 | 91.0 | 637 | 4.0864 | 0.805 | 0.2926 | 1.1752 | 0.805 | 0.7833 | 0.1568 | 0.0654 | | 3.4085 | 92.0 | 644 | 4.0880 | 0.81 | 0.2925 | 1.1757 | 0.81 | 0.7892 | 0.1526 | 0.0651 | | 3.4085 | 93.0 | 651 | 4.0903 | 0.805 | 0.2927 | 1.1752 | 0.805 | 0.7833 | 0.1567 | 0.0654 | | 3.4085 | 94.0 | 658 | 4.0918 | 0.805 | 0.2927 | 1.1750 | 0.805 | 0.7833 | 0.1572 | 0.0655 | | 3.4085 | 95.0 | 665 | 4.0927 | 0.805 | 0.2926 | 1.1750 | 0.805 | 0.7833 | 0.1570 | 0.0655 | | 3.4085 | 96.0 | 672 | 4.0937 | 0.805 | 0.2927 | 1.1751 | 0.805 | 0.7833 | 0.1572 | 0.0655 | | 3.4085 | 97.0 | 679 | 4.0946 | 0.805 | 0.2926 | 1.1750 | 0.805 | 0.7833 | 0.1573 | 0.0655 | | 3.4085 | 98.0 | 686 | 4.0950 | 0.805 | 0.2926 | 1.1752 | 0.805 | 0.7833 | 0.1572 | 0.0655 | | 3.4085 | 99.0 | 693 | 4.0955 | 0.805 | 0.2927 | 1.1753 | 0.805 | 0.7833 | 0.1572 | 0.0655 | | 3.4085 | 100.0 | 700 | 4.0957 | 0.805 | 0.2927 | 1.1753 | 0.805 | 0.7833 | 0.1572 | 0.0655 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/18-tiny_tobacco3482_hint_

# 18-tiny_tobacco3482_hint_ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 71.6043 - Accuracy: 0.85 - Brier Loss: 0.2638 - Nll: 1.3122 - F1 Micro: 0.85 - F1 Macro: 0.8425 - Ece: 0.1342 - Aurc: 0.0355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 74.9320 | 0.26 | 0.8713 | 4.9021 | 0.26 | 0.1920 | 0.3072 | 0.7822 | | No log | 2.0 | 50 | 74.1171 | 0.54 | 0.5982 | 2.7516 | 0.54 | 0.4539 | 0.2763 | 0.2591 | | No log | 3.0 | 75 | 73.6525 | 0.685 | 0.4431 | 1.5999 | 0.685 | 0.6196 | 0.1890 | 0.1355 | | No log | 4.0 | 100 | 73.3675 | 0.735 | 0.3894 | 1.5393 | 0.735 | 0.7201 | 0.2334 | 0.1214 | | No log | 5.0 | 125 | 73.0199 | 0.785 | 0.3130 | 1.6799 | 0.785 | 0.7426 | 0.1738 | 0.0705 | | No log | 6.0 | 150 | 72.7837 | 0.8 | 0.3068 | 1.5290 | 0.8000 | 0.7711 | 0.1531 | 0.0582 | | No log | 7.0 | 175 | 72.7218 | 0.76 | 0.3913 | 1.8944 | 0.76 | 0.7334 | 0.1998 | 0.0987 | | No log | 8.0 | 200 | 72.7351 | 0.695 | 0.4805 | 1.4948 | 0.695 | 0.6565 | 0.2585 | 0.1329 | | No log | 9.0 | 225 | 72.2520 | 0.79 | 0.3279 | 1.5198 | 0.79 | 0.7440 | 0.1878 | 0.0704 | | No log | 10.0 | 250 | 72.3792 | 0.785 | 0.3370 | 1.7982 | 0.785 | 0.7687 | 0.1655 | 0.0757 | | No log | 11.0 | 275 | 72.2408 | 0.765 | 0.3542 | 1.6304 | 0.765 | 0.7364 | 0.1838 | 0.0660 | | No log | 12.0 | 300 | 72.1003 | 0.76 | 0.3664 | 1.4090 | 0.76 | 0.7247 | 0.1832 | 0.0817 | | No log | 13.0 | 325 | 72.4671 | 0.77 | 0.3886 | 1.7820 | 0.7700 | 0.7380 | 0.1985 | 0.0833 | | No log | 14.0 | 350 | 72.1519 | 0.785 | 0.3633 | 1.5939 | 0.785 | 0.7563 | 0.1814 | 0.0687 | | No log | 15.0 | 375 | 72.2206 | 0.775 | 0.3723 | 1.6025 | 0.775 | 0.7406 | 0.1824 | 0.0800 | | No log | 16.0 | 400 | 72.0554 | 0.805 | 0.3080 | 1.4448 | 0.805 | 0.7520 | 0.1577 | 0.0485 | | No log | 17.0 | 425 | 72.3130 | 0.8 | 0.3412 | 1.7255 | 0.8000 | 0.7653 | 0.1637 | 0.0694 | | No log | 18.0 | 450 | 72.1019 | 0.815 | 0.3228 | 1.4827 | 0.815 | 0.7918 | 0.1753 | 0.0592 | | No log | 19.0 | 475 | 72.2643 | 0.8 | 0.3558 | 1.5960 | 0.8000 | 0.7604 | 0.1677 | 0.0737 | | 71.4928 | 20.0 | 500 | 71.9509 | 0.78 | 0.3398 | 1.5615 | 0.78 | 0.7596 | 0.1683 | 0.0561 | | 71.4928 | 21.0 | 525 | 71.9389 | 0.82 | 0.2947 | 1.5336 | 0.82 | 0.8008 | 0.1561 | 0.0538 | | 71.4928 | 22.0 | 550 | 72.0399 | 0.83 | 0.2843 | 1.4576 | 0.83 | 0.7914 | 0.1347 | 0.0523 | | 71.4928 | 23.0 | 575 | 72.0529 | 0.815 | 0.3263 | 1.3174 | 0.815 | 0.7923 | 0.1677 | 0.0523 | | 71.4928 | 24.0 | 600 | 72.3487 | 0.775 | 0.3838 | 1.5418 | 0.775 | 0.7560 | 0.1880 | 0.0794 | | 71.4928 | 25.0 | 625 | 71.9154 | 0.825 | 0.2949 | 1.2538 | 0.825 | 0.7992 | 0.1471 | 0.0584 | | 71.4928 | 26.0 | 650 | 72.0222 | 0.815 | 0.3151 | 1.5809 | 0.815 | 0.7830 | 0.1601 | 0.0594 | | 71.4928 | 27.0 | 675 | 72.0422 | 0.815 | 0.3269 | 1.5161 | 0.815 | 0.7954 | 0.1606 | 0.0597 | | 71.4928 | 28.0 | 700 | 72.0172 | 0.845 | 0.2828 | 1.3388 | 0.845 | 0.8350 | 0.1447 | 0.0649 | | 71.4928 | 29.0 | 725 | 71.9113 | 0.84 | 0.2685 | 1.2082 | 0.8400 | 0.8202 | 0.1365 | 0.0562 | | 71.4928 | 30.0 | 750 | 71.9516 | 0.84 | 0.2856 | 1.2664 | 0.8400 | 0.8359 | 0.1415 | 0.0563 | | 71.4928 | 31.0 | 775 | 71.8583 | 0.835 | 0.2782 | 1.2979 | 0.835 | 0.8277 | 0.1447 | 0.0545 | | 71.4928 | 32.0 | 800 | 71.9071 | 0.84 | 0.2766 | 1.2772 | 0.8400 | 0.8359 | 0.1378 | 0.0546 | | 71.4928 | 33.0 | 825 | 71.8580 | 0.85 | 0.2699 | 1.2985 | 0.85 | 0.8482 | 0.1351 | 0.0525 | | 71.4928 | 34.0 | 850 | 71.8499 | 0.835 | 0.2872 | 1.3022 | 0.835 | 0.8292 | 0.1462 | 0.0532 | | 71.4928 | 35.0 | 875 | 72.0085 | 0.84 | 0.2897 | 1.3042 | 0.8400 | 0.8323 | 0.1420 | 0.0616 | | 71.4928 | 36.0 | 900 | 71.8423 | 0.82 | 0.2929 | 1.2266 | 0.82 | 0.8056 | 0.1543 | 0.0521 | | 71.4928 | 37.0 | 925 | 71.7886 | 0.845 | 0.2807 | 1.2181 | 0.845 | 0.8254 | 0.1332 | 0.0400 | | 71.4928 | 38.0 | 950 | 71.8857 | 0.83 | 0.2877 | 1.4036 | 0.83 | 0.8166 | 0.1490 | 0.0480 | | 71.4928 | 39.0 | 975 | 71.9388 | 0.83 | 0.2877 | 1.3374 | 0.83 | 0.8119 | 0.1459 | 0.0451 | | 70.1673 | 40.0 | 1000 | 72.0368 | 0.8 | 0.3368 | 1.7112 | 0.8000 | 0.7897 | 0.1741 | 0.0578 | | 70.1673 | 41.0 | 1025 | 72.0295 | 0.8 | 0.3208 | 1.5473 | 0.8000 | 0.7862 | 0.1587 | 0.0622 | | 70.1673 | 42.0 | 1050 | 71.7048 | 0.86 | 0.2547 | 1.3240 | 0.8600 | 0.8374 | 0.1257 | 0.0408 | | 70.1673 | 43.0 | 1075 | 71.7541 | 0.835 | 0.2680 | 1.4095 | 0.835 | 0.8178 | 0.1418 | 0.0445 | | 70.1673 | 44.0 | 1100 | 71.7746 | 0.845 | 0.2721 | 1.5529 | 0.845 | 0.8383 | 0.1412 | 0.0405 | | 70.1673 | 45.0 | 1125 | 71.7661 | 0.83 | 0.2908 | 1.5315 | 0.83 | 0.8104 | 0.1415 | 0.0437 | | 70.1673 | 46.0 | 1150 | 71.7563 | 0.84 | 0.2787 | 1.4088 | 0.8400 | 0.8238 | 0.1360 | 0.0416 | | 70.1673 | 47.0 | 1175 | 71.7670 | 0.84 | 0.2709 | 1.2801 | 0.8400 | 0.8303 | 0.1322 | 0.0401 | | 70.1673 | 48.0 | 1200 | 71.7458 | 0.84 | 0.2699 | 1.4180 | 0.8400 | 0.8265 | 0.1433 | 0.0397 | | 70.1673 | 49.0 | 1225 | 71.7226 | 0.84 | 0.2653 | 1.4126 | 0.8400 | 0.8265 | 0.1282 | 0.0390 | | 70.1673 | 50.0 | 1250 | 71.7163 | 0.85 | 0.2608 | 1.4227 | 0.85 | 0.8402 | 0.1339 | 0.0394 | | 70.1673 | 51.0 | 1275 | 71.7044 | 0.845 | 0.2612 | 1.4130 | 0.845 | 0.8371 | 0.1314 | 0.0387 | | 70.1673 | 52.0 | 1300 | 71.6821 | 0.85 | 0.2545 | 1.4880 | 0.85 | 0.8392 | 0.1286 | 0.0383 | | 70.1673 | 53.0 | 1325 | 71.6764 | 0.845 | 0.2598 | 1.3776 | 0.845 | 0.8301 | 0.1299 | 0.0377 | | 70.1673 | 54.0 | 1350 | 71.6750 | 0.855 | 0.2590 | 1.3404 | 0.855 | 0.8479 | 0.1361 | 0.0383 | | 70.1673 | 55.0 | 1375 | 71.7192 | 0.855 | 0.2543 | 1.4804 | 0.855 | 0.8482 | 0.1346 | 0.0388 | | 70.1673 | 56.0 | 1400 | 71.6907 | 0.85 | 0.2552 | 1.4102 | 0.85 | 0.8389 | 0.1274 | 0.0377 | | 70.1673 | 57.0 | 1425 | 71.6778 | 0.85 | 0.2572 | 1.4026 | 0.85 | 0.8392 | 0.1319 | 0.0389 | | 70.1673 | 58.0 | 1450 | 71.6735 | 0.85 | 0.2559 | 1.4062 | 0.85 | 0.8394 | 0.1288 | 0.0386 | | 70.1673 | 59.0 | 1475 | 71.6938 | 0.855 | 0.2549 | 1.4710 | 0.855 | 0.8482 | 0.1309 | 0.0387 | | 69.9715 | 60.0 | 1500 | 71.6799 | 0.85 | 0.2599 | 1.3989 | 0.85 | 0.8389 | 0.1309 | 0.0384 | | 69.9715 | 61.0 | 1525 | 71.6753 | 0.865 | 0.2539 | 1.3555 | 0.865 | 0.8619 | 0.1201 | 0.0374 | | 69.9715 | 62.0 | 1550 | 71.6657 | 0.855 | 0.2562 | 1.4005 | 0.855 | 0.8453 | 0.1301 | 0.0365 | | 69.9715 | 63.0 | 1575 | 71.6941 | 0.855 | 0.2569 | 1.4083 | 0.855 | 0.8453 | 0.1297 | 0.0369 | | 69.9715 | 64.0 | 1600 | 71.6430 | 0.85 | 0.2567 | 1.3933 | 0.85 | 0.8395 | 0.1239 | 0.0370 | | 69.9715 | 65.0 | 1625 | 71.6666 | 0.85 | 0.2582 | 1.4014 | 0.85 | 0.8395 | 0.1357 | 0.0375 | | 69.9715 | 66.0 | 1650 | 71.6550 | 0.85 | 0.2578 | 1.3849 | 0.85 | 0.8395 | 0.1253 | 0.0370 | | 69.9715 | 67.0 | 1675 | 71.6321 | 0.855 | 0.2573 | 1.3932 | 0.855 | 0.8466 | 0.1276 | 0.0362 | | 69.9715 | 68.0 | 1700 | 71.6237 | 0.855 | 0.2576 | 1.3976 | 0.855 | 0.8453 | 0.1231 | 0.0374 | | 69.9715 | 69.0 | 1725 | 71.6287 | 0.85 | 0.2589 | 1.3914 | 0.85 | 0.8403 | 0.1299 | 0.0366 | | 69.9715 | 70.0 | 1750 | 71.6325 | 0.85 | 0.2580 | 1.3907 | 0.85 | 0.8425 | 0.1321 | 0.0365 | | 69.9715 | 71.0 | 1775 | 71.6175 | 0.85 | 0.2572 | 1.3914 | 0.85 | 0.8412 | 0.1318 | 0.0365 | | 69.9715 | 72.0 | 1800 | 71.6208 | 0.85 | 0.2591 | 1.3860 | 0.85 | 0.8425 | 0.1325 | 0.0355 | | 69.9715 | 73.0 | 1825 | 71.6157 | 0.85 | 0.2600 | 1.3894 | 0.85 | 0.8425 | 0.1335 | 0.0361 | | 69.9715 | 74.0 | 1850 | 71.6405 | 0.85 | 0.2632 | 1.3335 | 0.85 | 0.8425 | 0.1306 | 0.0359 | | 69.9715 | 75.0 | 1875 | 71.6099 | 0.85 | 0.2586 | 1.3899 | 0.85 | 0.8425 | 0.1283 | 0.0360 | | 69.9715 | 76.0 | 1900 | 71.6058 | 0.85 | 0.2599 | 1.3220 | 0.85 | 0.8425 | 0.1260 | 0.0357 | | 69.9715 | 77.0 | 1925 | 71.6096 | 0.85 | 0.2591 | 1.3859 | 0.85 | 0.8425 | 0.1279 | 0.0355 | | 69.9715 | 78.0 | 1950 | 71.6213 | 0.85 | 0.2604 | 1.3875 | 0.85 | 0.8425 | 0.1284 | 0.0351 | | 69.9715 | 79.0 | 1975 | 71.6240 | 0.85 | 0.2610 | 1.3867 | 0.85 | 0.8425 | 0.1347 | 0.0356 | | 69.8814 | 80.0 | 2000 | 71.6246 | 0.855 | 0.2608 | 1.3183 | 0.855 | 0.8483 | 0.1238 | 0.0354 | | 69.8814 | 81.0 | 2025 | 71.6133 | 0.85 | 0.2595 | 1.3177 | 0.85 | 0.8425 | 0.1268 | 0.0357 | | 69.8814 | 82.0 | 2050 | 71.6120 | 0.85 | 0.2593 | 1.3873 | 0.85 | 0.8425 | 0.1278 | 0.0353 | | 69.8814 | 83.0 | 2075 | 71.6183 | 0.85 | 0.2600 | 1.3220 | 0.85 | 0.8425 | 0.1342 | 0.0356 | | 69.8814 | 84.0 | 2100 | 71.6008 | 0.85 | 0.2610 | 1.3168 | 0.85 | 0.8425 | 0.1359 | 0.0358 | | 69.8814 | 85.0 | 2125 | 71.5986 | 0.85 | 0.2621 | 1.3156 | 0.85 | 0.8425 | 0.1327 | 0.0355 | | 69.8814 | 86.0 | 2150 | 71.6000 | 0.85 | 0.2609 | 1.3167 | 0.85 | 0.8425 | 0.1311 | 0.0354 | | 69.8814 | 87.0 | 2175 | 71.6006 | 0.855 | 0.2611 | 1.3171 | 0.855 | 0.8483 | 0.1251 | 0.0353 | | 69.8814 | 88.0 | 2200 | 71.6033 | 0.85 | 0.2620 | 1.3132 | 0.85 | 0.8425 | 0.1331 | 0.0357 | | 69.8814 | 89.0 | 2225 | 71.6176 | 0.855 | 0.2635 | 1.3142 | 0.855 | 0.8483 | 0.1326 | 0.0357 | | 69.8814 | 90.0 | 2250 | 71.6201 | 0.855 | 0.2636 | 1.3126 | 0.855 | 0.8483 | 0.1282 | 0.0356 | | 69.8814 | 91.0 | 2275 | 71.6128 | 0.85 | 0.2629 | 1.3126 | 0.85 | 0.8425 | 0.1273 | 0.0356 | | 69.8814 | 92.0 | 2300 | 71.6086 | 0.855 | 0.2631 | 1.3147 | 0.855 | 0.8515 | 0.1261 | 0.0358 | | 69.8814 | 93.0 | 2325 | 71.6010 | 0.85 | 0.2638 | 1.3122 | 0.85 | 0.8425 | 0.1292 | 0.0356 | | 69.8814 | 94.0 | 2350 | 71.6053 | 0.85 | 0.2636 | 1.3125 | 0.85 | 0.8425 | 0.1269 | 0.0354 | | 69.8814 | 95.0 | 2375 | 71.6004 | 0.85 | 0.2640 | 1.3128 | 0.85 | 0.8425 | 0.1346 | 0.0356 | | 69.8814 | 96.0 | 2400 | 71.6035 | 0.85 | 0.2644 | 1.3128 | 0.85 | 0.8425 | 0.1346 | 0.0356 | | 69.8814 | 97.0 | 2425 | 71.6027 | 0.85 | 0.2639 | 1.3117 | 0.85 | 0.8425 | 0.1343 | 0.0355 | | 69.8814 | 98.0 | 2450 | 71.6039 | 0.85 | 0.2639 | 1.3117 | 0.85 | 0.8425 | 0.1343 | 0.0354 | | 69.8814 | 99.0 | 2475 | 71.6018 | 0.85 | 0.2640 | 1.3122 | 0.85 | 0.8425 | 0.1342 | 0.0356 | | 69.8448 | 100.0 | 2500 | 71.6043 | 0.85 | 0.2638 | 1.3122 | 0.85 | 0.8425 | 0.1342 | 0.0355 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

marcoi/vit-base-patch16-224-finetuned-flower

# vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

[ "daisy", "dandelion", "roses", "sunflowers", "tulips" ]

jordyvl/81-tiny_tobacco3482_og_simkd

# 81-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 230.0462 - Accuracy: 0.845 - Brier Loss: 0.2451 - Nll: 1.1250 - F1 Micro: 0.845 - F1 Macro: 0.8350 - Ece: 0.1115 - Aurc: 0.0383 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 238.2967 | 0.275 | 0.8789 | 6.2995 | 0.275 | 0.1849 | 0.2898 | 0.4790 | | No log | 2.0 | 100 | 236.7866 | 0.41 | 0.7214 | 3.0130 | 0.41 | 0.2599 | 0.2544 | 0.3985 | | No log | 3.0 | 150 | 235.3687 | 0.59 | 0.5690 | 2.4483 | 0.59 | 0.4507 | 0.2464 | 0.2336 | | No log | 4.0 | 200 | 234.7639 | 0.65 | 0.4658 | 2.3318 | 0.65 | 0.4996 | 0.2365 | 0.1474 | | No log | 5.0 | 250 | 234.0798 | 0.675 | 0.4692 | 2.3351 | 0.675 | 0.5158 | 0.2359 | 0.1335 | | No log | 6.0 | 300 | 234.2002 | 0.68 | 0.4267 | 2.1570 | 0.68 | 0.5724 | 0.1990 | 0.1124 | | No log | 7.0 | 350 | 234.1154 | 0.665 | 0.4874 | 2.9269 | 0.665 | 0.5631 | 0.2249 | 0.1259 | | No log | 8.0 | 400 | 233.2390 | 0.77 | 0.3224 | 1.8874 | 0.7700 | 0.6850 | 0.1754 | 0.0695 | | No log | 9.0 | 450 | 233.1396 | 0.755 | 0.3583 | 2.1139 | 0.755 | 0.7060 | 0.1573 | 0.0802 | | 234.3515 | 10.0 | 500 | 233.5895 | 0.705 | 0.4653 | 3.2740 | 0.705 | 0.6813 | 0.2183 | 0.1180 | | 234.3515 | 11.0 | 550 | 233.0481 | 0.745 | 0.3803 | 2.7330 | 0.745 | 0.6697 | 0.1730 | 0.0936 | | 234.3515 | 12.0 | 600 | 232.9339 | 0.78 | 0.3319 | 1.8094 | 0.78 | 0.7416 | 0.1700 | 0.0660 | | 234.3515 | 13.0 | 650 | 233.0181 | 0.755 | 0.3182 | 1.9630 | 0.755 | 0.7596 | 0.1772 | 0.0823 | | 234.3515 | 14.0 | 700 | 232.4934 | 0.815 | 0.2659 | 1.3869 | 0.815 | 0.7749 | 0.1512 | 0.0511 | | 234.3515 | 15.0 | 750 | 232.6922 | 0.79 | 0.3361 | 1.8793 | 0.79 | 0.7671 | 0.1633 | 0.0926 | | 234.3515 | 16.0 | 800 | 232.3391 | 0.815 | 0.2785 | 1.5488 | 0.815 | 0.8038 | 0.1476 | 0.0549 | | 234.3515 | 17.0 | 850 | 232.5412 | 0.775 | 0.3100 | 2.0192 | 0.775 | 0.7675 | 0.1617 | 0.0502 | | 234.3515 | 18.0 | 900 | 232.5307 | 0.785 | 0.3211 | 1.6793 | 0.785 | 0.7541 | 0.1679 | 0.0676 | | 234.3515 | 19.0 | 950 | 232.3055 | 0.825 | 0.2862 | 1.5993 | 0.825 | 0.8204 | 0.1443 | 0.0620 | | 231.365 | 20.0 | 1000 | 232.5636 | 0.78 | 0.3431 | 2.6048 | 0.78 | 0.7835 | 0.1658 | 0.0847 | | 231.365 | 21.0 | 1050 | 231.8748 | 0.845 | 0.2737 | 1.4332 | 0.845 | 0.8454 | 0.1616 | 0.0602 | | 231.365 | 22.0 | 1100 | 232.1032 | 0.825 | 0.2557 | 1.6240 | 0.825 | 0.8290 | 0.1422 | 0.0442 | | 231.365 | 23.0 | 1150 | 231.7278 | 0.835 | 0.2740 | 1.6260 | 0.835 | 0.8246 | 0.1541 | 0.0613 | | 231.365 | 24.0 | 1200 | 231.9350 | 0.84 | 0.2636 | 1.6028 | 0.8400 | 0.8410 | 0.1472 | 0.0471 | | 231.365 | 25.0 | 1250 | 231.6054 | 0.82 | 0.2753 | 1.3835 | 0.82 | 0.8090 | 0.1526 | 0.0478 | | 231.365 | 26.0 | 1300 | 231.6516 | 0.84 | 0.2737 | 1.3849 | 0.8400 | 0.8354 | 0.1492 | 0.0543 | | 231.365 | 27.0 | 1350 | 231.5453 | 0.84 | 0.2536 | 1.1384 | 0.8400 | 0.8265 | 0.1363 | 0.0431 | | 231.365 | 28.0 | 1400 | 231.4833 | 0.85 | 0.2292 | 1.1206 | 0.85 | 0.8350 | 0.1250 | 0.0362 | | 231.365 | 29.0 | 1450 | 231.3722 | 0.815 | 0.2856 | 1.1706 | 0.815 | 0.8038 | 0.1606 | 0.0486 | | 230.3328 | 30.0 | 1500 | 231.3517 | 0.84 | 0.2608 | 1.3387 | 0.8400 | 0.8382 | 0.1366 | 0.0483 | | 230.3328 | 31.0 | 1550 | 231.3705 | 0.815 | 0.2724 | 1.2558 | 0.815 | 0.7992 | 0.1448 | 0.0463 | | 230.3328 | 32.0 | 1600 | 231.4319 | 0.84 | 0.2588 | 1.0691 | 0.8400 | 0.8301 | 0.1317 | 0.0435 | | 230.3328 | 33.0 | 1650 | 231.2119 | 0.86 | 0.2323 | 1.1693 | 0.8600 | 0.8609 | 0.1229 | 0.0470 | | 230.3328 | 34.0 | 1700 | 231.2836 | 0.83 | 0.2477 | 1.1294 | 0.83 | 0.8201 | 0.1398 | 0.0406 | | 230.3328 | 35.0 | 1750 | 231.2669 | 0.845 | 0.2569 | 1.1508 | 0.845 | 0.8369 | 0.1357 | 0.0449 | | 230.3328 | 36.0 | 1800 | 231.0634 | 0.85 | 0.2422 | 1.0830 | 0.85 | 0.8404 | 0.1372 | 0.0419 | | 230.3328 | 37.0 | 1850 | 231.1141 | 0.85 | 0.2398 | 1.0879 | 0.85 | 0.8455 | 0.1347 | 0.0437 | | 230.3328 | 38.0 | 1900 | 231.0520 | 0.815 | 0.2626 | 1.2325 | 0.815 | 0.8077 | 0.1304 | 0.0478 | | 230.3328 | 39.0 | 1950 | 230.9089 | 0.83 | 0.2507 | 1.1245 | 0.83 | 0.8258 | 0.1347 | 0.0487 | | 229.6409 | 40.0 | 2000 | 231.0532 | 0.86 | 0.2207 | 1.1258 | 0.8600 | 0.8464 | 0.1206 | 0.0428 | | 229.6409 | 41.0 | 2050 | 230.9307 | 0.855 | 0.2350 | 1.1326 | 0.855 | 0.8498 | 0.1284 | 0.0401 | | 229.6409 | 42.0 | 2100 | 230.8493 | 0.86 | 0.2306 | 1.1075 | 0.8600 | 0.8548 | 0.1441 | 0.0417 | | 229.6409 | 43.0 | 2150 | 230.7516 | 0.87 | 0.2198 | 1.0312 | 0.87 | 0.8587 | 0.1184 | 0.0404 | | 229.6409 | 44.0 | 2200 | 230.8540 | 0.85 | 0.2485 | 1.1724 | 0.85 | 0.8444 | 0.1425 | 0.0451 | | 229.6409 | 45.0 | 2250 | 230.7995 | 0.86 | 0.2284 | 1.1183 | 0.8600 | 0.8490 | 0.1284 | 0.0368 | | 229.6409 | 46.0 | 2300 | 230.7162 | 0.825 | 0.2701 | 1.1206 | 0.825 | 0.8159 | 0.1390 | 0.0437 | | 229.6409 | 47.0 | 2350 | 230.5593 | 0.855 | 0.2341 | 1.2242 | 0.855 | 0.8459 | 0.1226 | 0.0392 | | 229.6409 | 48.0 | 2400 | 230.6472 | 0.86 | 0.2377 | 1.0233 | 0.8600 | 0.8558 | 0.1319 | 0.0354 | | 229.6409 | 49.0 | 2450 | 230.7080 | 0.84 | 0.2548 | 1.1208 | 0.8400 | 0.8294 | 0.1484 | 0.0426 | | 229.142 | 50.0 | 2500 | 230.5862 | 0.845 | 0.2543 | 1.2129 | 0.845 | 0.8322 | 0.1358 | 0.0415 | | 229.142 | 51.0 | 2550 | 230.6550 | 0.845 | 0.2462 | 1.0937 | 0.845 | 0.8333 | 0.1306 | 0.0411 | | 229.142 | 52.0 | 2600 | 230.5789 | 0.835 | 0.2595 | 1.1393 | 0.835 | 0.8249 | 0.1369 | 0.0428 | | 229.142 | 53.0 | 2650 | 230.5895 | 0.85 | 0.2519 | 1.0185 | 0.85 | 0.8447 | 0.1263 | 0.0439 | | 229.142 | 54.0 | 2700 | 230.4955 | 0.86 | 0.2402 | 1.0837 | 0.8600 | 0.8590 | 0.1382 | 0.0394 | | 229.142 | 55.0 | 2750 | 230.4579 | 0.84 | 0.2560 | 1.1514 | 0.8400 | 0.8312 | 0.1439 | 0.0431 | | 229.142 | 56.0 | 2800 | 230.5190 | 0.845 | 0.2527 | 1.2868 | 0.845 | 0.8334 | 0.1342 | 0.0406 | | 229.142 | 57.0 | 2850 | 230.4989 | 0.84 | 0.2536 | 1.0785 | 0.8400 | 0.8278 | 0.1377 | 0.0397 | | 229.142 | 58.0 | 2900 | 230.4445 | 0.84 | 0.2416 | 1.2104 | 0.8400 | 0.8340 | 0.1339 | 0.0383 | | 229.142 | 59.0 | 2950 | 230.3159 | 0.87 | 0.2211 | 1.1138 | 0.87 | 0.8630 | 0.1171 | 0.0394 | | 228.7442 | 60.0 | 3000 | 230.3405 | 0.835 | 0.2603 | 1.1327 | 0.835 | 0.8273 | 0.1422 | 0.0416 | | 228.7442 | 61.0 | 3050 | 230.3892 | 0.845 | 0.2395 | 1.1619 | 0.845 | 0.8308 | 0.1263 | 0.0377 | | 228.7442 | 62.0 | 3100 | 230.3728 | 0.84 | 0.2478 | 1.0053 | 0.8400 | 0.8291 | 0.1208 | 0.0389 | | 228.7442 | 63.0 | 3150 | 230.2486 | 0.845 | 0.2545 | 1.1989 | 0.845 | 0.8438 | 0.1364 | 0.0434 | | 228.7442 | 64.0 | 3200 | 230.2353 | 0.84 | 0.2512 | 1.1260 | 0.8400 | 0.8330 | 0.1191 | 0.0430 | | 228.7442 | 65.0 | 3250 | 230.2041 | 0.845 | 0.2429 | 1.1529 | 0.845 | 0.8384 | 0.1343 | 0.0401 | | 228.7442 | 66.0 | 3300 | 230.2439 | 0.845 | 0.2511 | 1.1125 | 0.845 | 0.8366 | 0.1367 | 0.0430 | | 228.7442 | 67.0 | 3350 | 230.1510 | 0.85 | 0.2471 | 1.0528 | 0.85 | 0.8457 | 0.1414 | 0.0402 | | 228.7442 | 68.0 | 3400 | 230.2274 | 0.85 | 0.2455 | 1.1150 | 0.85 | 0.8397 | 0.1311 | 0.0427 | | 228.7442 | 69.0 | 3450 | 230.2165 | 0.845 | 0.2524 | 1.0517 | 0.845 | 0.8421 | 0.1312 | 0.0410 | | 228.4757 | 70.0 | 3500 | 230.1976 | 0.835 | 0.2600 | 1.0845 | 0.835 | 0.8258 | 0.1353 | 0.0410 | | 228.4757 | 71.0 | 3550 | 230.1062 | 0.85 | 0.2487 | 1.1447 | 0.85 | 0.8410 | 0.1297 | 0.0427 | | 228.4757 | 72.0 | 3600 | 229.9867 | 0.835 | 0.2584 | 1.0641 | 0.835 | 0.8273 | 0.1236 | 0.0440 | | 228.4757 | 73.0 | 3650 | 230.1918 | 0.845 | 0.2411 | 1.1521 | 0.845 | 0.8363 | 0.1373 | 0.0389 | | 228.4757 | 74.0 | 3700 | 230.0781 | 0.85 | 0.2524 | 1.0980 | 0.85 | 0.8390 | 0.1298 | 0.0409 | | 228.4757 | 75.0 | 3750 | 230.1432 | 0.835 | 0.2554 | 1.0967 | 0.835 | 0.8230 | 0.1227 | 0.0407 | | 228.4757 | 76.0 | 3800 | 230.1512 | 0.84 | 0.2535 | 1.0945 | 0.8400 | 0.8295 | 0.1321 | 0.0422 | | 228.4757 | 77.0 | 3850 | 230.0682 | 0.84 | 0.2502 | 1.0301 | 0.8400 | 0.8370 | 0.1312 | 0.0403 | | 228.4757 | 78.0 | 3900 | 230.0357 | 0.835 | 0.2521 | 1.1572 | 0.835 | 0.8293 | 0.1244 | 0.0412 | | 228.4757 | 79.0 | 3950 | 230.1252 | 0.845 | 0.2509 | 1.0961 | 0.845 | 0.8381 | 0.1273 | 0.0409 | | 228.2815 | 80.0 | 4000 | 230.0584 | 0.845 | 0.2539 | 1.0795 | 0.845 | 0.8363 | 0.1235 | 0.0432 | | 228.2815 | 81.0 | 4050 | 229.9967 | 0.85 | 0.2427 | 1.1156 | 0.85 | 0.8382 | 0.1184 | 0.0394 | | 228.2815 | 82.0 | 4100 | 230.0755 | 0.84 | 0.2563 | 1.0833 | 0.8400 | 0.8302 | 0.1295 | 0.0406 | | 228.2815 | 83.0 | 4150 | 230.0798 | 0.845 | 0.2477 | 1.1713 | 0.845 | 0.8385 | 0.1259 | 0.0427 | | 228.2815 | 84.0 | 4200 | 230.0299 | 0.84 | 0.2477 | 1.0907 | 0.8400 | 0.8260 | 0.1213 | 0.0383 | | 228.2815 | 85.0 | 4250 | 230.0568 | 0.845 | 0.2483 | 1.0763 | 0.845 | 0.8350 | 0.1238 | 0.0409 | | 228.2815 | 86.0 | 4300 | 230.0743 | 0.85 | 0.2464 | 1.0549 | 0.85 | 0.8418 | 0.1271 | 0.0398 | | 228.2815 | 87.0 | 4350 | 230.0061 | 0.845 | 0.2505 | 1.1585 | 0.845 | 0.8350 | 0.1312 | 0.0375 | | 228.2815 | 88.0 | 4400 | 229.9674 | 0.845 | 0.2478 | 1.0763 | 0.845 | 0.8346 | 0.1394 | 0.0410 | | 228.2815 | 89.0 | 4450 | 229.9697 | 0.85 | 0.2451 | 1.0833 | 0.85 | 0.8406 | 0.1324 | 0.0364 | | 228.1298 | 90.0 | 4500 | 230.0305 | 0.845 | 0.2496 | 1.1008 | 0.845 | 0.8350 | 0.1308 | 0.0395 | | 228.1298 | 91.0 | 4550 | 229.9740 | 0.845 | 0.2495 | 1.0605 | 0.845 | 0.8350 | 0.1309 | 0.0413 | | 228.1298 | 92.0 | 4600 | 229.9962 | 0.85 | 0.2497 | 1.1193 | 0.85 | 0.8408 | 0.1294 | 0.0399 | | 228.1298 | 93.0 | 4650 | 229.9740 | 0.85 | 0.2491 | 1.0496 | 0.85 | 0.8383 | 0.1270 | 0.0390 | | 228.1298 | 94.0 | 4700 | 229.9698 | 0.84 | 0.2516 | 1.0644 | 0.8400 | 0.8295 | 0.1430 | 0.0398 | | 228.1298 | 95.0 | 4750 | 229.9247 | 0.845 | 0.2516 | 1.1705 | 0.845 | 0.8350 | 0.1227 | 0.0371 | | 228.1298 | 96.0 | 4800 | 230.0451 | 0.84 | 0.2507 | 1.0970 | 0.8400 | 0.8285 | 0.1368 | 0.0391 | | 228.1298 | 97.0 | 4850 | 229.9402 | 0.85 | 0.2495 | 1.1427 | 0.85 | 0.8430 | 0.1379 | 0.0413 | | 228.1298 | 98.0 | 4900 | 230.0130 | 0.84 | 0.2532 | 1.0964 | 0.8400 | 0.8285 | 0.1412 | 0.0389 | | 228.1298 | 99.0 | 4950 | 230.0200 | 0.845 | 0.2482 | 1.0916 | 0.845 | 0.8317 | 0.1190 | 0.0392 | | 228.0583 | 100.0 | 5000 | 230.0462 | 0.845 | 0.2451 | 1.1250 | 0.845 | 0.8350 | 0.1115 | 0.0383 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/60-tiny_tobacco3482_og_simkd

# 60-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 230.9055 - Accuracy: 0.855 - Brier Loss: 0.2199 - Nll: 1.0336 - F1 Micro: 0.855 - F1 Macro: 0.8458 - Ece: 0.1335 - Aurc: 0.0336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 238.9631 | 0.285 | 0.8828 | 6.5847 | 0.285 | 0.1874 | 0.2964 | 0.5019 | | No log | 2.0 | 100 | 237.5668 | 0.385 | 0.7452 | 3.0464 | 0.3850 | 0.2403 | 0.2776 | 0.4072 | | No log | 3.0 | 150 | 236.0767 | 0.615 | 0.5834 | 2.4900 | 0.615 | 0.4713 | 0.2629 | 0.2189 | | No log | 4.0 | 200 | 235.7085 | 0.635 | 0.5104 | 2.9259 | 0.635 | 0.4933 | 0.2605 | 0.1700 | | No log | 5.0 | 250 | 234.8680 | 0.66 | 0.4494 | 2.2358 | 0.66 | 0.4974 | 0.2021 | 0.1292 | | No log | 6.0 | 300 | 235.0645 | 0.68 | 0.4615 | 2.3164 | 0.68 | 0.5568 | 0.2601 | 0.1346 | | No log | 7.0 | 350 | 234.3820 | 0.735 | 0.3741 | 1.8624 | 0.735 | 0.6245 | 0.2265 | 0.0894 | | No log | 8.0 | 400 | 233.9906 | 0.72 | 0.3954 | 1.7623 | 0.72 | 0.6479 | 0.1779 | 0.0980 | | No log | 9.0 | 450 | 234.0194 | 0.755 | 0.3410 | 1.6963 | 0.755 | 0.6965 | 0.1730 | 0.0718 | | 234.3621 | 10.0 | 500 | 234.2365 | 0.705 | 0.3859 | 2.4174 | 0.705 | 0.6957 | 0.1759 | 0.0944 | | 234.3621 | 11.0 | 550 | 233.5580 | 0.76 | 0.3331 | 1.5730 | 0.76 | 0.7135 | 0.1817 | 0.0709 | | 234.3621 | 12.0 | 600 | 233.3485 | 0.815 | 0.2725 | 1.6179 | 0.815 | 0.7769 | 0.1835 | 0.0497 | | 234.3621 | 13.0 | 650 | 233.6395 | 0.805 | 0.3038 | 1.7563 | 0.805 | 0.7813 | 0.1976 | 0.0698 | | 234.3621 | 14.0 | 700 | 233.1443 | 0.805 | 0.2928 | 1.4722 | 0.805 | 0.7779 | 0.1513 | 0.0546 | | 234.3621 | 15.0 | 750 | 233.1237 | 0.83 | 0.2569 | 1.7889 | 0.83 | 0.8231 | 0.1361 | 0.0460 | | 234.3621 | 16.0 | 800 | 232.9007 | 0.825 | 0.2675 | 1.7492 | 0.825 | 0.8125 | 0.1742 | 0.0558 | | 234.3621 | 17.0 | 850 | 233.0284 | 0.79 | 0.2861 | 1.4243 | 0.79 | 0.7698 | 0.1418 | 0.0520 | | 234.3621 | 18.0 | 900 | 232.9831 | 0.79 | 0.3009 | 1.4085 | 0.79 | 0.7440 | 0.1962 | 0.0643 | | 234.3621 | 19.0 | 950 | 232.9837 | 0.825 | 0.2618 | 1.4839 | 0.825 | 0.7949 | 0.1444 | 0.0457 | | 231.5126 | 20.0 | 1000 | 232.9143 | 0.825 | 0.2599 | 1.5299 | 0.825 | 0.8086 | 0.1570 | 0.0463 | | 231.5126 | 21.0 | 1050 | 232.5251 | 0.835 | 0.2495 | 1.2311 | 0.835 | 0.8279 | 0.1511 | 0.0472 | | 231.5126 | 22.0 | 1100 | 232.6748 | 0.855 | 0.2165 | 1.2547 | 0.855 | 0.8448 | 0.1450 | 0.0299 | | 231.5126 | 23.0 | 1150 | 232.6610 | 0.83 | 0.2450 | 1.2944 | 0.83 | 0.8113 | 0.1545 | 0.0403 | | 231.5126 | 24.0 | 1200 | 232.7660 | 0.83 | 0.2480 | 1.4783 | 0.83 | 0.8101 | 0.1645 | 0.0397 | | 231.5126 | 25.0 | 1250 | 232.5843 | 0.855 | 0.2336 | 1.1227 | 0.855 | 0.8303 | 0.1672 | 0.0397 | | 231.5126 | 26.0 | 1300 | 232.3482 | 0.84 | 0.2321 | 1.1720 | 0.8400 | 0.8346 | 0.1527 | 0.0380 | | 231.5126 | 27.0 | 1350 | 232.3758 | 0.84 | 0.2353 | 1.1684 | 0.8400 | 0.8327 | 0.1520 | 0.0372 | | 231.5126 | 28.0 | 1400 | 232.3022 | 0.82 | 0.2460 | 1.0888 | 0.82 | 0.7920 | 0.1746 | 0.0392 | | 231.5126 | 29.0 | 1450 | 232.1077 | 0.845 | 0.2355 | 1.3720 | 0.845 | 0.8306 | 0.1314 | 0.0372 | | 230.5624 | 30.0 | 1500 | 232.3631 | 0.825 | 0.2443 | 1.3202 | 0.825 | 0.7971 | 0.1404 | 0.0373 | | 230.5624 | 31.0 | 1550 | 232.1099 | 0.84 | 0.2509 | 1.2935 | 0.8400 | 0.8199 | 0.1552 | 0.0453 | | 230.5624 | 32.0 | 1600 | 232.1548 | 0.855 | 0.2275 | 1.0948 | 0.855 | 0.8360 | 0.1428 | 0.0347 | | 230.5624 | 33.0 | 1650 | 232.0168 | 0.84 | 0.2284 | 1.1303 | 0.8400 | 0.8260 | 0.1460 | 0.0374 | | 230.5624 | 34.0 | 1700 | 232.1122 | 0.875 | 0.2154 | 1.3044 | 0.875 | 0.8612 | 0.1410 | 0.0307 | | 230.5624 | 35.0 | 1750 | 232.0452 | 0.84 | 0.2382 | 1.1732 | 0.8400 | 0.8199 | 0.1218 | 0.0376 | | 230.5624 | 36.0 | 1800 | 231.8549 | 0.835 | 0.2390 | 1.1322 | 0.835 | 0.8227 | 0.1478 | 0.0375 | | 230.5624 | 37.0 | 1850 | 231.8938 | 0.845 | 0.2558 | 1.0115 | 0.845 | 0.8261 | 0.1730 | 0.0447 | | 230.5624 | 38.0 | 1900 | 231.8979 | 0.85 | 0.2292 | 1.1142 | 0.85 | 0.8361 | 0.1359 | 0.0348 | | 230.5624 | 39.0 | 1950 | 231.7455 | 0.84 | 0.2217 | 1.0746 | 0.8400 | 0.8185 | 0.1294 | 0.0309 | | 229.9089 | 40.0 | 2000 | 231.8694 | 0.85 | 0.2200 | 1.0529 | 0.85 | 0.8334 | 0.1435 | 0.0343 | | 229.9089 | 41.0 | 2050 | 231.8044 | 0.84 | 0.2317 | 0.9332 | 0.8400 | 0.8204 | 0.1432 | 0.0337 | | 229.9089 | 42.0 | 2100 | 231.6414 | 0.855 | 0.2165 | 1.1356 | 0.855 | 0.8393 | 0.1357 | 0.0321 | | 229.9089 | 43.0 | 2150 | 231.5806 | 0.835 | 0.2378 | 1.0314 | 0.835 | 0.8124 | 0.1443 | 0.0382 | | 229.9089 | 44.0 | 2200 | 231.6199 | 0.855 | 0.2287 | 1.0907 | 0.855 | 0.8463 | 0.1196 | 0.0362 | | 229.9089 | 45.0 | 2250 | 231.5991 | 0.85 | 0.2208 | 1.0967 | 0.85 | 0.8350 | 0.1321 | 0.0339 | | 229.9089 | 46.0 | 2300 | 231.5103 | 0.85 | 0.2249 | 1.0330 | 0.85 | 0.8270 | 0.1239 | 0.0332 | | 229.9089 | 47.0 | 2350 | 231.4252 | 0.87 | 0.2126 | 1.1054 | 0.87 | 0.8618 | 0.1230 | 0.0312 | | 229.9089 | 48.0 | 2400 | 231.4696 | 0.86 | 0.2136 | 1.0952 | 0.8600 | 0.8503 | 0.1304 | 0.0302 | | 229.9089 | 49.0 | 2450 | 231.5416 | 0.84 | 0.2329 | 1.0155 | 0.8400 | 0.8256 | 0.1381 | 0.0356 | | 229.4364 | 50.0 | 2500 | 231.4932 | 0.84 | 0.2215 | 1.1382 | 0.8400 | 0.8177 | 0.1557 | 0.0319 | | 229.4364 | 51.0 | 2550 | 231.4270 | 0.84 | 0.2312 | 1.0191 | 0.8400 | 0.8253 | 0.1376 | 0.0371 | | 229.4364 | 52.0 | 2600 | 231.3520 | 0.85 | 0.2233 | 1.2815 | 0.85 | 0.8289 | 0.1444 | 0.0334 | | 229.4364 | 53.0 | 2650 | 231.3922 | 0.86 | 0.2223 | 1.0950 | 0.8600 | 0.8423 | 0.1309 | 0.0334 | | 229.4364 | 54.0 | 2700 | 231.3504 | 0.855 | 0.2171 | 1.0165 | 0.855 | 0.8422 | 0.1304 | 0.0313 | | 229.4364 | 55.0 | 2750 | 231.2676 | 0.87 | 0.2129 | 1.0704 | 0.87 | 0.8598 | 0.1334 | 0.0320 | | 229.4364 | 56.0 | 2800 | 231.2823 | 0.84 | 0.2390 | 1.0982 | 0.8400 | 0.8226 | 0.1187 | 0.0372 | | 229.4364 | 57.0 | 2850 | 231.2740 | 0.85 | 0.2251 | 0.9521 | 0.85 | 0.8271 | 0.1388 | 0.0320 | | 229.4364 | 58.0 | 2900 | 231.2784 | 0.85 | 0.2284 | 1.0194 | 0.85 | 0.8320 | 0.1306 | 0.0360 | | 229.4364 | 59.0 | 2950 | 231.2078 | 0.84 | 0.2265 | 1.0036 | 0.8400 | 0.8253 | 0.1359 | 0.0366 | | 229.043 | 60.0 | 3000 | 231.2086 | 0.85 | 0.2380 | 1.0247 | 0.85 | 0.8391 | 0.1395 | 0.0374 | | 229.043 | 61.0 | 3050 | 231.2673 | 0.845 | 0.2410 | 1.0205 | 0.845 | 0.8272 | 0.1466 | 0.0389 | | 229.043 | 62.0 | 3100 | 231.1900 | 0.855 | 0.2219 | 1.0835 | 0.855 | 0.8449 | 0.1351 | 0.0346 | | 229.043 | 63.0 | 3150 | 231.0561 | 0.845 | 0.2417 | 0.9740 | 0.845 | 0.8332 | 0.1503 | 0.0405 | | 229.043 | 64.0 | 3200 | 231.1282 | 0.845 | 0.2387 | 1.1105 | 0.845 | 0.8270 | 0.1198 | 0.0379 | | 229.043 | 65.0 | 3250 | 231.0782 | 0.85 | 0.2334 | 0.9838 | 0.85 | 0.8403 | 0.1282 | 0.0356 | | 229.043 | 66.0 | 3300 | 231.0704 | 0.84 | 0.2442 | 1.0380 | 0.8400 | 0.8275 | 0.1543 | 0.0410 | | 229.043 | 67.0 | 3350 | 231.0450 | 0.85 | 0.2246 | 1.0023 | 0.85 | 0.8394 | 0.1212 | 0.0353 | | 229.043 | 68.0 | 3400 | 231.1017 | 0.85 | 0.2257 | 1.0437 | 0.85 | 0.8334 | 0.1232 | 0.0350 | | 229.043 | 69.0 | 3450 | 231.0068 | 0.85 | 0.2321 | 1.0075 | 0.85 | 0.8445 | 0.1271 | 0.0361 | | 228.7748 | 70.0 | 3500 | 231.0666 | 0.85 | 0.2274 | 1.0133 | 0.85 | 0.8382 | 0.1334 | 0.0361 | | 228.7748 | 71.0 | 3550 | 230.9450 | 0.85 | 0.2417 | 1.0738 | 0.85 | 0.8356 | 0.1294 | 0.0380 | | 228.7748 | 72.0 | 3600 | 230.7952 | 0.85 | 0.2379 | 0.9779 | 0.85 | 0.8391 | 0.1309 | 0.0393 | | 228.7748 | 73.0 | 3650 | 231.0920 | 0.86 | 0.2188 | 1.0154 | 0.8600 | 0.8538 | 0.1230 | 0.0335 | | 228.7748 | 74.0 | 3700 | 230.9152 | 0.855 | 0.2408 | 1.1637 | 0.855 | 0.8486 | 0.1490 | 0.0400 | | 228.7748 | 75.0 | 3750 | 230.9537 | 0.85 | 0.2195 | 1.0135 | 0.85 | 0.8301 | 0.1131 | 0.0321 | | 228.7748 | 76.0 | 3800 | 230.9977 | 0.855 | 0.2208 | 1.0136 | 0.855 | 0.8484 | 0.1296 | 0.0334 | | 228.7748 | 77.0 | 3850 | 230.9619 | 0.855 | 0.2348 | 1.0158 | 0.855 | 0.8526 | 0.1346 | 0.0371 | | 228.7748 | 78.0 | 3900 | 230.9416 | 0.84 | 0.2315 | 1.0372 | 0.8400 | 0.8219 | 0.1290 | 0.0353 | | 228.7748 | 79.0 | 3950 | 231.0093 | 0.85 | 0.2196 | 1.0981 | 0.85 | 0.8318 | 0.1380 | 0.0335 | | 228.5779 | 80.0 | 4000 | 230.9455 | 0.845 | 0.2290 | 1.0193 | 0.845 | 0.8332 | 0.1459 | 0.0350 | | 228.5779 | 81.0 | 4050 | 230.8672 | 0.845 | 0.2184 | 1.0164 | 0.845 | 0.8309 | 0.1560 | 0.0322 | | 228.5779 | 82.0 | 4100 | 230.9410 | 0.855 | 0.2282 | 1.0116 | 0.855 | 0.8486 | 0.1309 | 0.0349 | | 228.5779 | 83.0 | 4150 | 230.9393 | 0.855 | 0.2258 | 1.0168 | 0.855 | 0.8498 | 0.1227 | 0.0355 | | 228.5779 | 84.0 | 4200 | 230.8770 | 0.845 | 0.2204 | 1.0105 | 0.845 | 0.8303 | 0.1259 | 0.0315 | | 228.5779 | 85.0 | 4250 | 230.9236 | 0.845 | 0.2271 | 1.0079 | 0.845 | 0.8332 | 0.1274 | 0.0353 | | 228.5779 | 86.0 | 4300 | 230.9332 | 0.845 | 0.2237 | 1.0410 | 0.845 | 0.8288 | 0.1480 | 0.0340 | | 228.5779 | 87.0 | 4350 | 230.8607 | 0.845 | 0.2273 | 1.0343 | 0.845 | 0.8313 | 0.1070 | 0.0338 | | 228.5779 | 88.0 | 4400 | 230.7832 | 0.85 | 0.2434 | 1.1399 | 0.85 | 0.8432 | 0.1483 | 0.0407 | | 228.5779 | 89.0 | 4450 | 230.8136 | 0.85 | 0.2263 | 1.0002 | 0.85 | 0.8367 | 0.1283 | 0.0343 | | 228.4285 | 90.0 | 4500 | 230.9154 | 0.86 | 0.2243 | 1.0189 | 0.8600 | 0.8552 | 0.1319 | 0.0332 | | 228.4285 | 91.0 | 4550 | 230.8125 | 0.86 | 0.2320 | 0.9955 | 0.8600 | 0.8552 | 0.1437 | 0.0361 | | 228.4285 | 92.0 | 4600 | 230.8634 | 0.855 | 0.2238 | 1.0341 | 0.855 | 0.8477 | 0.1252 | 0.0329 | | 228.4285 | 93.0 | 4650 | 230.8527 | 0.84 | 0.2326 | 0.9819 | 0.8400 | 0.8242 | 0.1248 | 0.0362 | | 228.4285 | 94.0 | 4700 | 230.8176 | 0.845 | 0.2347 | 1.0080 | 0.845 | 0.8332 | 0.1356 | 0.0369 | | 228.4285 | 95.0 | 4750 | 230.7853 | 0.85 | 0.2286 | 1.0163 | 0.85 | 0.8445 | 0.1176 | 0.0356 | | 228.4285 | 96.0 | 4800 | 230.9345 | 0.855 | 0.2218 | 1.0151 | 0.855 | 0.8458 | 0.1252 | 0.0328 | | 228.4285 | 97.0 | 4850 | 230.8020 | 0.855 | 0.2300 | 1.0166 | 0.855 | 0.8462 | 0.1424 | 0.0349 | | 228.4285 | 98.0 | 4900 | 230.8873 | 0.855 | 0.2240 | 1.0244 | 0.855 | 0.8477 | 0.1309 | 0.0343 | | 228.4285 | 99.0 | 4950 | 230.8796 | 0.855 | 0.2276 | 1.0084 | 0.855 | 0.8498 | 0.1254 | 0.0344 | | 228.3542 | 100.0 | 5000 | 230.9055 | 0.855 | 0.2199 | 1.0336 | 0.855 | 0.8458 | 0.1335 | 0.0336 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/39-tiny_tobacco3482_og_simkd

# 39-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 239.0946 - Accuracy: 0.835 - Brier Loss: 0.3084 - Nll: 1.2679 - F1 Micro: 0.835 - F1 Macro: 0.8047 - Ece: 0.2467 - Aurc: 0.0448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 246.9268 | 0.22 | 0.8877 | 7.2845 | 0.22 | 0.1442 | 0.2508 | 0.5734 | | No log | 2.0 | 100 | 245.5493 | 0.355 | 0.7851 | 4.2469 | 0.3550 | 0.2408 | 0.2761 | 0.4302 | | No log | 3.0 | 150 | 244.0258 | 0.58 | 0.6569 | 2.7197 | 0.58 | 0.4698 | 0.3335 | 0.2529 | | No log | 4.0 | 200 | 243.8010 | 0.615 | 0.5800 | 3.1495 | 0.615 | 0.4701 | 0.2865 | 0.1927 | | No log | 5.0 | 250 | 242.9670 | 0.64 | 0.5193 | 2.2510 | 0.64 | 0.4843 | 0.2774 | 0.1631 | | No log | 6.0 | 300 | 243.1274 | 0.62 | 0.5595 | 2.8430 | 0.62 | 0.4881 | 0.2980 | 0.1558 | | No log | 7.0 | 350 | 242.1450 | 0.675 | 0.4620 | 2.2479 | 0.675 | 0.5389 | 0.2322 | 0.1266 | | No log | 8.0 | 400 | 242.5806 | 0.63 | 0.5222 | 2.6597 | 0.63 | 0.5539 | 0.2649 | 0.1584 | | No log | 9.0 | 450 | 241.9880 | 0.755 | 0.4313 | 1.8660 | 0.755 | 0.6923 | 0.2780 | 0.1028 | | 241.4671 | 10.0 | 500 | 241.9459 | 0.73 | 0.4274 | 1.9762 | 0.7300 | 0.6550 | 0.2539 | 0.0843 | | 241.4671 | 11.0 | 550 | 241.4543 | 0.74 | 0.4410 | 1.7736 | 0.74 | 0.6867 | 0.2589 | 0.1197 | | 241.4671 | 12.0 | 600 | 241.5311 | 0.79 | 0.3750 | 1.6579 | 0.79 | 0.7193 | 0.2754 | 0.0582 | | 241.4671 | 13.0 | 650 | 241.6211 | 0.77 | 0.4231 | 1.4441 | 0.7700 | 0.7350 | 0.3134 | 0.0761 | | 241.4671 | 14.0 | 700 | 241.2984 | 0.795 | 0.3898 | 1.5615 | 0.795 | 0.7650 | 0.2879 | 0.0721 | | 241.4671 | 15.0 | 750 | 241.2752 | 0.81 | 0.3641 | 1.5167 | 0.81 | 0.7692 | 0.2956 | 0.0515 | | 241.4671 | 16.0 | 800 | 240.9899 | 0.78 | 0.3821 | 1.7296 | 0.78 | 0.7434 | 0.2935 | 0.0688 | | 241.4671 | 17.0 | 850 | 241.3208 | 0.81 | 0.3850 | 1.6487 | 0.81 | 0.7628 | 0.2946 | 0.0657 | | 241.4671 | 18.0 | 900 | 241.0431 | 0.82 | 0.3678 | 1.5667 | 0.82 | 0.7701 | 0.3109 | 0.0504 | | 241.4671 | 19.0 | 950 | 240.9436 | 0.815 | 0.3615 | 1.1826 | 0.815 | 0.7624 | 0.3019 | 0.0557 | | 238.6853 | 20.0 | 1000 | 240.7993 | 0.83 | 0.3475 | 1.2078 | 0.83 | 0.7887 | 0.3062 | 0.0541 | | 238.6853 | 21.0 | 1050 | 240.6124 | 0.825 | 0.3577 | 1.1927 | 0.825 | 0.7956 | 0.2883 | 0.0560 | | 238.6853 | 22.0 | 1100 | 240.7105 | 0.815 | 0.3535 | 1.4564 | 0.815 | 0.7550 | 0.2814 | 0.0553 | | 238.6853 | 23.0 | 1150 | 240.6886 | 0.815 | 0.3584 | 1.1556 | 0.815 | 0.7815 | 0.2993 | 0.0564 | | 238.6853 | 24.0 | 1200 | 240.5978 | 0.84 | 0.3451 | 1.1351 | 0.8400 | 0.8002 | 0.2937 | 0.0485 | | 238.6853 | 25.0 | 1250 | 240.3825 | 0.815 | 0.3453 | 1.2408 | 0.815 | 0.7918 | 0.2853 | 0.0525 | | 238.6853 | 26.0 | 1300 | 240.2633 | 0.83 | 0.3431 | 1.3299 | 0.83 | 0.7883 | 0.2715 | 0.0562 | | 238.6853 | 27.0 | 1350 | 240.5535 | 0.82 | 0.3475 | 1.4406 | 0.82 | 0.7874 | 0.2897 | 0.0536 | | 238.6853 | 28.0 | 1400 | 240.3554 | 0.835 | 0.3447 | 1.2483 | 0.835 | 0.7947 | 0.3078 | 0.0464 | | 238.6853 | 29.0 | 1450 | 240.2271 | 0.82 | 0.3274 | 1.5224 | 0.82 | 0.7862 | 0.2691 | 0.0467 | | 237.7411 | 30.0 | 1500 | 240.2261 | 0.825 | 0.3388 | 1.2921 | 0.825 | 0.7939 | 0.2913 | 0.0485 | | 237.7411 | 31.0 | 1550 | 240.4772 | 0.83 | 0.3474 | 1.3063 | 0.83 | 0.7823 | 0.2995 | 0.0516 | | 237.7411 | 32.0 | 1600 | 240.2594 | 0.85 | 0.3509 | 1.3993 | 0.85 | 0.8184 | 0.3142 | 0.0496 | | 237.7411 | 33.0 | 1650 | 239.9501 | 0.805 | 0.3413 | 1.2018 | 0.805 | 0.7630 | 0.2619 | 0.0592 | | 237.7411 | 34.0 | 1700 | 240.2149 | 0.86 | 0.3409 | 1.2633 | 0.8600 | 0.8490 | 0.3229 | 0.0467 | | 237.7411 | 35.0 | 1750 | 240.0215 | 0.82 | 0.3264 | 1.1435 | 0.82 | 0.7705 | 0.2538 | 0.0499 | | 237.7411 | 36.0 | 1800 | 239.9490 | 0.85 | 0.3204 | 1.4364 | 0.85 | 0.8147 | 0.2752 | 0.0432 | | 237.7411 | 37.0 | 1850 | 239.9360 | 0.825 | 0.3262 | 1.1487 | 0.825 | 0.7921 | 0.2706 | 0.0482 | | 237.7411 | 38.0 | 1900 | 240.0647 | 0.855 | 0.3322 | 1.1655 | 0.855 | 0.8339 | 0.3053 | 0.0460 | | 237.7411 | 39.0 | 1950 | 239.7761 | 0.825 | 0.3237 | 1.2252 | 0.825 | 0.7862 | 0.2732 | 0.0499 | | 237.1023 | 40.0 | 2000 | 239.9426 | 0.83 | 0.3220 | 1.2096 | 0.83 | 0.7906 | 0.2748 | 0.0457 | | 237.1023 | 41.0 | 2050 | 239.8765 | 0.835 | 0.3241 | 1.3547 | 0.835 | 0.7929 | 0.2661 | 0.0462 | | 237.1023 | 42.0 | 2100 | 239.8423 | 0.835 | 0.3329 | 1.2423 | 0.835 | 0.8000 | 0.2955 | 0.0527 | | 237.1023 | 43.0 | 2150 | 239.6674 | 0.83 | 0.3215 | 1.1217 | 0.83 | 0.8069 | 0.2597 | 0.0486 | | 237.1023 | 44.0 | 2200 | 239.7237 | 0.81 | 0.3206 | 1.1322 | 0.81 | 0.7724 | 0.2500 | 0.0473 | | 237.1023 | 45.0 | 2250 | 239.7981 | 0.82 | 0.3263 | 1.4167 | 0.82 | 0.7754 | 0.2786 | 0.0485 | | 237.1023 | 46.0 | 2300 | 239.4825 | 0.83 | 0.3149 | 1.2610 | 0.83 | 0.7879 | 0.2436 | 0.0487 | | 237.1023 | 47.0 | 2350 | 239.6016 | 0.83 | 0.3195 | 1.2602 | 0.83 | 0.7903 | 0.2631 | 0.0529 | | 237.1023 | 48.0 | 2400 | 239.6693 | 0.84 | 0.3248 | 1.2935 | 0.8400 | 0.7992 | 0.2581 | 0.0505 | | 237.1023 | 49.0 | 2450 | 239.6932 | 0.845 | 0.3235 | 1.1698 | 0.845 | 0.8024 | 0.2600 | 0.0459 | | 236.6301 | 50.0 | 2500 | 239.5297 | 0.845 | 0.3151 | 1.4464 | 0.845 | 0.8057 | 0.2775 | 0.0467 | | 236.6301 | 51.0 | 2550 | 239.6262 | 0.83 | 0.3236 | 1.2198 | 0.83 | 0.7879 | 0.2680 | 0.0466 | | 236.6301 | 52.0 | 2600 | 239.4326 | 0.855 | 0.3111 | 1.2320 | 0.855 | 0.8338 | 0.2785 | 0.0452 | | 236.6301 | 53.0 | 2650 | 239.5918 | 0.825 | 0.3171 | 1.2295 | 0.825 | 0.7966 | 0.2517 | 0.0440 | | 236.6301 | 54.0 | 2700 | 239.5557 | 0.845 | 0.3215 | 1.1543 | 0.845 | 0.8027 | 0.2723 | 0.0453 | | 236.6301 | 55.0 | 2750 | 239.4767 | 0.83 | 0.3113 | 1.0283 | 0.83 | 0.8107 | 0.2457 | 0.0446 | | 236.6301 | 56.0 | 2800 | 239.3879 | 0.85 | 0.3112 | 1.2353 | 0.85 | 0.8146 | 0.2547 | 0.0449 | | 236.6301 | 57.0 | 2850 | 239.3733 | 0.85 | 0.3104 | 1.3730 | 0.85 | 0.8236 | 0.2593 | 0.0451 | | 236.6301 | 58.0 | 2900 | 239.4988 | 0.835 | 0.3174 | 1.3295 | 0.835 | 0.7984 | 0.2572 | 0.0468 | | 236.6301 | 59.0 | 2950 | 239.3514 | 0.815 | 0.3103 | 1.2168 | 0.815 | 0.7746 | 0.2382 | 0.0453 | | 236.2404 | 60.0 | 3000 | 239.3031 | 0.835 | 0.3083 | 1.2398 | 0.835 | 0.7944 | 0.2577 | 0.0479 | | 236.2404 | 61.0 | 3050 | 239.3242 | 0.855 | 0.3095 | 1.2921 | 0.855 | 0.8307 | 0.2739 | 0.0438 | | 236.2404 | 62.0 | 3100 | 239.3217 | 0.815 | 0.3135 | 1.2385 | 0.815 | 0.7799 | 0.2559 | 0.0481 | | 236.2404 | 63.0 | 3150 | 239.2695 | 0.835 | 0.3102 | 1.0724 | 0.835 | 0.8068 | 0.2276 | 0.0471 | | 236.2404 | 64.0 | 3200 | 239.3596 | 0.83 | 0.3124 | 1.1534 | 0.83 | 0.7911 | 0.2440 | 0.0442 | | 236.2404 | 65.0 | 3250 | 239.2498 | 0.825 | 0.3097 | 1.2191 | 0.825 | 0.7804 | 0.2450 | 0.0459 | | 236.2404 | 66.0 | 3300 | 239.1448 | 0.82 | 0.3020 | 1.0196 | 0.82 | 0.7825 | 0.2559 | 0.0452 | | 236.2404 | 67.0 | 3350 | 239.1730 | 0.825 | 0.3017 | 1.1825 | 0.825 | 0.8065 | 0.2494 | 0.0444 | | 236.2404 | 68.0 | 3400 | 239.3466 | 0.835 | 0.3159 | 1.2392 | 0.835 | 0.7973 | 0.2617 | 0.0448 | | 236.2404 | 69.0 | 3450 | 239.1867 | 0.825 | 0.3058 | 1.1611 | 0.825 | 0.7940 | 0.2596 | 0.0450 | | 235.9626 | 70.0 | 3500 | 239.2792 | 0.84 | 0.3106 | 1.1812 | 0.8400 | 0.8134 | 0.2590 | 0.0444 | | 235.9626 | 71.0 | 3550 | 239.0603 | 0.82 | 0.3055 | 1.1710 | 0.82 | 0.7895 | 0.2251 | 0.0466 | | 235.9626 | 72.0 | 3600 | 238.9947 | 0.815 | 0.3069 | 1.1766 | 0.815 | 0.7905 | 0.2315 | 0.0485 | | 235.9626 | 73.0 | 3650 | 239.2322 | 0.83 | 0.3108 | 1.1724 | 0.83 | 0.8015 | 0.2595 | 0.0450 | | 235.9626 | 74.0 | 3700 | 239.0134 | 0.825 | 0.3087 | 1.2661 | 0.825 | 0.7854 | 0.2352 | 0.0467 | | 235.9626 | 75.0 | 3750 | 239.1055 | 0.825 | 0.3090 | 1.1748 | 0.825 | 0.7946 | 0.2483 | 0.0458 | | 235.9626 | 76.0 | 3800 | 239.0925 | 0.825 | 0.3133 | 1.1843 | 0.825 | 0.7918 | 0.2466 | 0.0490 | | 235.9626 | 77.0 | 3850 | 239.1586 | 0.835 | 0.3115 | 1.1877 | 0.835 | 0.8140 | 0.2498 | 0.0455 | | 235.9626 | 78.0 | 3900 | 239.1394 | 0.83 | 0.3103 | 1.2698 | 0.83 | 0.7897 | 0.2424 | 0.0467 | | 235.9626 | 79.0 | 3950 | 239.2314 | 0.83 | 0.3121 | 1.2519 | 0.83 | 0.7938 | 0.2378 | 0.0453 | | 235.7667 | 80.0 | 4000 | 239.1433 | 0.83 | 0.3076 | 1.1725 | 0.83 | 0.7924 | 0.2412 | 0.0459 | | 235.7667 | 81.0 | 4050 | 239.0533 | 0.83 | 0.3026 | 1.2551 | 0.83 | 0.7994 | 0.2661 | 0.0448 | | 235.7667 | 82.0 | 4100 | 239.0847 | 0.825 | 0.3123 | 1.1798 | 0.825 | 0.7964 | 0.2611 | 0.0483 | | 235.7667 | 83.0 | 4150 | 239.1199 | 0.835 | 0.3089 | 1.2642 | 0.835 | 0.8047 | 0.2551 | 0.0471 | | 235.7667 | 84.0 | 4200 | 239.0148 | 0.835 | 0.3055 | 1.1264 | 0.835 | 0.7975 | 0.2341 | 0.0439 | | 235.7667 | 85.0 | 4250 | 239.0657 | 0.825 | 0.3107 | 1.1645 | 0.825 | 0.7924 | 0.2363 | 0.0452 | | 235.7667 | 86.0 | 4300 | 239.1023 | 0.83 | 0.3134 | 1.2785 | 0.83 | 0.7949 | 0.2353 | 0.0462 | | 235.7667 | 87.0 | 4350 | 239.0670 | 0.83 | 0.3028 | 1.2849 | 0.83 | 0.7927 | 0.2295 | 0.0428 | | 235.7667 | 88.0 | 4400 | 239.0111 | 0.82 | 0.3097 | 1.1759 | 0.82 | 0.7865 | 0.2447 | 0.0462 | | 235.7667 | 89.0 | 4450 | 238.9677 | 0.845 | 0.2995 | 1.1189 | 0.845 | 0.8231 | 0.2402 | 0.0423 | | 235.6149 | 90.0 | 4500 | 239.1255 | 0.83 | 0.3063 | 1.1282 | 0.83 | 0.8028 | 0.2695 | 0.0431 | | 235.6149 | 91.0 | 4550 | 238.9880 | 0.815 | 0.3102 | 1.1766 | 0.815 | 0.7851 | 0.2388 | 0.0486 | | 235.6149 | 92.0 | 4600 | 239.0212 | 0.84 | 0.3079 | 1.1941 | 0.8400 | 0.8121 | 0.2627 | 0.0442 | | 235.6149 | 93.0 | 4650 | 238.9948 | 0.82 | 0.3077 | 1.1702 | 0.82 | 0.7874 | 0.2533 | 0.0468 | | 235.6149 | 94.0 | 4700 | 239.0357 | 0.82 | 0.3103 | 1.1823 | 0.82 | 0.7878 | 0.2639 | 0.0471 | | 235.6149 | 95.0 | 4750 | 238.9936 | 0.81 | 0.2995 | 1.1251 | 0.81 | 0.7738 | 0.2519 | 0.0447 | | 235.6149 | 96.0 | 4800 | 239.1319 | 0.835 | 0.3138 | 1.1957 | 0.835 | 0.8045 | 0.2806 | 0.0455 | | 235.6149 | 97.0 | 4850 | 239.0160 | 0.815 | 0.3061 | 1.1852 | 0.815 | 0.7756 | 0.2281 | 0.0453 | | 235.6149 | 98.0 | 4900 | 239.1168 | 0.83 | 0.3152 | 1.2637 | 0.83 | 0.7997 | 0.2509 | 0.0474 | | 235.6149 | 99.0 | 4950 | 239.0691 | 0.83 | 0.3085 | 1.1799 | 0.83 | 0.7998 | 0.2524 | 0.0439 | | 235.5384 | 100.0 | 5000 | 239.0946 | 0.835 | 0.3084 | 1.2679 | 0.835 | 0.8047 | 0.2467 | 0.0448 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/18-tiny_tobacco3482_og_simkd

# 18-tiny_tobacco3482_og_simkd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 269.3729 - Accuracy: 0.36 - Brier Loss: 0.8320 - Nll: 4.3971 - F1 Micro: 0.36 - F1 Macro: 0.2725 - Ece: 0.3539 - Aurc: 0.6748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 278.3639 | 0.075 | 0.8950 | 9.1030 | 0.075 | 0.0452 | 0.1624 | 0.8470 | | No log | 2.0 | 100 | 276.7339 | 0.095 | 0.8890 | 8.6785 | 0.095 | 0.0820 | 0.1806 | 0.9095 | | No log | 3.0 | 150 | 275.0460 | 0.16 | 0.8755 | 6.5857 | 0.16 | 0.0866 | 0.2296 | 0.8951 | | No log | 4.0 | 200 | 274.4719 | 0.33 | 0.8662 | 6.6801 | 0.33 | 0.1980 | 0.3166 | 0.5931 | | No log | 5.0 | 250 | 273.5627 | 0.23 | 0.8660 | 6.4504 | 0.23 | 0.1193 | 0.2606 | 0.7477 | | No log | 6.0 | 300 | 273.5152 | 0.22 | 0.8622 | 6.1576 | 0.22 | 0.1323 | 0.2578 | 0.6395 | | No log | 7.0 | 350 | 273.0874 | 0.2 | 0.8584 | 5.8986 | 0.2000 | 0.1249 | 0.2508 | 0.8129 | | No log | 8.0 | 400 | 273.7433 | 0.19 | 0.8764 | 6.0516 | 0.19 | 0.1554 | 0.2550 | 0.8545 | | No log | 9.0 | 450 | 272.4757 | 0.185 | 0.8533 | 5.9072 | 0.185 | 0.1340 | 0.2529 | 0.7360 | | 273.9531 | 10.0 | 500 | 272.7899 | 0.14 | 0.8669 | 6.3132 | 0.14 | 0.1080 | 0.2373 | 0.8882 | | 273.9531 | 11.0 | 550 | 271.7198 | 0.245 | 0.8484 | 4.7561 | 0.245 | 0.1370 | 0.2741 | 0.7203 | | 273.9531 | 12.0 | 600 | 272.7391 | 0.095 | 0.8761 | 6.3636 | 0.095 | 0.0930 | 0.2120 | 0.9142 | | 273.9531 | 13.0 | 650 | 271.8800 | 0.24 | 0.8566 | 4.9415 | 0.24 | 0.1926 | 0.2825 | 0.7850 | | 273.9531 | 14.0 | 700 | 271.4349 | 0.305 | 0.8408 | 4.6971 | 0.305 | 0.1935 | 0.3087 | 0.7274 | | 273.9531 | 15.0 | 750 | 271.7440 | 0.24 | 0.8476 | 3.9346 | 0.24 | 0.1835 | 0.2902 | 0.8098 | | 273.9531 | 16.0 | 800 | 271.1944 | 0.28 | 0.8464 | 4.1470 | 0.28 | 0.2074 | 0.3074 | 0.7919 | | 273.9531 | 17.0 | 850 | 271.2062 | 0.34 | 0.8363 | 4.8972 | 0.34 | 0.2136 | 0.3237 | 0.6403 | | 273.9531 | 18.0 | 900 | 271.6591 | 0.295 | 0.8548 | 4.7423 | 0.295 | 0.1990 | 0.3066 | 0.7590 | | 273.9531 | 19.0 | 950 | 271.5129 | 0.255 | 0.8571 | 4.8864 | 0.255 | 0.1898 | 0.2901 | 0.7929 | | 270.6992 | 20.0 | 1000 | 271.4151 | 0.265 | 0.8543 | 5.2793 | 0.265 | 0.1924 | 0.2928 | 0.7842 | | 270.6992 | 21.0 | 1050 | 271.0936 | 0.35 | 0.8449 | 4.4171 | 0.35 | 0.2347 | 0.3389 | 0.7230 | | 270.6992 | 22.0 | 1100 | 271.2365 | 0.285 | 0.8504 | 4.7910 | 0.285 | 0.2020 | 0.3147 | 0.7840 | | 270.6992 | 23.0 | 1150 | 271.3472 | 0.26 | 0.8496 | 4.1785 | 0.26 | 0.1800 | 0.2922 | 0.7983 | | 270.6992 | 24.0 | 1200 | 271.1550 | 0.335 | 0.8466 | 4.8743 | 0.335 | 0.2518 | 0.3381 | 0.7148 | | 270.6992 | 25.0 | 1250 | 270.7837 | 0.34 | 0.8455 | 4.7405 | 0.34 | 0.2392 | 0.3420 | 0.7592 | | 270.6992 | 26.0 | 1300 | 271.2034 | 0.325 | 0.8485 | 5.0231 | 0.325 | 0.2418 | 0.3302 | 0.7411 | | 270.6992 | 27.0 | 1350 | 270.5752 | 0.385 | 0.8334 | 4.5239 | 0.3850 | 0.2866 | 0.3535 | 0.6197 | | 270.6992 | 28.0 | 1400 | 270.6892 | 0.35 | 0.8407 | 4.5651 | 0.35 | 0.2593 | 0.3428 | 0.6812 | | 270.6992 | 29.0 | 1450 | 270.7246 | 0.36 | 0.8411 | 4.6054 | 0.36 | 0.2699 | 0.3365 | 0.6772 | | 269.5806 | 30.0 | 1500 | 270.6962 | 0.32 | 0.8453 | 4.3997 | 0.32 | 0.2170 | 0.3145 | 0.7129 | | 269.5806 | 31.0 | 1550 | 270.8489 | 0.325 | 0.8478 | 4.8399 | 0.325 | 0.2367 | 0.3180 | 0.6999 | | 269.5806 | 32.0 | 1600 | 270.5093 | 0.36 | 0.8370 | 4.6472 | 0.36 | 0.2765 | 0.3456 | 0.6472 | | 269.5806 | 33.0 | 1650 | 270.5440 | 0.35 | 0.8407 | 4.6948 | 0.35 | 0.2532 | 0.3292 | 0.6436 | | 269.5806 | 34.0 | 1700 | 270.6743 | 0.395 | 0.8387 | 4.1379 | 0.395 | 0.2872 | 0.3730 | 0.6805 | | 269.5806 | 35.0 | 1750 | 270.6282 | 0.33 | 0.8415 | 4.2499 | 0.33 | 0.2445 | 0.3410 | 0.7220 | | 269.5806 | 36.0 | 1800 | 270.5709 | 0.31 | 0.8478 | 4.8275 | 0.31 | 0.2295 | 0.3063 | 0.7223 | | 269.5806 | 37.0 | 1850 | 270.4933 | 0.33 | 0.8449 | 4.7300 | 0.33 | 0.2415 | 0.3157 | 0.6997 | | 269.5806 | 38.0 | 1900 | 270.4860 | 0.355 | 0.8360 | 4.4343 | 0.3550 | 0.2598 | 0.3373 | 0.6545 | | 269.5806 | 39.0 | 1950 | 269.9668 | 0.37 | 0.8306 | 4.4080 | 0.37 | 0.2602 | 0.3349 | 0.6056 | | 268.8099 | 40.0 | 2000 | 270.3401 | 0.385 | 0.8389 | 5.0506 | 0.3850 | 0.2777 | 0.3567 | 0.6686 | | 268.8099 | 41.0 | 2050 | 270.0377 | 0.365 | 0.8320 | 4.6238 | 0.3650 | 0.2466 | 0.3429 | 0.6167 | | 268.8099 | 42.0 | 2100 | 270.2510 | 0.355 | 0.8363 | 4.7053 | 0.3550 | 0.2498 | 0.3352 | 0.6564 | | 268.8099 | 43.0 | 2150 | 270.0252 | 0.38 | 0.8275 | 4.2261 | 0.38 | 0.2690 | 0.3557 | 0.5995 | | 268.8099 | 44.0 | 2200 | 269.9756 | 0.36 | 0.8251 | 4.2336 | 0.36 | 0.2516 | 0.3226 | 0.6005 | | 268.8099 | 45.0 | 2250 | 270.1610 | 0.34 | 0.8383 | 4.5052 | 0.34 | 0.2593 | 0.3164 | 0.6854 | | 268.8099 | 46.0 | 2300 | 270.0320 | 0.33 | 0.8396 | 4.4659 | 0.33 | 0.2458 | 0.3114 | 0.7070 | | 268.8099 | 47.0 | 2350 | 269.9535 | 0.335 | 0.8316 | 4.6707 | 0.335 | 0.2419 | 0.3203 | 0.6503 | | 268.8099 | 48.0 | 2400 | 270.0645 | 0.365 | 0.8320 | 4.4182 | 0.3650 | 0.2517 | 0.3230 | 0.6360 | | 268.8099 | 49.0 | 2450 | 270.0541 | 0.375 | 0.8348 | 4.4803 | 0.375 | 0.2717 | 0.3504 | 0.6444 | | 268.2443 | 50.0 | 2500 | 269.8891 | 0.33 | 0.8374 | 4.7223 | 0.33 | 0.2365 | 0.3223 | 0.6716 | | 268.2443 | 51.0 | 2550 | 269.8773 | 0.39 | 0.8355 | 4.4693 | 0.39 | 0.2637 | 0.3547 | 0.6525 | | 268.2443 | 52.0 | 2600 | 269.6983 | 0.39 | 0.8298 | 4.5670 | 0.39 | 0.2716 | 0.3624 | 0.6230 | | 268.2443 | 53.0 | 2650 | 270.0016 | 0.35 | 0.8327 | 4.3052 | 0.35 | 0.2402 | 0.3367 | 0.6602 | | 268.2443 | 54.0 | 2700 | 269.7764 | 0.35 | 0.8318 | 4.3783 | 0.35 | 0.2432 | 0.3189 | 0.6373 | | 268.2443 | 55.0 | 2750 | 269.7582 | 0.36 | 0.8272 | 4.4652 | 0.36 | 0.2535 | 0.3130 | 0.6197 | | 268.2443 | 56.0 | 2800 | 269.7654 | 0.385 | 0.8253 | 4.4241 | 0.3850 | 0.2751 | 0.3375 | 0.6019 | | 268.2443 | 57.0 | 2850 | 269.7094 | 0.375 | 0.8331 | 4.5019 | 0.375 | 0.2691 | 0.3413 | 0.6391 | | 268.2443 | 58.0 | 2900 | 269.7442 | 0.37 | 0.8323 | 4.5422 | 0.37 | 0.2813 | 0.3311 | 0.6468 | | 268.2443 | 59.0 | 2950 | 269.7930 | 0.385 | 0.8265 | 4.4502 | 0.3850 | 0.2820 | 0.3331 | 0.6032 | | 267.7843 | 60.0 | 3000 | 269.6804 | 0.375 | 0.8270 | 4.3152 | 0.375 | 0.2723 | 0.3221 | 0.6005 | | 267.7843 | 61.0 | 3050 | 269.7132 | 0.36 | 0.8355 | 4.4928 | 0.36 | 0.2625 | 0.3464 | 0.6679 | | 267.7843 | 62.0 | 3100 | 269.6766 | 0.375 | 0.8308 | 4.5392 | 0.375 | 0.2644 | 0.3272 | 0.6307 | | 267.7843 | 63.0 | 3150 | 269.5538 | 0.32 | 0.8345 | 4.4571 | 0.32 | 0.2299 | 0.3160 | 0.6791 | | 267.7843 | 64.0 | 3200 | 269.5080 | 0.375 | 0.8262 | 4.1608 | 0.375 | 0.2581 | 0.3323 | 0.6357 | | 267.7843 | 65.0 | 3250 | 269.6766 | 0.355 | 0.8362 | 4.4220 | 0.3550 | 0.2647 | 0.3250 | 0.6591 | | 267.7843 | 66.0 | 3300 | 269.4889 | 0.38 | 0.8249 | 4.3636 | 0.38 | 0.2786 | 0.3452 | 0.6364 | | 267.7843 | 67.0 | 3350 | 269.4171 | 0.4 | 0.8246 | 4.2888 | 0.4000 | 0.2877 | 0.3479 | 0.6114 | | 267.7843 | 68.0 | 3400 | 269.6962 | 0.37 | 0.8286 | 4.3171 | 0.37 | 0.2614 | 0.3376 | 0.6369 | | 267.7843 | 69.0 | 3450 | 269.3859 | 0.425 | 0.8225 | 4.2805 | 0.425 | 0.3053 | 0.3649 | 0.5751 | | 267.4508 | 70.0 | 3500 | 269.5482 | 0.345 | 0.8377 | 4.7547 | 0.345 | 0.2559 | 0.3341 | 0.6728 | | 267.4508 | 71.0 | 3550 | 269.3435 | 0.4 | 0.8216 | 4.3030 | 0.4000 | 0.2858 | 0.3391 | 0.6045 | | 267.4508 | 72.0 | 3600 | 269.2284 | 0.405 | 0.8198 | 4.3223 | 0.405 | 0.3006 | 0.3598 | 0.5984 | | 267.4508 | 73.0 | 3650 | 269.5614 | 0.37 | 0.8300 | 4.3579 | 0.37 | 0.2645 | 0.3515 | 0.6597 | | 267.4508 | 74.0 | 3700 | 269.2826 | 0.39 | 0.8217 | 4.3413 | 0.39 | 0.2738 | 0.3317 | 0.5869 | | 267.4508 | 75.0 | 3750 | 269.3375 | 0.39 | 0.8226 | 4.2796 | 0.39 | 0.2712 | 0.3485 | 0.6140 | | 267.4508 | 76.0 | 3800 | 269.2377 | 0.39 | 0.8198 | 4.4825 | 0.39 | 0.2771 | 0.3450 | 0.5936 | | 267.4508 | 77.0 | 3850 | 269.4238 | 0.375 | 0.8278 | 4.5048 | 0.375 | 0.2736 | 0.3349 | 0.6386 | | 267.4508 | 78.0 | 3900 | 269.4522 | 0.4 | 0.8283 | 4.5498 | 0.4000 | 0.2870 | 0.3318 | 0.6187 | | 267.4508 | 79.0 | 3950 | 269.4372 | 0.355 | 0.8316 | 4.5034 | 0.3550 | 0.2621 | 0.3182 | 0.6581 | | 267.2267 | 80.0 | 4000 | 269.4058 | 0.38 | 0.8262 | 4.4096 | 0.38 | 0.2834 | 0.3453 | 0.6197 | | 267.2267 | 81.0 | 4050 | 269.2977 | 0.37 | 0.8251 | 4.4666 | 0.37 | 0.2669 | 0.3374 | 0.6406 | | 267.2267 | 82.0 | 4100 | 269.3194 | 0.37 | 0.8286 | 4.4947 | 0.37 | 0.2751 | 0.3332 | 0.6326 | | 267.2267 | 83.0 | 4150 | 269.2936 | 0.365 | 0.8301 | 4.3184 | 0.3650 | 0.2725 | 0.3358 | 0.6815 | | 267.2267 | 84.0 | 4200 | 269.2045 | 0.4 | 0.8242 | 4.2851 | 0.4000 | 0.2947 | 0.3526 | 0.6133 | | 267.2267 | 85.0 | 4250 | 269.2916 | 0.38 | 0.8256 | 4.4965 | 0.38 | 0.2872 | 0.3190 | 0.6079 | | 267.2267 | 86.0 | 4300 | 269.3817 | 0.35 | 0.8327 | 4.3865 | 0.35 | 0.2686 | 0.3341 | 0.6660 | | 267.2267 | 87.0 | 4350 | 269.2809 | 0.36 | 0.8296 | 4.4521 | 0.36 | 0.2664 | 0.3367 | 0.6620 | | 267.2267 | 88.0 | 4400 | 269.1650 | 0.405 | 0.8181 | 4.2997 | 0.405 | 0.2857 | 0.3527 | 0.5624 | | 267.2267 | 89.0 | 4450 | 269.1071 | 0.375 | 0.8297 | 4.3559 | 0.375 | 0.2737 | 0.3386 | 0.6423 | | 267.0481 | 90.0 | 4500 | 269.3120 | 0.385 | 0.8268 | 4.4102 | 0.3850 | 0.2833 | 0.3438 | 0.6264 | | 267.0481 | 91.0 | 4550 | 269.1590 | 0.4 | 0.8243 | 4.2495 | 0.4000 | 0.2964 | 0.3382 | 0.5986 | | 267.0481 | 92.0 | 4600 | 269.1963 | 0.4 | 0.8230 | 4.4469 | 0.4000 | 0.3070 | 0.3521 | 0.5969 | | 267.0481 | 93.0 | 4650 | 269.1320 | 0.39 | 0.8256 | 4.2528 | 0.39 | 0.2856 | 0.3355 | 0.6072 | | 267.0481 | 94.0 | 4700 | 269.2391 | 0.4 | 0.8222 | 4.3351 | 0.4000 | 0.2891 | 0.3392 | 0.6019 | | 267.0481 | 95.0 | 4750 | 269.2017 | 0.395 | 0.8243 | 4.3717 | 0.395 | 0.2931 | 0.3435 | 0.6271 | | 267.0481 | 96.0 | 4800 | 269.4085 | 0.37 | 0.8290 | 4.3941 | 0.37 | 0.2817 | 0.3255 | 0.6242 | | 267.0481 | 97.0 | 4850 | 269.2195 | 0.4 | 0.8231 | 4.4454 | 0.4000 | 0.2889 | 0.3452 | 0.5916 | | 267.0481 | 98.0 | 4900 | 269.3515 | 0.375 | 0.8296 | 4.3291 | 0.375 | 0.2754 | 0.3370 | 0.6380 | | 267.0481 | 99.0 | 4950 | 269.3496 | 0.395 | 0.8244 | 4.4743 | 0.395 | 0.2989 | 0.3432 | 0.6100 | | 266.9625 | 100.0 | 5000 | 269.3729 | 0.36 | 0.8320 | 4.3971 | 0.36 | 0.2725 | 0.3539 | 0.6748 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/300-tiny_tobacco3482_kd_CEKD_t2.5_a0.5

# 300-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4770 - Accuracy: 0.82 - Brier Loss: 0.2875 - Nll: 1.3922 - F1 Micro: 0.82 - F1 Macro: 0.8020 - Ece: 0.2219 - Aurc: 0.0517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 13 | 1.7938 | 0.235 | 0.8938 | 7.8599 | 0.235 | 0.1394 | 0.3127 | 0.7433 | | No log | 2.0 | 26 | 1.2738 | 0.455 | 0.6913 | 3.6965 | 0.455 | 0.3679 | 0.2904 | 0.3352 | | No log | 3.0 | 39 | 1.0682 | 0.555 | 0.5748 | 2.0566 | 0.555 | 0.4968 | 0.2475 | 0.2296 | | No log | 4.0 | 52 | 0.8509 | 0.655 | 0.4621 | 1.7782 | 0.655 | 0.6085 | 0.2242 | 0.1405 | | No log | 5.0 | 65 | 0.7670 | 0.71 | 0.4142 | 1.4993 | 0.7100 | 0.6560 | 0.2271 | 0.1082 | | No log | 6.0 | 78 | 0.7285 | 0.735 | 0.3857 | 1.5730 | 0.735 | 0.6874 | 0.2098 | 0.0996 | | No log | 7.0 | 91 | 0.7052 | 0.72 | 0.3804 | 1.4916 | 0.72 | 0.6974 | 0.2249 | 0.0959 | | No log | 8.0 | 104 | 0.7590 | 0.71 | 0.3925 | 1.8047 | 0.7100 | 0.6641 | 0.1956 | 0.1008 | | No log | 9.0 | 117 | 0.7657 | 0.71 | 0.4006 | 1.8296 | 0.7100 | 0.7169 | 0.2330 | 0.1025 | | No log | 10.0 | 130 | 0.6512 | 0.755 | 0.3514 | 1.5899 | 0.755 | 0.7256 | 0.1863 | 0.0853 | | No log | 11.0 | 143 | 0.6615 | 0.775 | 0.3638 | 1.8180 | 0.775 | 0.7564 | 0.2106 | 0.0911 | | No log | 12.0 | 156 | 0.6195 | 0.785 | 0.3398 | 1.6998 | 0.785 | 0.7419 | 0.2337 | 0.0643 | | No log | 13.0 | 169 | 0.6065 | 0.78 | 0.3471 | 1.5917 | 0.78 | 0.7550 | 0.2280 | 0.0793 | | No log | 14.0 | 182 | 0.6314 | 0.75 | 0.3486 | 1.9235 | 0.75 | 0.7315 | 0.2105 | 0.0755 | | No log | 15.0 | 195 | 0.6426 | 0.745 | 0.3686 | 1.8633 | 0.745 | 0.7100 | 0.2099 | 0.0891 | | No log | 16.0 | 208 | 0.5849 | 0.765 | 0.3476 | 1.3466 | 0.765 | 0.7505 | 0.1978 | 0.0827 | | No log | 17.0 | 221 | 0.5604 | 0.79 | 0.3311 | 1.3948 | 0.79 | 0.7581 | 0.2258 | 0.0705 | | No log | 18.0 | 234 | 0.5504 | 0.78 | 0.3230 | 1.4757 | 0.78 | 0.7712 | 0.2104 | 0.0624 | | No log | 19.0 | 247 | 0.5586 | 0.785 | 0.3247 | 1.5297 | 0.785 | 0.7642 | 0.2159 | 0.0655 | | No log | 20.0 | 260 | 0.5879 | 0.78 | 0.3366 | 1.5348 | 0.78 | 0.7727 | 0.2162 | 0.0716 | | No log | 21.0 | 273 | 0.5558 | 0.805 | 0.3113 | 1.5720 | 0.805 | 0.7945 | 0.2161 | 0.0652 | | No log | 22.0 | 286 | 0.5439 | 0.795 | 0.3258 | 1.7373 | 0.795 | 0.7883 | 0.2307 | 0.0745 | | No log | 23.0 | 299 | 0.5155 | 0.795 | 0.3094 | 1.4183 | 0.795 | 0.7725 | 0.2221 | 0.0625 | | No log | 24.0 | 312 | 0.5039 | 0.81 | 0.2994 | 1.4458 | 0.81 | 0.7830 | 0.2114 | 0.0624 | | No log | 25.0 | 325 | 0.5142 | 0.81 | 0.3101 | 1.2798 | 0.81 | 0.7928 | 0.2205 | 0.0624 | | No log | 26.0 | 338 | 0.5007 | 0.8 | 0.3100 | 1.2390 | 0.8000 | 0.7730 | 0.2038 | 0.0645 | | No log | 27.0 | 351 | 0.4779 | 0.815 | 0.2865 | 1.3312 | 0.815 | 0.7863 | 0.2061 | 0.0518 | | No log | 28.0 | 364 | 0.4893 | 0.825 | 0.2927 | 1.3993 | 0.825 | 0.8009 | 0.2219 | 0.0555 | | No log | 29.0 | 377 | 0.4938 | 0.82 | 0.2996 | 1.4038 | 0.82 | 0.7888 | 0.2138 | 0.0586 | | No log | 30.0 | 390 | 0.4668 | 0.82 | 0.2795 | 1.3366 | 0.82 | 0.7944 | 0.2217 | 0.0495 | | No log | 31.0 | 403 | 0.4662 | 0.8 | 0.2805 | 1.1721 | 0.8000 | 0.7761 | 0.2009 | 0.0494 | | No log | 32.0 | 416 | 0.4787 | 0.82 | 0.2887 | 1.3872 | 0.82 | 0.8043 | 0.2161 | 0.0542 | | No log | 33.0 | 429 | 0.4842 | 0.81 | 0.2909 | 1.4774 | 0.81 | 0.7854 | 0.2246 | 0.0562 | | No log | 34.0 | 442 | 0.4899 | 0.81 | 0.2979 | 1.4419 | 0.81 | 0.7843 | 0.2155 | 0.0607 | | No log | 35.0 | 455 | 0.4832 | 0.815 | 0.2920 | 1.3892 | 0.815 | 0.7907 | 0.2296 | 0.0552 | | No log | 36.0 | 468 | 0.4739 | 0.815 | 0.2869 | 1.2603 | 0.815 | 0.7932 | 0.2385 | 0.0532 | | No log | 37.0 | 481 | 0.4747 | 0.81 | 0.2877 | 1.4390 | 0.81 | 0.7848 | 0.2163 | 0.0526 | | No log | 38.0 | 494 | 0.4710 | 0.815 | 0.2842 | 1.3024 | 0.815 | 0.7885 | 0.2153 | 0.0516 | | 0.2992 | 39.0 | 507 | 0.4712 | 0.81 | 0.2839 | 1.3676 | 0.81 | 0.7860 | 0.2282 | 0.0518 | | 0.2992 | 40.0 | 520 | 0.4772 | 0.815 | 0.2883 | 1.3845 | 0.815 | 0.7953 | 0.2216 | 0.0527 | | 0.2992 | 41.0 | 533 | 0.4751 | 0.82 | 0.2877 | 1.3207 | 0.82 | 0.8018 | 0.2177 | 0.0521 | | 0.2992 | 42.0 | 546 | 0.4724 | 0.82 | 0.2860 | 1.3075 | 0.82 | 0.8018 | 0.2183 | 0.0508 | | 0.2992 | 43.0 | 559 | 0.4745 | 0.82 | 0.2869 | 1.3079 | 0.82 | 0.8020 | 0.2184 | 0.0522 | | 0.2992 | 44.0 | 572 | 0.4779 | 0.815 | 0.2884 | 1.4039 | 0.815 | 0.7922 | 0.2142 | 0.0531 | | 0.2992 | 45.0 | 585 | 0.4738 | 0.82 | 0.2859 | 1.3153 | 0.82 | 0.8018 | 0.2079 | 0.0516 | | 0.2992 | 46.0 | 598 | 0.4755 | 0.815 | 0.2874 | 1.3273 | 0.815 | 0.7922 | 0.2279 | 0.0526 | | 0.2992 | 47.0 | 611 | 0.4736 | 0.82 | 0.2858 | 1.3190 | 0.82 | 0.8018 | 0.2182 | 0.0515 | | 0.2992 | 48.0 | 624 | 0.4753 | 0.82 | 0.2876 | 1.3170 | 0.82 | 0.8018 | 0.2274 | 0.0521 | | 0.2992 | 49.0 | 637 | 0.4755 | 0.82 | 0.2866 | 1.4452 | 0.82 | 0.8018 | 0.2245 | 0.0516 | | 0.2992 | 50.0 | 650 | 0.4754 | 0.815 | 0.2869 | 1.2915 | 0.815 | 0.7924 | 0.2336 | 0.0523 | | 0.2992 | 51.0 | 663 | 0.4747 | 0.82 | 0.2861 | 1.3336 | 0.82 | 0.8020 | 0.2309 | 0.0517 | | 0.2992 | 52.0 | 676 | 0.4765 | 0.815 | 0.2880 | 1.3456 | 0.815 | 0.7924 | 0.2137 | 0.0524 | | 0.2992 | 53.0 | 689 | 0.4756 | 0.82 | 0.2866 | 1.3288 | 0.82 | 0.8020 | 0.2236 | 0.0518 | | 0.2992 | 54.0 | 702 | 0.4757 | 0.82 | 0.2873 | 1.3860 | 0.82 | 0.8018 | 0.2085 | 0.0516 | | 0.2992 | 55.0 | 715 | 0.4753 | 0.815 | 0.2866 | 1.3284 | 0.815 | 0.7922 | 0.2100 | 0.0515 | | 0.2992 | 56.0 | 728 | 0.4759 | 0.82 | 0.2870 | 1.3199 | 0.82 | 0.8020 | 0.2240 | 0.0518 | | 0.2992 | 57.0 | 741 | 0.4764 | 0.82 | 0.2874 | 1.3901 | 0.82 | 0.8020 | 0.2241 | 0.0517 | | 0.2992 | 58.0 | 754 | 0.4754 | 0.815 | 0.2870 | 1.3246 | 0.815 | 0.7924 | 0.2260 | 0.0520 | | 0.2992 | 59.0 | 767 | 0.4759 | 0.815 | 0.2870 | 1.3862 | 0.815 | 0.7924 | 0.2176 | 0.0520 | | 0.2992 | 60.0 | 780 | 0.4765 | 0.815 | 0.2874 | 1.3873 | 0.815 | 0.7924 | 0.2266 | 0.0523 | | 0.2992 | 61.0 | 793 | 0.4763 | 0.82 | 0.2873 | 1.3851 | 0.82 | 0.8020 | 0.2161 | 0.0517 | | 0.2992 | 62.0 | 806 | 0.4768 | 0.815 | 0.2878 | 1.3903 | 0.815 | 0.7924 | 0.2128 | 0.0522 | | 0.2992 | 63.0 | 819 | 0.4767 | 0.82 | 0.2876 | 1.3866 | 0.82 | 0.8020 | 0.2120 | 0.0521 | | 0.2992 | 64.0 | 832 | 0.4762 | 0.82 | 0.2872 | 1.3910 | 0.82 | 0.8020 | 0.2157 | 0.0516 | | 0.2992 | 65.0 | 845 | 0.4765 | 0.82 | 0.2874 | 1.3892 | 0.82 | 0.8020 | 0.2178 | 0.0519 | | 0.2992 | 66.0 | 858 | 0.4767 | 0.82 | 0.2875 | 1.3462 | 0.82 | 0.8020 | 0.2180 | 0.0519 | | 0.2992 | 67.0 | 871 | 0.4764 | 0.82 | 0.2872 | 1.3894 | 0.82 | 0.8020 | 0.2252 | 0.0518 | | 0.2992 | 68.0 | 884 | 0.4767 | 0.82 | 0.2874 | 1.3860 | 0.82 | 0.8020 | 0.2118 | 0.0518 | | 0.2992 | 69.0 | 897 | 0.4766 | 0.82 | 0.2874 | 1.3894 | 0.82 | 0.8020 | 0.2180 | 0.0519 | | 0.2992 | 70.0 | 910 | 0.4765 | 0.82 | 0.2872 | 1.3882 | 0.82 | 0.8020 | 0.2280 | 0.0517 | | 0.2992 | 71.0 | 923 | 0.4766 | 0.82 | 0.2874 | 1.3875 | 0.82 | 0.8020 | 0.2177 | 0.0519 | | 0.2992 | 72.0 | 936 | 0.4765 | 0.82 | 0.2874 | 1.3880 | 0.82 | 0.8020 | 0.2148 | 0.0517 | | 0.2992 | 73.0 | 949 | 0.4766 | 0.82 | 0.2873 | 1.3915 | 0.82 | 0.8020 | 0.2109 | 0.0516 | | 0.2992 | 74.0 | 962 | 0.4765 | 0.82 | 0.2872 | 1.3900 | 0.82 | 0.8020 | 0.2110 | 0.0517 | | 0.2992 | 75.0 | 975 | 0.4769 | 0.82 | 0.2875 | 1.3913 | 0.82 | 0.8020 | 0.2251 | 0.0520 | | 0.2992 | 76.0 | 988 | 0.4770 | 0.82 | 0.2876 | 1.3909 | 0.82 | 0.8020 | 0.2196 | 0.0520 | | 0.0695 | 77.0 | 1001 | 0.4768 | 0.82 | 0.2875 | 1.3890 | 0.82 | 0.8020 | 0.2212 | 0.0517 | | 0.0695 | 78.0 | 1014 | 0.4767 | 0.82 | 0.2873 | 1.3935 | 0.82 | 0.8020 | 0.2281 | 0.0518 | | 0.0695 | 79.0 | 1027 | 0.4767 | 0.82 | 0.2874 | 1.3897 | 0.82 | 0.8020 | 0.2282 | 0.0517 | | 0.0695 | 80.0 | 1040 | 0.4770 | 0.82 | 0.2876 | 1.3889 | 0.82 | 0.8020 | 0.2174 | 0.0518 | | 0.0695 | 81.0 | 1053 | 0.4770 | 0.82 | 0.2875 | 1.3935 | 0.82 | 0.8020 | 0.2221 | 0.0518 | | 0.0695 | 82.0 | 1066 | 0.4766 | 0.82 | 0.2873 | 1.3901 | 0.82 | 0.8020 | 0.2283 | 0.0517 | | 0.0695 | 83.0 | 1079 | 0.4768 | 0.82 | 0.2874 | 1.3902 | 0.82 | 0.8020 | 0.2283 | 0.0517 | | 0.0695 | 84.0 | 1092 | 0.4770 | 0.82 | 0.2874 | 1.3917 | 0.82 | 0.8020 | 0.2217 | 0.0518 | | 0.0695 | 85.0 | 1105 | 0.4769 | 0.82 | 0.2875 | 1.3913 | 0.82 | 0.8020 | 0.2283 | 0.0518 | | 0.0695 | 86.0 | 1118 | 0.4769 | 0.82 | 0.2874 | 1.3916 | 0.82 | 0.8020 | 0.2282 | 0.0517 | | 0.0695 | 87.0 | 1131 | 0.4769 | 0.82 | 0.2874 | 1.3912 | 0.82 | 0.8020 | 0.2218 | 0.0517 | | 0.0695 | 88.0 | 1144 | 0.4770 | 0.82 | 0.2875 | 1.3923 | 0.82 | 0.8020 | 0.2218 | 0.0517 | | 0.0695 | 89.0 | 1157 | 0.4768 | 0.82 | 0.2874 | 1.3905 | 0.82 | 0.8020 | 0.2283 | 0.0518 | | 0.0695 | 90.0 | 1170 | 0.4769 | 0.82 | 0.2875 | 1.3924 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 91.0 | 1183 | 0.4769 | 0.82 | 0.2874 | 1.3923 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 92.0 | 1196 | 0.4768 | 0.82 | 0.2874 | 1.3908 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 93.0 | 1209 | 0.4770 | 0.82 | 0.2875 | 1.3909 | 0.82 | 0.8020 | 0.2219 | 0.0518 | | 0.0695 | 94.0 | 1222 | 0.4768 | 0.82 | 0.2873 | 1.3918 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 95.0 | 1235 | 0.4769 | 0.82 | 0.2874 | 1.3914 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 96.0 | 1248 | 0.4770 | 0.82 | 0.2875 | 1.3917 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 97.0 | 1261 | 0.4769 | 0.82 | 0.2874 | 1.3918 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 98.0 | 1274 | 0.4770 | 0.82 | 0.2875 | 1.3920 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 99.0 | 1287 | 0.4770 | 0.82 | 0.2875 | 1.3922 | 0.82 | 0.8020 | 0.2219 | 0.0517 | | 0.0695 | 100.0 | 1300 | 0.4770 | 0.82 | 0.2875 | 1.3922 | 0.82 | 0.8020 | 0.2219 | 0.0517 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]

jordyvl/300-tiny_tobacco3482_kd

# 300-tiny_tobacco3482_kd This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 - Accuracy: 0.79 - Brier Loss: 0.3334 - Nll: 1.0051 - F1 Micro: 0.79 - F1 Macro: 0.7591 - Ece: 0.2152 - Aurc: 0.0601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 13 | 1.7587 | 0.225 | 0.8896 | 7.6699 | 0.225 | 0.1499 | 0.2894 | 0.7588 | | No log | 2.0 | 26 | 1.1905 | 0.4 | 0.7901 | 3.7933 | 0.4000 | 0.2961 | 0.3234 | 0.4395 | | No log | 3.0 | 39 | 0.9530 | 0.53 | 0.6670 | 2.9907 | 0.53 | 0.4066 | 0.3137 | 0.2821 | | No log | 4.0 | 52 | 0.8046 | 0.615 | 0.5821 | 2.2124 | 0.615 | 0.4898 | 0.3097 | 0.1862 | | No log | 5.0 | 65 | 0.7084 | 0.685 | 0.5084 | 2.1522 | 0.685 | 0.6027 | 0.3191 | 0.1389 | | No log | 6.0 | 78 | 0.7243 | 0.68 | 0.4683 | 2.2673 | 0.68 | 0.5904 | 0.2695 | 0.1279 | | No log | 7.0 | 91 | 0.6734 | 0.67 | 0.4675 | 2.2909 | 0.67 | 0.5844 | 0.2436 | 0.1510 | | No log | 8.0 | 104 | 0.5780 | 0.7 | 0.4215 | 2.0061 | 0.7 | 0.6160 | 0.2418 | 0.1016 | | No log | 9.0 | 117 | 0.6270 | 0.71 | 0.4402 | 1.8620 | 0.7100 | 0.6574 | 0.2485 | 0.1249 | | No log | 10.0 | 130 | 0.5604 | 0.72 | 0.4074 | 1.5914 | 0.72 | 0.6430 | 0.2566 | 0.0935 | | No log | 11.0 | 143 | 0.5814 | 0.705 | 0.4079 | 1.6933 | 0.705 | 0.6190 | 0.2350 | 0.1035 | | No log | 12.0 | 156 | 0.5901 | 0.71 | 0.4176 | 1.7974 | 0.7100 | 0.6472 | 0.2225 | 0.1058 | | No log | 13.0 | 169 | 0.5041 | 0.71 | 0.3918 | 1.8429 | 0.7100 | 0.6562 | 0.2336 | 0.0958 | | No log | 14.0 | 182 | 0.5099 | 0.72 | 0.3982 | 1.6343 | 0.72 | 0.6550 | 0.2202 | 0.1021 | | No log | 15.0 | 195 | 0.4843 | 0.745 | 0.3951 | 1.3599 | 0.745 | 0.6719 | 0.2680 | 0.0884 | | No log | 16.0 | 208 | 0.4529 | 0.74 | 0.3776 | 1.3838 | 0.74 | 0.6951 | 0.2112 | 0.0839 | | No log | 17.0 | 221 | 0.4420 | 0.745 | 0.3782 | 1.4403 | 0.745 | 0.6982 | 0.2285 | 0.0800 | | No log | 18.0 | 234 | 0.4428 | 0.755 | 0.3710 | 1.3696 | 0.755 | 0.7298 | 0.2170 | 0.0825 | | No log | 19.0 | 247 | 0.4306 | 0.75 | 0.3794 | 1.4095 | 0.75 | 0.7235 | 0.2470 | 0.0862 | | No log | 20.0 | 260 | 0.4166 | 0.74 | 0.3648 | 1.2893 | 0.74 | 0.6776 | 0.2312 | 0.0835 | | No log | 21.0 | 273 | 0.3830 | 0.77 | 0.3524 | 1.1764 | 0.7700 | 0.7256 | 0.2535 | 0.0730 | | No log | 22.0 | 286 | 0.3918 | 0.77 | 0.3564 | 1.2293 | 0.7700 | 0.7067 | 0.2372 | 0.0710 | | No log | 23.0 | 299 | 0.4125 | 0.75 | 0.3656 | 1.1419 | 0.75 | 0.7109 | 0.2357 | 0.0766 | | No log | 24.0 | 312 | 0.3771 | 0.785 | 0.3543 | 1.0960 | 0.785 | 0.7583 | 0.2345 | 0.0712 | | No log | 25.0 | 325 | 0.3846 | 0.745 | 0.3613 | 1.0616 | 0.745 | 0.7061 | 0.2060 | 0.0766 | | No log | 26.0 | 338 | 0.3660 | 0.77 | 0.3547 | 1.3094 | 0.7700 | 0.7196 | 0.2515 | 0.0724 | | No log | 27.0 | 351 | 0.3634 | 0.78 | 0.3476 | 1.0645 | 0.78 | 0.7479 | 0.2401 | 0.0677 | | No log | 28.0 | 364 | 0.3715 | 0.755 | 0.3522 | 1.1981 | 0.755 | 0.6984 | 0.2257 | 0.0709 | | No log | 29.0 | 377 | 0.3701 | 0.765 | 0.3597 | 1.1645 | 0.765 | 0.7239 | 0.2631 | 0.0747 | | No log | 30.0 | 390 | 0.3562 | 0.775 | 0.3465 | 1.1094 | 0.775 | 0.7140 | 0.2428 | 0.0659 | | No log | 31.0 | 403 | 0.3811 | 0.775 | 0.3499 | 1.2515 | 0.775 | 0.7368 | 0.2214 | 0.0694 | | No log | 32.0 | 416 | 0.3555 | 0.77 | 0.3439 | 1.1715 | 0.7700 | 0.7053 | 0.2532 | 0.0705 | | No log | 33.0 | 429 | 0.3592 | 0.775 | 0.3449 | 1.1606 | 0.775 | 0.7364 | 0.2336 | 0.0729 | | No log | 34.0 | 442 | 0.3555 | 0.78 | 0.3431 | 1.1054 | 0.78 | 0.7373 | 0.2143 | 0.0653 | | No log | 35.0 | 455 | 0.3454 | 0.77 | 0.3415 | 1.0386 | 0.7700 | 0.7333 | 0.2463 | 0.0668 | | No log | 36.0 | 468 | 0.3403 | 0.8 | 0.3394 | 1.1435 | 0.8000 | 0.7664 | 0.2674 | 0.0625 | | No log | 37.0 | 481 | 0.3390 | 0.785 | 0.3379 | 1.1183 | 0.785 | 0.7552 | 0.2432 | 0.0633 | | No log | 38.0 | 494 | 0.3413 | 0.79 | 0.3347 | 1.1538 | 0.79 | 0.7406 | 0.2239 | 0.0615 | | 0.2994 | 39.0 | 507 | 0.3364 | 0.795 | 0.3362 | 0.9975 | 0.795 | 0.7650 | 0.2334 | 0.0639 | | 0.2994 | 40.0 | 520 | 0.3340 | 0.79 | 0.3328 | 1.0045 | 0.79 | 0.7466 | 0.2711 | 0.0580 | | 0.2994 | 41.0 | 533 | 0.3381 | 0.77 | 0.3391 | 0.9829 | 0.7700 | 0.7427 | 0.2147 | 0.0675 | | 0.2994 | 42.0 | 546 | 0.3297 | 0.8 | 0.3319 | 1.0739 | 0.8000 | 0.7685 | 0.2613 | 0.0585 | | 0.2994 | 43.0 | 559 | 0.3338 | 0.8 | 0.3373 | 1.1507 | 0.8000 | 0.7719 | 0.2491 | 0.0637 | | 0.2994 | 44.0 | 572 | 0.3316 | 0.79 | 0.3359 | 1.1274 | 0.79 | 0.7539 | 0.2469 | 0.0620 | | 0.2994 | 45.0 | 585 | 0.3283 | 0.79 | 0.3336 | 1.0644 | 0.79 | 0.7531 | 0.2636 | 0.0612 | | 0.2994 | 46.0 | 598 | 0.3297 | 0.8 | 0.3344 | 1.1343 | 0.8000 | 0.7670 | 0.2317 | 0.0600 | | 0.2994 | 47.0 | 611 | 0.3293 | 0.79 | 0.3318 | 1.0692 | 0.79 | 0.7542 | 0.2396 | 0.0616 | | 0.2994 | 48.0 | 624 | 0.3339 | 0.79 | 0.3357 | 1.1225 | 0.79 | 0.7590 | 0.2508 | 0.0617 | | 0.2994 | 49.0 | 637 | 0.3290 | 0.795 | 0.3343 | 1.0692 | 0.795 | 0.7618 | 0.2529 | 0.0604 | | 0.2994 | 50.0 | 650 | 0.3298 | 0.79 | 0.3348 | 1.1343 | 0.79 | 0.7591 | 0.2330 | 0.0609 | | 0.2994 | 51.0 | 663 | 0.3305 | 0.795 | 0.3330 | 1.0045 | 0.795 | 0.7618 | 0.2357 | 0.0607 | | 0.2994 | 52.0 | 676 | 0.3299 | 0.79 | 0.3339 | 1.0722 | 0.79 | 0.7542 | 0.2562 | 0.0614 | | 0.2994 | 53.0 | 689 | 0.3280 | 0.8 | 0.3325 | 1.0688 | 0.8000 | 0.7685 | 0.2500 | 0.0593 | | 0.2994 | 54.0 | 702 | 0.3284 | 0.795 | 0.3323 | 1.0175 | 0.795 | 0.7618 | 0.2436 | 0.0598 | | 0.2994 | 55.0 | 715 | 0.3287 | 0.79 | 0.3331 | 1.0750 | 0.79 | 0.7591 | 0.2497 | 0.0604 | | 0.2994 | 56.0 | 728 | 0.3286 | 0.795 | 0.3335 | 1.0115 | 0.795 | 0.7618 | 0.2296 | 0.0602 | | 0.2994 | 57.0 | 741 | 0.3285 | 0.79 | 0.3330 | 1.0648 | 0.79 | 0.7591 | 0.2446 | 0.0602 | | 0.2994 | 58.0 | 754 | 0.3299 | 0.795 | 0.3339 | 1.0193 | 0.795 | 0.7618 | 0.2345 | 0.0608 | | 0.2994 | 59.0 | 767 | 0.3294 | 0.79 | 0.3329 | 1.0139 | 0.79 | 0.7591 | 0.2369 | 0.0601 | | 0.2994 | 60.0 | 780 | 0.3292 | 0.795 | 0.3332 | 1.0118 | 0.795 | 0.7618 | 0.2226 | 0.0601 | | 0.2994 | 61.0 | 793 | 0.3293 | 0.795 | 0.3333 | 1.0716 | 0.795 | 0.7618 | 0.2282 | 0.0602 | | 0.2994 | 62.0 | 806 | 0.3294 | 0.795 | 0.3331 | 1.0107 | 0.795 | 0.7618 | 0.2224 | 0.0601 | | 0.2994 | 63.0 | 819 | 0.3295 | 0.795 | 0.3336 | 1.0144 | 0.795 | 0.7618 | 0.2294 | 0.0605 | | 0.2994 | 64.0 | 832 | 0.3293 | 0.795 | 0.3332 | 1.0104 | 0.795 | 0.7618 | 0.2324 | 0.0603 | | 0.2994 | 65.0 | 845 | 0.3298 | 0.795 | 0.3337 | 1.0114 | 0.795 | 0.7618 | 0.2478 | 0.0606 | | 0.2994 | 66.0 | 858 | 0.3297 | 0.795 | 0.3333 | 1.0076 | 0.795 | 0.7618 | 0.2366 | 0.0601 | | 0.2994 | 67.0 | 871 | 0.3298 | 0.79 | 0.3338 | 1.0120 | 0.79 | 0.7591 | 0.2513 | 0.0606 | | 0.2994 | 68.0 | 884 | 0.3297 | 0.795 | 0.3337 | 1.0110 | 0.795 | 0.7618 | 0.2376 | 0.0605 | | 0.2994 | 69.0 | 897 | 0.3297 | 0.795 | 0.3335 | 1.0115 | 0.795 | 0.7618 | 0.2228 | 0.0602 | | 0.2994 | 70.0 | 910 | 0.3292 | 0.795 | 0.3333 | 1.0089 | 0.795 | 0.7618 | 0.2215 | 0.0602 | | 0.2994 | 71.0 | 923 | 0.3297 | 0.795 | 0.3334 | 1.0083 | 0.795 | 0.7618 | 0.2226 | 0.0600 | | 0.2994 | 72.0 | 936 | 0.3297 | 0.79 | 0.3335 | 1.0072 | 0.79 | 0.7591 | 0.2257 | 0.0604 | | 0.2994 | 73.0 | 949 | 0.3297 | 0.795 | 0.3332 | 1.0060 | 0.795 | 0.7618 | 0.2381 | 0.0600 | | 0.2994 | 74.0 | 962 | 0.3295 | 0.795 | 0.3335 | 1.0082 | 0.795 | 0.7618 | 0.2366 | 0.0603 | | 0.2994 | 75.0 | 975 | 0.3296 | 0.79 | 0.3334 | 1.0089 | 0.79 | 0.7591 | 0.2373 | 0.0601 | | 0.2994 | 76.0 | 988 | 0.3298 | 0.795 | 0.3334 | 1.0098 | 0.795 | 0.7618 | 0.2310 | 0.0602 | | 0.0006 | 77.0 | 1001 | 0.3297 | 0.79 | 0.3334 | 1.0084 | 0.79 | 0.7591 | 0.2228 | 0.0603 | | 0.0006 | 78.0 | 1014 | 0.3297 | 0.79 | 0.3333 | 1.0071 | 0.79 | 0.7591 | 0.2148 | 0.0600 | | 0.0006 | 79.0 | 1027 | 0.3298 | 0.795 | 0.3334 | 1.0059 | 0.795 | 0.7618 | 0.2309 | 0.0602 | | 0.0006 | 80.0 | 1040 | 0.3298 | 0.795 | 0.3334 | 1.0046 | 0.795 | 0.7618 | 0.2309 | 0.0602 | | 0.0006 | 81.0 | 1053 | 0.3298 | 0.79 | 0.3335 | 1.0073 | 0.79 | 0.7591 | 0.2239 | 0.0602 | | 0.0006 | 82.0 | 1066 | 0.3298 | 0.795 | 0.3336 | 1.0072 | 0.795 | 0.7618 | 0.2317 | 0.0603 | | 0.0006 | 83.0 | 1079 | 0.3297 | 0.795 | 0.3334 | 1.0055 | 0.795 | 0.7618 | 0.2224 | 0.0601 | | 0.0006 | 84.0 | 1092 | 0.3298 | 0.79 | 0.3335 | 1.0061 | 0.79 | 0.7591 | 0.2240 | 0.0601 | | 0.0006 | 85.0 | 1105 | 0.3297 | 0.79 | 0.3334 | 1.0052 | 0.79 | 0.7591 | 0.2322 | 0.0601 | | 0.0006 | 86.0 | 1118 | 0.3298 | 0.79 | 0.3335 | 1.0059 | 0.79 | 0.7591 | 0.2323 | 0.0602 | | 0.0006 | 87.0 | 1131 | 0.3298 | 0.79 | 0.3335 | 1.0065 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 88.0 | 1144 | 0.3298 | 0.79 | 0.3335 | 1.0056 | 0.79 | 0.7591 | 0.2235 | 0.0603 | | 0.0006 | 89.0 | 1157 | 0.3297 | 0.79 | 0.3334 | 1.0050 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 90.0 | 1170 | 0.3297 | 0.79 | 0.3334 | 1.0049 | 0.79 | 0.7591 | 0.2153 | 0.0602 | | 0.0006 | 91.0 | 1183 | 0.3297 | 0.79 | 0.3334 | 1.0059 | 0.79 | 0.7591 | 0.2234 | 0.0601 | | 0.0006 | 92.0 | 1196 | 0.3298 | 0.79 | 0.3334 | 1.0049 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 93.0 | 1209 | 0.3299 | 0.79 | 0.3335 | 1.0056 | 0.79 | 0.7591 | 0.2152 | 0.0601 | | 0.0006 | 94.0 | 1222 | 0.3298 | 0.79 | 0.3335 | 1.0049 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 95.0 | 1235 | 0.3298 | 0.79 | 0.3334 | 1.0048 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 96.0 | 1248 | 0.3298 | 0.79 | 0.3334 | 1.0050 | 0.79 | 0.7591 | 0.2152 | 0.0601 | | 0.0006 | 97.0 | 1261 | 0.3298 | 0.79 | 0.3335 | 1.0053 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 98.0 | 1274 | 0.3298 | 0.79 | 0.3334 | 1.0051 | 0.79 | 0.7591 | 0.2152 | 0.0602 | | 0.0006 | 99.0 | 1287 | 0.3298 | 0.79 | 0.3334 | 1.0052 | 0.79 | 0.7591 | 0.2152 | 0.0601 | | 0.0006 | 100.0 | 1300 | 0.3298 | 0.79 | 0.3334 | 1.0051 | 0.79 | 0.7591 | 0.2152 | 0.0601 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "adve", "email", "form", "letter", "memo", "news", "note", "report", "resume", "scientific" ]